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Snow Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 December 2017) | Viewed by 107960

Special Issue Editor

Dr. Claudia Notarnicola

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Guest Editor
EURAC Research – Institute for Earth Observation, Viale Druso 1, 39100 Bolzano, Italy
Interests: retrieval of bio-physical parameters from optical and radar data; multi-sensor data fusion; integrated approach for environmental monitoring in mountain areas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention.

In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. In particular, the SAR images have demonstrated to be effective and robust measures to identify wet snow, whereas optical data have proven to be an effective source of information to identify the snow cover extension when cloud cover is not present.

Moreover, remote sensing from space and aircraft, combined with complementary terrestrial observations and with physical models, have been used to monitor snow evolution and changes in relation to different climate conditions. Of course, an important aspect of space-based (and airborne) remote sensing is that we can investigate areas in which ground observations are not possible due to physical or political constraints. Actually, this scenario has changed with the introduction of the Sentinel family.

This Special Issue invites innovative remote sensing methods and applications on monitoring and modeling snow. Submissions are encouraged to cover a broad range of topics, which may include, but are not limited to, the following activities:

  • Remote sensing algorithm development, automation, implementation, and validation
  • Synergy of optical and SAR images to monitor snow status
  • Integration of remote sensing and hydrological modeling
  • Investigation on spatial and temporal variability of snow cover
  • Detection and monitoring of snow parameters (snow depth, snow density, snow water equivalent, etc.)
  • Analysis of time series of satellite snow cover extent

Dr. Claudia Notarnicola
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

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Published Papers (16 papers)

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16 pages, 3841 KiB  
Article
Snow Wetness Retrieved from L-Band Radiometry
by Reza Naderpour and Mike Schwank
Remote Sens. 2018, 10(3), 359; https://doi.org/10.3390/rs10030359 - 26 Feb 2018
Cited by 24 | Viewed by 4830
Abstract
The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission [...] Read more.
The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS), coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. Two types of snow liquid water retrievals were performed based on L-band brightness temperatures measured over (i) areas with a metal reflector placed on the ground (“reflector area”— T B , R ), and (ii) natural snow-covered ground (“natural area”— T B , N ). The reliable representation of temporal variations of snow liquid water is demonstrated for both types of the aforementioned quasi-simultaneous retrievals. This is verified by the fact that both types of snow liquid water retrievals indicate a dry snowpack throughout the “cold winter period” with frozen ground and air temperatures well below freezing, and synchronously respond to snowpack moisture variations during the “early spring period”. The robust and reliable performance of snow liquid water retrieved from T B , R , together with their level of detail, suggest the use of these retrievals as “references” to assess the meaningfulness of the snow liquid water retrievals based on T B , N . It is noteworthy that the latter retrievals are achieved in a two-step retrieval procedure using exclusively L-band brightness temperatures, without the need for in-situ measurements, such as ground permittivity ε G and snow mass-density ρ S . The latter two are estimated in the first retrieval-step employing the well-established two-parameter ( ρ S , ε G ) retrieval scheme designed for dry snow conditions and explored in the companion paper that is included in this special issue in terms of its sensitivity with respect to disturbative melting effects. The two-step retrieval approach proposed and investigated here, opens up the possibility of using airborne or spaceborne L-band radiometry to estimate ( ρ S , ε G ) and additionally snow liquid water as a new passive L-band data product. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematics of the Davos-Laret Remote Sensing Field Laboratory [<a href="#B22-remotesensing-10-00359" class="html-bibr">22</a>] during the winter 2016/17 campaign.</p> Full article ">Figure 2
<p>(<b>a</b>) Measured snow height <math display="inline"> <semantics> <mrow> <msub> <mi>h</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math>, and (<b>b</b>) density <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math> of the bottom ~10 cm of the snowpack. Snow melted down in the second half of March, and disappeared within ~10 days (see [<a href="#B22-remotesensing-10-00359" class="html-bibr">22</a>]).</p> Full article ">Figure 3
<p>Flowchart representing the approaches used to achieve “reference” retrievals <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> of snow liquid water content from <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> measured over the “reflector area” (<b>a</b>), and <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> of snow liquid water content retrieved from measurements <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> measured over the “natural area” (<b>b</b>). Retrieval approaches are under-laid in light gray; specific parameter values are under-laid in white.</p> Full article ">Figure 4
<p>Retrievals <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> of snow liquid water content derived from <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for (<b>a</b>) <span class="html-italic">RM</span> = “HV”, (<b>b</b>) <span class="html-italic">RM</span> = “H”, and (<b>c</b>) <span class="html-italic">RM</span> = “V”, respectively. Light gray overlays indicate the zoomed-in view of <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> shown in <a href="#remotesensing-10-00359-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 5
<p>Zoomed-in views of <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> shown in <a href="#remotesensing-10-00359-f004" class="html-fig">Figure 4</a>a for (<b>a</b>) 12–20 February during the “early-spring period”, and (<b>b</b>) 8–30 January during the “cold winter period”. Air temperatures <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>air</mi> </mrow> </msub> </mrow> </semantics> </math> are indicated by magenta lines; precipitation rates <span class="html-italic">r</span> are shown in the bottom panels.</p> Full article ">Figure 6
<p>Same snow liquid water content retrievals <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> during the “early-spring period” as in <a href="#remotesensing-10-00359-f005" class="html-fig">Figure 5</a>a (blue, left axes). Corresponding snow liquid water column <math display="inline"> <semantics> <mrow> <mi>W</mi> <msubsup> <mi>C</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>⋅</mo> <msub> <mi>h</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math> (green, right axes) considering snow height <math display="inline"> <semantics> <mrow> <msub> <mi>h</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math>, as shown in <a href="#remotesensing-10-00359-f002" class="html-fig">Figure 2</a>a.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>c</b>) show snow liquid water <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> retrievals using <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for <span class="html-italic">RM</span> = “HV”, “H”, and “V”, respectively. The retrievals are shown for the snow-covered period from 8 January to 15 March The horizontal dashed red lines indicate the <math display="inline"> <semantics> <mrow> <msub> <mi>W</mi> <mi mathvariant="normal">S</mi> </msub> <mo>=</mo> <mn>0.01</mn> <mtext> </mtext> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics> </math> threshold as the rough moist-snow approximation.</p> Full article ">Figure 8
<p>The orange line indicates <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> retrievals using <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> from 12–20 February. The blue and magenta lines show the <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> retrievals (<a href="#remotesensing-10-00359-f005" class="html-fig">Figure 5</a>a) and the air temperature, respectively. It is evident that <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> retrievals and <math display="inline"> <semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi mathvariant="normal">S</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> “reference” retrievals are harmonized and undergo a similar diurnal variation pattern.</p> Full article ">
26 pages, 5527 KiB  
Article
Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects
by Mike Schwank and Reza Naderpour
Remote Sens. 2018, 10(2), 354; https://doi.org/10.3390/rs10020354 - 24 Feb 2018
Cited by 24 | Viewed by 5093
Abstract
Ground permittivity and snow density retrievals for the “snow-free period”, “cold winter period”, and “early spring period” are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and [...] Read more.
Ground permittivity and snow density retrievals for the “snow-free period”, “cold winter period”, and “early spring period” are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and multi-angle two-parameter retrieval algorithms employed during each of the aforementioned three periods is assessed using in-situ measured ground permittivity and snow density. Additionally, a synthetic sensitivity analysis is conducted that studies melting effects on the retrievals in the form of two types of “geophysical noise” (snow liquid water and footprint-dependent ground permittivity). Experimental and synthetic analyses show that both types of investigated “geophysical noise” noticeably disturb the retrievals and result in an increased correlation between them. The strength of this correlation is successfully used as a quality-indicator flag for the purpose of filtering out highly correlated ground permittivity and snow density retrievals. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Experimental and synthetic retrievals are performed in retrieval modes RM = “H”, “V”, and “HV”, where brightness temperatures from polarizations p = H, p = V, or both p = H and V are used, respectively, in the retrieval procedure. Our analysis shows that retrievals for RM = “V” are predominantly least prone to the investigated “geophysical noise”. The presented experimental results indicate that retrievals match in-situ observations best for the “snow-free period” and the “cold winter period” when “geophysical noise” is at minimum. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Diagram of the footprint areas and the location of the in-situ sensors. ETH L-band Radiometer-II (ELBARA-II) was mounted atop an 8-m tower indicated by the hollow black square.</p> Full article ">Figure 2
<p>Panels (<b>a</b>,<b>b</b>) show the time series of in-situ measured ground permittivities along transects 1 and 2 (shown in <a href="#remotesensing-10-00354-f001" class="html-fig">Figure 1</a>), respectively. In panels (<b>a</b>,<b>b</b>), red indicates ground permittivitiy <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> values resulting from averaging all 12 in-situ sensor readings. Panel (<b>c</b>) shows the average ground temperature <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> measured by the 12 SMT-100 sensors. Panel (<b>d</b>) indicates temperatures <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>air</mi> </mrow> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mn>15</mn> <mi>cm</mi> </mrow> </msub> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mn>50</mn> <mi>cm</mi> </mrow> </msub> </mrow> </semantics> </math> measured by ELBARA-II’s PT-100 temperature sensor and SMT-100 sensors placed 15 cm and 50 cm above ground, respectively. <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mn>15</mn> <mi>cm</mi> </mrow> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mn>50</mn> <mi>cm</mi> </mrow> </msub> </mrow> </semantics> </math> show either air or snow temperatures depending on the snow height at the time of measurement. Panel (<b>e</b>) shows precipitation (both rain and snow). Panel (<b>f</b>) shows mass-density of the lowest 10 cm of the snowpack, as measured in-situ with a manual density cutter.</p> Full article ">Figure 3
<p>Flowchart of the modeling approach used to infer sensitivities of retrieval pairs <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mrow> <mi mathvariant="bold-italic">R</mi> <mi mathvariant="bold-italic">M</mi> </mrow> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> to “melting effects” such as: (<b>a</b>) snow liquid-water, and (<b>b</b>) spatial heterogeneity of ground permittivity.</p> Full article ">Figure 4
<p>Scatterplots of retrieval pairs <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> (orange squares) for <span class="html-italic">RM</span> = “H” (panel (<b>a</b>)) and “V” (panel (<b>b</b>)) simulated for the two-dimensional space of “true” values (crossed black circles). For each <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mo>*</mo> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mo>*</mo> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> snow liquid water column (the studied sensitive parameter) is varied within the range <math display="inline"> <semantics> <mrow> <mn>0</mn> <mtext> </mtext> <mi>mm</mi> <mo>≤</mo> <mi>W</mi> <msub> <mi>C</mi> <mi mathvariant="normal">S</mi> </msub> <mo>≤</mo> <mn>1</mn> <mtext> </mtext> <mi>mm</mi> </mrow> </semantics> </math> in steps of <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mi>W</mi> <msub> <mi>C</mi> <mi mathvariant="normal">S</mi> </msub> <mo>=</mo> <mn>0.1</mn> <mtext> </mtext> <mi>mm</mi> </mrow> </semantics> </math>. Panels (<b>c</b>,<b>d</b>) show Root Mean Square Errors <math display="inline"> <semantics> <mrow> <mi>RMSE</mi> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>RM</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> (solid blue dots), <math display="inline"> <semantics> <mrow> <mi>RMSE</mi> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>RM</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> (open red dots) and retrievals’ coefficients of determination <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>RM</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>RM</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> caused by <math display="inline"> <semantics> <mrow> <mi>W</mi> <msub> <mi>C</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>Scatterplots of retrieval pairs <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for <span class="html-italic">RM</span> = “H” (panel (<b>a</b>)) and “V” (panel (<b>b</b>)) for “true” values (crossed black circles) <math display="inline"> <semantics> <mrow> <mn>100</mn> <msup> <mrow> <mtext> </mtext> <mi>kg</mi> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>≤</mo> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mo>*</mo> </msubsup> <mo>≤</mo> <mn>400</mn> <msup> <mrow> <mtext> </mtext> <mi>kg</mi> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mn>5</mn> <mo>≤</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mo>*</mo> </msubsup> <mo>≤</mo> <mn>20</mn> </mrow> </semantics> </math>. For each <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mo>*</mo> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mo>*</mo> </msubsup> </mrow> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </semantics> </math> the sensitive parameter in question is varied within <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>≤</mo> <mo>Δ</mo> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> <mo>≤</mo> <mn>2</mn> </mrow> </semantics> </math> (in steps of <math display="inline"> <semantics> <mrow> <mi>δ</mi> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics> </math>). <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> expresses <math display="inline"> <semantics> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> </semantics> </math>-dependent ground permittivities <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mrow> <mi mathvariant="normal">G</mi> <mo>,</mo> <mi>θ</mi> </mrow> <mrow> <mi>t</mi> <mi>y</mi> <mi>p</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math>; “true” <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mo>*</mo> </msubsup> </mrow> </semantics> </math> are defined as <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mtext> </mtext> <mo stretchy="false">〈</mo> <msubsup> <mi>ε</mi> <mrow> <mi mathvariant="normal">G</mi> <mo>,</mo> <mi>θ</mi> </mrow> <mrow> <mi>t</mi> <mi>y</mi> <mi>p</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> <mo stretchy="false">〉</mo> </mrow> </semantics> </math> (averaging over <math display="inline"> <semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>min</mi> </mrow> </msub> <mo>=</mo> <mn>30</mn> <mo>°</mo> <mo>≤</mo> <msub> <mi>θ</mi> <mi>k</mi> </msub> <mo>≤</mo> <msub> <mi>θ</mi> <mrow> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mn>65</mn> <mo>°</mo> </mrow> </semantics> </math>). Retrieval sensitivities to increasing (<span class="html-italic">type</span> = “inc.”, green) and decreasing (<span class="html-italic">type</span> = “dec.”, orange) <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mrow> <mi mathvariant="normal">G</mi> <mo>,</mo> <mi>θ</mi> </mrow> <mrow> <mi>t</mi> <mi>y</mi> <mi>p</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> are shown. Panels (<b>c</b>) and (<b>d</b>) show <math display="inline"> <semantics> <mrow> <mi>RMSE</mi> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> (blue), <math display="inline"> <semantics> <mrow> <mi>RMSE</mi> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> (red), and retrievals’ coefficients of determination <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> (black) caused by <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 6
<p>Multi-angle two-parameter retrievals <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for the time period 15 December, 2016–15 March, 2017. Panels (<b>a</b>,<b>b</b>) show the time series of <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math>, respectively. Panels (<b>c</b>,<b>d</b>) and (<b>e</b>,<b>f</b>) show corresponding <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> for <span class="html-italic">RM</span> = “H” and “V”, respectively. Red markers show in-situ measured bottom-layer snow density <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math> and ground permittivity <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> (same data as shown in <a href="#remotesensing-10-00354-f002" class="html-fig">Figure 2</a>). The vertical dashed lines delimit the “snow-free period” (before 3 January), the “cold winter period” (3–31 January), and the “early spring period” (after 31 January).</p> Full article ">Figure 7
<p>Histograms of coefficients of determination <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mrow> <mo> </mo> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for <span class="html-italic">RM</span> = “V” (<b>a</b>,<b>b</b>) and <span class="html-italic">RM</span> = “H” (<b>c</b>,<b>d</b>) of the retrieval pairs <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mrow> <mi mathvariant="bold-italic">R</mi> <mi mathvariant="bold-italic">M</mi> </mrow> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> computed based on a sliding 12-h time window between 15 December, 2016 and 5 February, 2017. (<b>a</b>,<b>c</b>) are derived from “morning” (2:00–8:00) measurements, (<b>b</b>,<b>d</b>) are derived from “afternoon” (12:00–18:00) measurements.</p> Full article ">Figure 8
<p>Multi-angle two-parameter retrievals <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for the time period 15 December, 2016–15 March, 2017. Panels (<b>a</b>,<b>b</b>) show the time series of <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math>, respectively. Panels (<b>c</b>,<b>d</b>) and (<b>e</b>,<b>f</b>) show corresponding <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mi>R</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics> </math> for <span class="html-italic">RM</span> = “H” and “V”, respectively. Red markers show in-situ measured bottom-layer snow density <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math> and ground permittivity <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> (same data as shown in <a href="#remotesensing-10-00354-f002" class="html-fig">Figure 2</a>). The vertical dashed lines delimit the “snow-free period” (before 3 January), the “cold winter period” (3–31 January), and the “early spring period” (after 31 January). Retrievals are in red when the “quality flag” is raised and in blue when not raised. The “quality flag” approach used employs the threshold <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mrow> <mo> </mo> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mo>“</mo> <mi mathvariant="normal">V</mi> <mo>”</mo> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mo>“</mo> <mi mathvariant="normal">V</mi> <mo>”</mo> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> <mo>&lt;</mo> <mn>0.1</mn> </mrow> </semantics> </math> between <math display="inline"> <semantics> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> </mstyle> <mrow> <mo>“</mo> <mi mathvariant="normal">V</mi> <mo>”</mo> </mrow> </msup> <mo>=</mo> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>ρ</mi> <mi mathvariant="normal">S</mi> <mrow> <mo>“</mo> <mi mathvariant="normal">V</mi> <mo>”</mo> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mo>“</mo> <mi mathvariant="normal">V</mi> <mo>”</mo> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> retrievals computed from 12-hour asymmetric sliding windows.</p> Full article ">Figure 9
<p>(<b>a</b>,<b>b</b>) Time series of the footprint-specific <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi mathvariant="normal">S</mi> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> single-angle retrievals from 3 January (first snow event) to 15 March (end of measurement campaign). Colored bars show the color code of the retrievals performed at nadir angles <math display="inline"> <semantics> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> </semantics> </math> labelled on the left vertical axes. Failed retrievals are shown in blue.</p> Full article ">Figure 10
<p>(<b>a</b>) Single-angle retrievals <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi mathvariant="normal">G</mi> <mo>,</mo> <mi>steep</mi> </mrow> </msub> <mo>≡</mo> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> <mo>=</mo> <mn>30</mn> <mo>°</mo> <mo>,</mo> <mo> </mo> <mn>35</mn> <mo>°</mo> </mrow> </semantics> </math> (blue) and <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi mathvariant="normal">G</mi> <mo>,</mo> <mi>shallow</mi> </mrow> </msub> <mo>≡</mo> <mo stretchy="false">〈</mo> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> <mo stretchy="false">〉</mo> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> <mo>=</mo> <mn>60</mn> <mo>°</mo> <mo>,</mo> <mo> </mo> <mn>65</mn> <mo>°</mo> </mrow> </semantics> </math> (red). (<b>b</b>) Comparison of single-angle retrievals <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi mathvariant="normal">G</mi> <mo>,</mo> <mi>scan</mi> </mrow> </msub> <mo>≡</mo> <mo stretchy="false">〈</mo> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>θ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> <mo stretchy="false">〉</mo> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <mn>30</mn> <mo>°</mo> <mo>≤</mo> <msub> <mi>θ</mi> <mi>k</mi> </msub> <mo>≤</mo> <mn>65</mn> <mo>°</mo> </mrow> </semantics> </math> (blue) with multi-angle retrievals <math display="inline"> <semantics> <mrow> <msubsup> <mi>ε</mi> <mi mathvariant="normal">G</mi> <mrow> <mo>“</mo> <mi>HV</mi> <mo>”</mo> </mrow> </msubsup> </mrow> </semantics> </math> (red, same as in <a href="#remotesensing-10-00354-f006" class="html-fig">Figure 6</a>a). Spatially averaged in-situ references <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> (same as in <a href="#remotesensing-10-00354-f002" class="html-fig">Figure 2</a>a,b) and their spatial variability are shown by the green lines and gray areas, respectively. The specific date 21 February is marked with the vertical dashed black line.</p> Full article ">
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Article
Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)
by Yunlong Wang, Xiaodong Huang, Hui Liang, Yanhua Sun, Qisheng Feng and Tiangang Liang
Remote Sens. 2018, 10(1), 136; https://doi.org/10.3390/rs10010136 - 18 Jan 2018
Cited by 46 | Viewed by 6643
Abstract
Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) [...] Read more.
Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) the maximum, minimum, and annual average snow-covered area (SCA) in the Northern Hemisphere exhibited a fluctuating downward trend; the variation of snow cover in the Northern Hemisphere had well-defined inter-annual and regional differences; (2) the average SCA in the Northern Hemisphere was the largest in January and the smallest in August; the SCA exhibited a downward trend for the monthly variations from February to April; and the seasonal variation in the SCA exhibited a downward trend in the spring, summer, and fall in the Northern Hemisphere (no pronounced variation trend in the winter was observed) during the 2000–2015 period; (3) the spatial distribution of the annual average snow-covered day (SCD) was related to the latitudinal zonality, and the areas exhibiting an upward trend were mainly at the mid to low latitudes with unstable SCA variations; and (4) the snow reduction was significant in the perennial SCA in the Northern Hemisphere, including high-latitude and high-elevation mountainous regions (between 35° and 50°N), such as the Tibetan Plateau, the Tianshan Mountains, the Pamir Plateau in Asia, the Alps in Europe, the Caucasus Mountains, and the Cordillera Mountains in North America. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Graphical abstract

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Full article ">Figure 1
<p>Moderate Resolution Imaging Spectroradiometer (MODIS) daily cloud-free snow cover products flow chart. Note: T<sub>ij</sub>, Y<sub>ij</sub>, and N<sub>ij,</sub> represent the pixel values of the ith column and the jth row of the current day image, previous day image, and posterior day image, respectively.</p> Full article ">Figure 2
<p>Comparison of the cloud-free daily snow product and Landsat Thematic Mapper (TM) for different land cover types.</p> Full article ">Figure 3
<p>Examples of snow-cover maps from MODIS composites and Landsat-TM on 5 May 2007.</p> Full article ">Figure 4
<p>Annual maximum, average and minimum snow-covered area in the Northern Hemisphere from 2000 to 2015.</p> Full article ">Figure 5
<p>Box plots of the monthly averages of snow-covered areas between 2000 and 2015.</p> Full article ">Figure 6
<p>The average area proportion of snow cover in each season in the Northern Hemisphere from 2000 to 2015.</p> Full article ">Figure 7
<p>Area percentage of the different snow-covered days during the 16-year period from 2000 to 2015 in the Northern Hemisphere.</p> Full article ">Figure 8
<p>Spatial distribution of the average annual snow-covered days (SCD) during the period 2000–2015 in the Northern Hemisphere.</p> Full article ">Figure 9
<p>Significance of the variation in the annual SCDs in the Northern Hemisphere based on the Mann–Kendall method.</p> Full article ">Figure 10
<p>Variation slope of the average annual SCDs in the Northern Hemisphere based on Sen’s median method from 2000 to 2015.</p> Full article ">
3422 KiB  
Article
Inter-Calibration of Passive Microwave Satellite Brightness Temperatures Observed by F13 SSM/I and F17 SSMIS for the Retrieval of Snow Depth on Arctic First-Year Sea Ice
by Qingquan Liu, Qing Ji, Xiaoping Pang, Xin Gao, Xi Zhao and Ruibo Lei
Remote Sens. 2018, 10(1), 36; https://doi.org/10.3390/rs10010036 - 26 Dec 2017
Cited by 8 | Viewed by 4554
Abstract
Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea [...] Read more.
Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea ice was further retrieved. The SDs derived from F17 TB and F13C TB which were calibrated F17 TB using F13 TB as the baseline were then compared and evaluated against in situ SD measurements based on the Operational IceBridge (OIB) airborne observations from 2009 to 2013. Results show that Cavalieri inter-calibration models (CA models) perform smaller root mean square error (RMSE) than Dai inter-calibration models (DA models), and the standard deviation of OIB SDs in the 25 km pixels is around 6 cm on first-year sea ice. Moreover, the SDs derived from the calibrated F17 TB using F13 TB as the baseline were in better agreement than the F17 SDs as compared with OIB SDs, with the biases of −2 cm (RMSE of 5 cm) and −9 cm (RMSE of 10 cm), respectively. We conclude that TB observations from F17 SSMIS calibrated to F13 SSM/I as the baseline should be recommended when performing the sensors’ biases correction for SD purpose based on the existing algorithm. These findings could serve as a reference for generating more consistent and reliable TB, which could help to improve the retrieval and analysis of long-term snow depth on the Arctic first-year sea ice. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Figure 1
<p>Study area overlapped with F13 SSM/I brightness temperatures and F17 SSMIS brightness temperatures (for the channel of 19H, 19V, 22V, 37V) on 20 March 2007.</p> Full article ">Figure 2
<p>Flow chart of processing, the number in the parentheses represents the year of the data.</p> Full article ">Figure 3
<p>Transects of OIB SD measurements on 100% first-year sea ice. The background represents the F17 SD at 25 km resolution. (<b>a</b>) represents the transects on 15 March 2012, north of the Alaska, and the two transects across the F17 SD 25 km pixels (referenced P1 and P2); (<b>b</b>) represents the transects on 17 March 2012, north of the Alaska, and the two transects across the F17 SD 25 km pixel (referenced P3).</p> Full article ">Figure 4
<p>Comparison between satellites derived SD and OIB measured SD: (<b>a</b>) F17 snow depth vs. OIB snow depth; (<b>b</b>) F13C snow depth vs. OIB snow depth. F13C snow depths are derived from F13C TB which are the result of F17 TB calibrated to F13 using CA models according to Cavalieri et al. [<a href="#B19-remotesensing-10-00036" class="html-bibr">19</a>]. The solid line is the one-to-one fitting line, and the dashed line is the regression line of F17 or F13C to OIB snow depth and <span class="html-italic">r</span> refers to the correlation coefficient<span class="html-italic">;</span> N refers to the pixel’s number, and n presents the total OIB snow depth records in those pixels.</p> Full article ">Figure 5
<p>Comparison among F13 SD, F17 SD and F13C SD. F13 SD from 2005 to 2007 are drawn in green line, F17 SD from 2007 to 2014 are drawn in blue line, and F13C SD from 2007 to 2014 are drawn in red line.</p> Full article ">
5342 KiB  
Article
Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm
by Yang Liu, Lanhai Li, Jinming Yang, Xi Chen and Jiansheng Hao
Remote Sens. 2017, 9(11), 1195; https://doi.org/10.3390/rs9111195 - 21 Nov 2017
Cited by 23 | Viewed by 6189
Abstract
Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the [...] Read more.
Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R2 = 0.31 vs. R2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R2 = 0.27 vs. R2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Full article ">Figure 1
<p>Sketch map of the study area of Bayanbulak.</p> Full article ">Figure 2
<p>Phase unwrapping map on 18 December 2016 (<b>a</b>) and 11 January 2017 (<b>b</b>).</p> Full article ">Figure 3
<p>Map of snow cover distribution in a typical region of the Bayanbulak Basin on 18 December 2016 (<b>a</b>) and 11 January 2017 (<b>b</b>).</p> Full article ">Figure 4
<p>Images of the local incidence angle on 18 December 2016 (<b>a</b>) and 11 January 2017 (<b>b</b>).</p> Full article ">Figure 5
<p>Spatial distribution of snow depth inversion on 18 December 2016 (<b>a1</b>) and 11 January 2017 (<b>a2</b>); spatial distribution of estimated snow depth by three dimensional variational (3DVAR) fusion algorithm on 18 December 2016 (<b>b1</b>) and 11 January 2017 (<b>b2</b>).</p> Full article ">Figure 6
<p>Comparison between actual snow depth, inversion, and the 3DVAR fusion snow depth on 18 December 2016 (<b>a</b>) and 11 January 2017 (<b>b</b>).</p> Full article ">Figure 7
<p>Coherence map after filtering on 18 December 2016 (<b>a</b>) and 11 January 2017 (<b>b</b>).</p> Full article ">Figure 8
<p>The verified snow density in observation stations on 18 December 2016 (<b>a</b>) and 11 January 2017 (<b>b</b>).</p> Full article ">
10174 KiB  
Article
Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis
by Reza Naderpour, Mike Schwank and Christian Mätzler
Remote Sens. 2017, 9(11), 1185; https://doi.org/10.3390/rs9111185 - 21 Nov 2017
Cited by 27 | Viewed by 5490
Abstract
The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer [...] Read more.
The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional “normality” tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as “early-spring snow”. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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<p>Schematics of the footprint areas and the location of the in-situ sensors. ELBARA-II was initially installed at the center (position P1) of the upper-most platform of the tower. The radiometer scaffold was moved to position P2 (bottom right corner of the tower) on 12 December 2016 for RFI improvement.</p> Full article ">Figure 2
<p>(<b>a</b>) Measured snowpack height <math display="inline"> <semantics> <mrow> <msub> <mi>h</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math>; and (<b>b</b>) average bottom-layer snow density <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math> over time. The snow cover quickly melted down in the second half of March 2017 and almost disappeared within approximately the last 10 day of the measurement campaign.</p> Full article ">Figure 3
<p>NIR photos of the snowpack profile taken on (<b>a</b>) 9 January and (<b>b</b>) 27 February. In addition to an increased snow height, significantly more complex layering and more variable snow grain size and types can be observed in the second profile.</p> Full article ">Figure 4
<p>Panels (<b>a</b>,<b>b</b>) show the time series of in-situ measured <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> along transects 1 and 2 (shown in <a href="#remotesensing-09-01185-f001" class="html-fig">Figure 1</a>), respectively. Panel (<b>c</b>) shows the average ground temperature <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi mathvariant="normal">G</mi> </msub> </mrow> </semantics> </math> measured by the 12 SMT-100 sensors along transects 1 and 2. Panel (<b>d</b>) indicates temperatures <math display="inline"> <semantics> <mrow> <mrow> <msub> <mi>T</mi> <mrow> <mi>air</mi> </mrow> </msub> </mrow> <mo>,</mo> <mo> </mo> <mrow> <msub> <mi>T</mi> <mrow> <mn>15</mn> <mo> </mo> <mi>cm</mi> </mrow> </msub> </mrow> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mn>50</mn> <mo> </mo> <mi>cm</mi> </mrow> </msub> </mrow> </semantics> </math> measured by ELBARA-II’s PT-100 temperature sensor and SMT-100 sensors placed 15 cm and 50 cm above ground, respectively. Panel (<b>e</b>) shows the recorded precipitation (both rain and snow) in units of mm/10 min. over the entire campaign.</p> Full article ">Figure 5
<p>Flowchart of the approach used to convert ELBARA-II raw data into calibrated <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mi mathvariant="normal">B</mi> <mi>p</mi> </msubsup> </mrow> </semantics> </math>.</p> Full article ">Figure 6
<p>Four examples of measured sample voltage distributions <math display="inline"> <semantics> <mrow> <mi>P</mi> <mi>D</mi> <msub> <mi>F</mi> <mi mathvariant="normal">m</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mrow> <msubsup> <mi>U</mi> <mrow> <mi>RMA</mi> <mo>,</mo> <mi>i</mi> </mrow> <mrow> <mi mathvariant="normal">V</mi> <mo>,</mo> <mn>1</mn> </mrow> </msubsup> </mrow> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics> </math>, and the corresponding Gaussian fits <math display="inline"> <semantics> <mrow> <mi>P</mi> <mi>D</mi> <msub> <mi>F</mi> <mrow> <mi>Gauss</mi> </mrow> </msub> <mrow> <mo stretchy="false">(</mo> <mrow> <msubsup> <mi>U</mi> <mrow> <mi>RMA</mi> <mo>,</mo> <mi>i</mi> </mrow> <mrow> <mi mathvariant="normal">V</mi> <mo>,</mo> <mn>1</mn> </mrow> </msubsup> </mrow> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics> </math>: (<b>a</b>) a “healthy” measurement; and (<b>b</b>–<b>d</b>) distorted measurements.</p> Full article ">Figure 6 Cont.
<p>Four examples of measured sample voltage distributions <math display="inline"> <semantics> <mrow> <mi>P</mi> <mi>D</mi> <msub> <mi>F</mi> <mi mathvariant="normal">m</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mrow> <msubsup> <mi>U</mi> <mrow> <mi>RMA</mi> <mo>,</mo> <mi>i</mi> </mrow> <mrow> <mi mathvariant="normal">V</mi> <mo>,</mo> <mn>1</mn> </mrow> </msubsup> </mrow> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics> </math>, and the corresponding Gaussian fits <math display="inline"> <semantics> <mrow> <mi>P</mi> <mi>D</mi> <msub> <mi>F</mi> <mrow> <mi>Gauss</mi> </mrow> </msub> <mrow> <mo stretchy="false">(</mo> <mrow> <msubsup> <mi>U</mi> <mrow> <mi>RMA</mi> <mo>,</mo> <mi>i</mi> </mrow> <mrow> <mi mathvariant="normal">V</mi> <mo>,</mo> <mn>1</mn> </mrow> </msubsup> </mrow> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics> </math>: (<b>a</b>) a “healthy” measurement; and (<b>b</b>–<b>d</b>) distorted measurements.</p> Full article ">Figure 7
<p>Flowchart illustrating the course of action followed to estimate effective losses of transmission line (TL) and active cold source (ACS) noise temperatures.</p> Full article ">Figure 8
<p>(<b>a</b>) Effective <math display="inline"> <semantics> <mrow> <msubsup> <mi>L</mi> <mrow> <mi>TL</mi> </mrow> <mi>p</mi> </msubsup> <msup> <mrow/> <mo>∗</mo> </msup> </mrow> </semantics> </math> and (<b>b</b>) 〈<math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi>ACS</mi> </mrow> <mrow> <mi>p</mi> <mo>,</mo> <mi>c</mi> <mi>h</mi> </mrow> </msubsup> </mrow> </semantics> </math>〉 (<span class="html-italic">ch</span> = 1, 2 and <span class="html-italic">p</span> = H, V) computed with increasing numbers of sky measurements.</p> Full article ">Figure 9
<p>(<b>a</b>) Block diagram of the L-band Specific Microwave Emission Model of Layered Snowpacks (“LS—MEMLS”) used to simulate L-band brightness temperatures <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mi mathvariant="normal">B</mi> <mi>p</mi> </msubsup> </mrow> </semantics> </math> over snow covered grounds; and (<b>b</b>) sketch of the two-stream emission model (2S-EM) employed in “LS—MEMLS”. Symbols and model components are explained in the text.</p> Full article ">Figure 10
<p>Sensitivities of brightness temperatures with respect to snow liquid water column <math display="inline"> <semantics> <mrow> <mi>W</mi> <msub> <mi>C</mi> <mi mathvariant="normal">S</mi> </msub> </mrow> </semantics> </math> for snow mass density <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi mathvariant="normal">S</mi> </msub> <mo>=</mo> <mn>300</mn> <mo> </mo> <msup> <mrow> <mi>kg</mi> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics> </math>: (<b>a</b>) “uniform” snow; (<b>b</b>) snow on “top” of the snowpack; (<b>c</b>) moist snow “sandwiched” in-between dry snow; and (<b>d</b>) a moist “bottom” snow-layer. <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mi>p</mi> </msubsup> </mrow> </semantics> </math> (thin lines) and <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mi>p</mi> </msubsup> </mrow></semantics> </math> (bold lines) are for snow atop the reflector (R) and atop the natural (N) frozen ground with <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">G</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, respectively. Polarization <span class="html-italic">p</span> = H (solid), <span class="html-italic">p</span> = V (dashed), and the nadir angles <math display="inline"> <semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics> </math> (black), <math display="inline"> <semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics> </math> (red), and <math display="inline"> <semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>60</mn> <mo>°</mo> </mrow> </semantics> </math> (green).</p> Full article ">Figure 11
<p><math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">N</mi> </mrow> <mi>p</mi> </msubsup> </mrow> </semantics> </math> (blue) and <math display="inline"> <semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi mathvariant="normal">B</mi> <mo>,</mo> <mi mathvariant="normal">R</mi> </mrow> <mi>p</mi> </msubsup> </mrow> </semantics> </math> (magenta) of brightness temperatures at: (<b>a</b>) horizontal (<span class="html-italic">p</span> = H); and (<b>b</b>) vertical (<span class="html-italic">p</span> = V) polarization measured over “natural” and “reflector” areas, respectively. Panels (<b>c</b>,<b>d</b>) show air temperature <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>air</mi> </mrow> </msub> </mrow> </semantics> </math> and precipitation rate, respectively. The vertical dashed lines delimit the “snow-free period” (before 3 January), the “cold winter period” (3–31 January), and the “early spring period” (after 31 January).</p> Full article ">
27596 KiB  
Article
Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
by Chunxia Zhou and Lei Zheng
Remote Sens. 2017, 9(11), 1171; https://doi.org/10.3390/rs9111171 - 15 Nov 2017
Cited by 44 | Viewed by 8593
Abstract
Surface snowmelt causes changes in mass and energy balance, and endangers the stabilities of the ice shelves in the Antarctic Peninsula (AP). The dynamic changes of the snow and ice conditions in the AP were observed by Sentinel-1 images with a spatial resolution [...] Read more.
Surface snowmelt causes changes in mass and energy balance, and endangers the stabilities of the ice shelves in the Antarctic Peninsula (AP). The dynamic changes of the snow and ice conditions in the AP were observed by Sentinel-1 images with a spatial resolution of 40 m in this study. Snowmelt detected by the special sensor microwave/imager (SSM/I) is used to study the relationship between summer snowmelt and winter synthetic aperture radar (SAR) backscatter. Radar glacier zones (RGZs) classifications were conducted based on their differences in liquid snow content, snow grain size, and the relative elevations. We developed a practical method based on the simulations of a microwave scattering model to classify RGZs by using Sentinel-1 images in the AP. The summer snowmelt detected by SSM/I and Sentinel-1 data are compared between 2014 and 2015. The SSM/I-derived melting days is used to validate the winter dry snow line (DSL). RGZs derived from Sentinel-1 images suggest that snowmelt expanded from inland of the Larsen C Ice Shelf to the coastal area, whereas an opposite direction was found in the George VI Ice Shelf. The long melting season in the grounding zone of the Larsen C Ice Shelf may result from the adiabatically-dried föhn winds on the east side of the AP. As the uppermost limit of summer snowmelt, DSL was mapped based on the winter Sentinel-1 mosaic of the AP. Compared with the SSM/I-derived melting days, the winter DSL mainly distributed in the areas melted for one to three days in summer. DSL elevations on the Palmer Land increased from south to north. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map of the Antarctic Peninsula (AP). (<b>a</b>) Location of the AP on Antarctica; (<b>b</b>) Digital elevation model (DEM) of the AP obtained from the National Snow and Ice Data Center (NSIDC, <a href="http://nsidc.org/" target="_blank">http://nsidc.org/</a>). The black line is the boundary of the study area, and the colored rectangles are the frames of Sentinel-1 images used in this study. The base map was obtained from Earthstar Geographics (<a href="http://www.terracolor.net/" target="_blank">http://www.terracolor.net/</a>).</p> Full article ">Figure 2
<p>Ascending special sensor microwave/imager (SSM/I) 19.35 GHz <span class="html-italic">T<sub>b</sub></span> in the horizontal polarization of two pixels from July 2014 to June 2015. The red line represents a melting pixel with a threshold of 190 K (red dot line) on the Larsen C Ice Shelf. The green line represents a frozen pixel with a threshold of 232 K (green dot line) on Palmer Land.</p> Full article ">Figure 3
<p>C-band <span class="html-italic">σ</span><sup>0</sup> changes with liquid water content simulated by MEMLS3&amp;a. Incidence angle was set as 30°, and snow depth was set as 1, 10, 50, and 200 cm in (<b>a</b>–<b>d</b>) respectively. Simulations with snow density ranging from 350 kg/m<sup>3</sup> to 540 kg/m<sup>3</sup> and snow grain size ranging from 0.5 mm to 3 mm were conducted.</p> Full article ">Figure 4
<p>C-band <span class="html-italic">σ</span><sup>0</sup> changes along with snow depth simulated by MEMLS3&amp;a. Incidence angle was set as 30°, coarse-grained (3–8 mm) snowpacks and fine-grained (0.5–2 mm) snowpacks of different snow densities (350–540 kg/m<sup>3</sup>) were considered.</p> Full article ">Figure 5
<p>Flowchart of the RGZ mapping with Sentinel-1 synthetic aperture radar (SAR) images.</p> Full article ">Figure 6
<p>(<b>a</b>) Winter mean ascending SSM/I 19.35 GHz <span class="html-italic">T<sub>b</sub></span> in horizontal polarization. (<b>b</b>) Melting days over the AP derived from SSM/I data during 2014–2015. (<b>c</b>) Normalized radar backscatter of Sentinel-1 mosaic with the reference angle of 30°. The yellow star shows the location of the Larsen F Ice Shelf, and the upstream Swan Glacier.</p> Full article ">Figure 7
<p>Relationships between melting days, elevation, and backscatter. (<b>a</b>) shows the mean melting days at 100 m elevation intervals (blue empty dots). (<b>b</b>) shows the mean winter <span class="html-italic">σ</span><sup>0</sup> for every five melting days (blue empty dots). The red lines and green bars represent the linear trend and sample size, respectively.</p> Full article ">Figure 8
<p>RGZs of the middle AP derived from Sentinel-1 images. (<b>a</b>) shows the reference image for mapping of the wet snow radar zone. The map on the bottom left shows the location of the images in the AP. (<b>b</b>–<b>i</b>) illustrate the dry snow (orange area), frozen percolation (cyan area), wet snow (red area), and bare ice radar zone (blue area) from 2 November 2014 to 2 March 2015.</p> Full article ">Figure 9
<p>Comparison of 2-m air temperature (red solid line) and melt areas detected by Sentinel-1 (cyan stems) and the SSM/I (green areas). Melting signals were detected when mean air temperature rose above 266 K (red dotted line).</p> Full article ">Figure 10
<p>Winter <span class="html-italic">σ</span><sup>0</sup> (blue line), melting days (green line), and elevation (black line) along a profile across the Larsen F Ice Shelf and the upstream Swan Glacier (corresponding to the star in <a href="#remotesensing-09-01171-f006" class="html-fig">Figure 6</a>c). The red line marks the location of the dry snow line (DSL). The backscatter map at the bottom left corner shows the route of profile AB (yellow line).</p> Full article ">Figure 11
<p>(<b>a</b>) shows the dry snow radar zone (yellow area) and DSL (red line) derived by Sentinel-1. The DSL was overlapped with melting days and elevation in (<b>b</b>,<b>c</b>), respectively.</p> Full article ">Figure 12
<p>Boxplot of the DSL elevations at a 0.5° interval of latitude on the Palmer land.</p> Full article ">Figure 13
<p>Misclassifications of RGZs in Agassiz Cape on 14 November 2015. (<b>a</b>) shows the location of Agassiz Cape and the misclassifications of the crevasses and mountains. Blue and yellow areas represent bare ice and dry snow, respectively. The base map is a Landsat 8 image from 2 November 2015 derived from the United States Geological Survey (USGS, <a href="http://glovis.usgs.gov/" target="_blank">http://glovis.usgs.gov/</a>). (<b>b</b>) illustrates the ice crevasses on the corresponding SAR image. (<b>c</b>) shows the local incidence angle.</p> Full article ">
5569 KiB  
Article
Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic
by Emiliano Cimoli, Marco Marcer, Baptiste Vandecrux, Carl E. Bøggild, Guy Williams and Sebastian B. Simonsen
Remote Sens. 2017, 9(11), 1144; https://doi.org/10.3390/rs9111144 - 7 Nov 2017
Cited by 39 | Viewed by 8224
Abstract
The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology [...] Read more.
The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m2. The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Figure 1

Figure 1
<p>Location of the surveyed areas with the generated orthomosaics (both SDEMs and TDEMs). The dotted line overlapped in the TDEMs indicates the areal extent of the SDEMs. The orthophotos display the position of the ground control points (GCPs) distributed across the surface that were used for georeferencing each area. The co-ground control points (CGCPs) refer to the number of GCPs shared in the co-georeferencing process. Camera positions refer to the land-based Structure from Motion (SfM) mapping performed for some of the terrain topographies. The orthomosaics assist in providing visual information on the snow surface and terrain type.</p> Full article ">Figure 2
<p>(<b>a</b>) The minimal set-up during the image acquisition phase for area Sval1 in overcast/fair conditions. (<b>b</b>) Global Navigation Satellite System (GNSS) data acquisition phase using the JAVAD antenna and receiver for area Sval2. Overcast and sastrugi sculpted snow can be observed. (<b>c</b>) Snow depth probing on a GCP location for area Sval1 during overcast conditions. (<b>d</b>) Survey preparation for area Green1 during fair conditions on completely fresh and featureless smooth snow. (<b>e</b>) Featureless conditions of area Green2 in clear/fair sky. (<b>f</b>) Typical low vegetation shrubs (5 to 30 cm) found in Arctic regions.</p> Full article ">Figure 3
<p>Schematization of the methodology performed for each surveyed area. The output box summarizes the overall total output of the study for all the surveyed areas. The tools/equipment used for each step are shown within the white boxes associated with each step. Although this outlines the equipment/software used in this study, several alternatives are available. SDEM and TDEM refer to both snow and terrain Digital Elevation Models (DEMs) respectively. GCP stands for the number of ground control points and GNSS for Global Navigation Satellite System.</p> Full article ">Figure 4
<p>Produced snow depth maps for all the surveyed areas, displayed over their underlying terrain reliefs. Maps were filtered from outliers outside the displayed color bar range (scattered and isolated pixels) for each specific map. The maps include the location of the validation points (VPs) that measured the difference between probed snow depth (<span class="html-italic">HS<sub>m</sub></span>) and the estimated snow depth (<span class="html-italic">HS<sub>UAS</sub></span>) locations in meters (m) (cross marks). This figure showcases the overall feasibility and performance of the proposed low-cost method to produce snow depth maps. It is noted that sectors distant from the GCP network are expected to be less representative of the actual snow depth due to inferior georeferencing.</p> Full article ">Figure 5
<p>Results from the snow-free validation procedure over all the surveyed areas. The histograms represent the distribution of pixel samples of estimated snow depth (<span class="html-italic">HS<sub>UAS</sub></span>) in snow-free areas providing an additional validation source. This assessment assesses the overall integrity of the SDEM models correct reconstruction through comparison with a different data source (the TDEMs) at common points away from the GCPs. Resolution refers to the pixel size of the sample in the SDEM orthophotos. Samples refer to the number of pixels extracted from the SDEM having an RGB mean inferior to 0.2 (associated with dark, snow-free pixels).</p> Full article ">Figure 6
<p>Snow depth transects across two exemplar cases of diverse underlying topographies are shown for both the advanced set-up on area Green1 (<b>a</b>) and for the minimal set-up on area Sval1 (<b>b</b>). The blue line represents the snow cover, the brown line the underlying topography. Panel (<b>c</b>) displays an example of topography induced shadow which introduces a Structure from Motion (SfM) error in the reconstruction. The arrow at the marked position in panel (<b>b</b>) indicates the location of this error. The arrow in panel (<b>a</b>) indicates instead the position of the reconstruction error associated with the land-based surveys’ poor camera positions. Overall, highly detailed features such as snow deposition on top of the boulders and small-scale footsteps are correctly represented.</p> Full article ">Figure 7
<p>(<b>a</b>,<b>b</b>) Typical considerations for image acquisition when performing an UAS-SfM survey; (<b>c</b>) UAS flight parameters required to calculate the ground footprint covered by the camera’s Field of View (FOV), following Equation (A1).</p> Full article ">Figure 8
<p>(<b>a</b>,<b>b</b>) Number of valid matches in an exemplar image from the advanced set-up with (<b>a</b>) and without (<b>b</b>) pre-processing using high-quality reconstruction setting in Photoscan. (<b>c</b>,<b>d</b>) Number of matches in another exemplar image from the advanced set-up with (<b>c</b>) and without pre-processing (<b>d</b>) using low-quality reconstruction setting in Photoscan. (<b>e</b>,<b>f</b>) Number of matches in minimal set-up with (<b>e</b>) and without (<b>f</b>) pre-processing using medium-quality reconstruction. Blue dots represent valid matched points between that image and overlapping image pairs. Gray dots refer to matched points that were not used in the DEM reconstruction process. This process was effective at increasing the number of matched points by between 10% and 105% depending on the image and quality of the reconstruction</p> Full article ">Figure 9
<p>Sub-section of area Green1 characterized by a fresh and smooth snow surface under fair conditions and mapped using the minimal set-up. The figure illustrates the extremely positive effect of the proposed image pre-processing method for feature-less areas mapped with the minimal set-up. (<b>a</b>) Area reconstructed without applying the image pre-processing workflow (<b>left</b>) and an exemplar image with matched points from the area photoset (<b>right</b>). (<b>b</b>) Area reconstructed with the image pre-processing workflow (<b>left</b>) and an exemplar image with matched points from the area photoset (<b>right</b>). Points refers to the numbers of points in the generated dense point cloud.</p> Full article ">Figure 10
<p>The impact of the image pre-processing workflow on the dense point clouds for different image enhancement intensities. Top row (<b>a</b>–<b>c</b>) is for the minimal set-up in area Sval2 for a sastrugi-sculpted surface and overcast day and bottom row (<b>d</b>–<b>f</b>) is for the advanced set-up in area Green3 with a slightly ripple-marked smooth snow area under good light conditions. (<b>a</b>) The dense point cloud section without image pre-processing; (<b>b</b>) the negative effects produced by the poor quality of the image; (<b>c</b>) the positive effect of image pre-processing for this type of area after reducing the quality of the reconstruction; (<b>d</b>) the impact on the dense cloud without image pre-processing; (<b>e</b>) the negative effect of overenhancing images due to the generation of pixel noise; (<b>f</b>) enhancing image content in consideration of the scale of the mapped features results in an improved solution. “Points” refers to the numbers of points in the generated dense point cloud. Overall, the survey conditions need to be considered when enhancing snow images for snow surface reconstructions.</p> Full article ">Figure 11
<p>Assessment of the proposed radial lens distortion correction performed over a small exemplar imagery sub-set from the advanced set-up. (<b>a</b>) Evidence of the systematic error present by subtracting the same generated DEM with and without the use of oblique imagery; (<b>b</b>) transect over image (<b>a</b>) showing the correction through addition of oblique imagery. In a similar fashion, (<b>c</b>,<b>d</b>) illustrates the minor improvement from correcting image distortion using radial correction software (Agisoft Lens). Both solutions were implemented across all photosets in the image pre-processing phase.</p> Full article ">
10284 KiB  
Article
Spatiotemporal Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free MODIS Fractional Snow Cover Product, 2001–2015
by Zhiguang Tang, Xiaoru Wang, Jian Wang, Xin Wang, Hongyi Li and Zongli Jiang
Remote Sens. 2017, 9(10), 1045; https://doi.org/10.3390/rs9101045 - 13 Oct 2017
Cited by 81 | Viewed by 7579
Abstract
The change in snow cover under climate change is poorly understood in Tianshan Mountains. Here, we investigate the spatiotemporal characteristics and trends of snow-covered area (SCA) and snow-covered days (SCD) in the Tianshan Mountains by using the cloud-removed MODIS fractional snow cover datasets [...] Read more.
The change in snow cover under climate change is poorly understood in Tianshan Mountains. Here, we investigate the spatiotemporal characteristics and trends of snow-covered area (SCA) and snow-covered days (SCD) in the Tianshan Mountains by using the cloud-removed MODIS fractional snow cover datasets from 2001–2015. The possible linkage between the snow cover and temperature and precipitation changes over the Tianshan Mountains is also investigated. The results are as follows: (1) The distribution of snow cover over the Tianshan Mountains exhibits a large spatiotemporal heterogeneity. The areas with SCD greater than 120 days are distributed in the principal mountains with elevations of above 3000 m. (2) In total, 26.39% (5.09% with a significant decline) and 34.26% (2.81% with a significant increase) of the study area show declining and increasing trend in SCD, respectively. The SCD mainly decreases in Central and Eastern Tianshan (decreased by 11.88% and 8.03%, respectively), while it increases in Northern and Western Tianshan (increased by 9.36% and 7.47%). (3) The snow cover variations are linked to the temperature and precipitation changes. Temperature tends to be the major factor effecting the snow cover changes in the Tianshan Mountains during 2001–2015. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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<p>Location and the extent of the study area, and boundaries of four subregions. Blue dots denote meteorological stations.</p> Full article ">Figure 2
<p>Monthly average of precipitation and temperature in the Tianshan Mountains during 2001–2015.</p> Full article ">Figure 3
<p>Original MODIS fractional snow cover (FSC) map (<b>a</b>); and the cloud-removed MODIS FSC map (<b>b</b>) on the 315th day of 2014.</p> Full article ">Figure 4
<p>Dynamic changes of daily snow-covered area (SCA) percentage (%) for four subdivided regions during 2001–2015 (<b>a</b>); and annual cycle of SCA (%) for the four subregions (<b>b</b>); and the whole region of the Tianshan Mountains (<b>b</b>). The SCA values in (<b>b</b>,<b>c</b>) are averages of 15 years from 2001 to 2015. The error bars in (<b>c</b>) show the standard deviation, indicating the interannual variations of SCA from 2001 to 2015.</p> Full article ">Figure 5
<p>Spatial patterns of monthly mean fractional snow cover (FSC, %) in the Tianshan Mountains for 2001–2015.</p> Full article ">Figure 6
<p>Interannual variation of seasonal mean snow-covered area (SCA, %) in: spring (<b>a</b>); summer (<b>b</b>); autumn (<b>c</b>); and winter (<b>d</b>), for different subregions and whole Tianshan Mountains from 2001 to 2015.</p> Full article ">Figure 7
<p>Spatial distribution of yearly Snow-covered days (SCD) in the Tianshan Mountains from 2001 to 2015.</p> Full article ">Figure 8
<p>Change trend (<span class="html-italic">Slope</span>, days/year) of SCD (<b>a</b>); and its significance level (<b>b</b>); and the change percentage (<span class="html-italic">Chp</span>, %) (<b>c</b>) in pixel scale during 2001–2015 over the Tianshan Mountains. Significant changes indicate its statistical significance at the 5% level.</p> Full article ">Figure 9
<p>Interannual variation of SCD for different subregions and whole Tianshan Mountains from 2001 to 2015.</p> Full article ">
5699 KiB  
Article
Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery
by Rafael Pimentel, Javier Herrero and María José Polo
Remote Sens. 2017, 9(10), 995; https://doi.org/10.3390/rs9100995 - 26 Sep 2017
Cited by 23 | Viewed by 6342
Abstract
Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area [...] Read more.
Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area and the contribution of the patchy areas to the seasonal and annual regime of the snow in a semiarid mountainous range, the Sierra Nevada Mountains in southern Spain, by means of Landsat imagery combined with terrestrial photography (TP). Two methodologies were tested: (1) difference indexes to produce binary maps; and (2) spectral mixture analysis (SMA) to obtain fractional maps; their results were validated from “ground-truth” data by means of TP in a small monitored control area. Both methods provided satisfactory results when the snow cover was above 85% of the study area; below this threshold, the use of spectral mixture analysis is clearly recommended. Mixed pixels can reach up to 40% of the area during wet and cold years, their importance being larger as altitude increases, proving the usefulness of TP for assessing the accuracy of remote data sources. Mixed pixels identification allows for determining the more vulnerable areas facing potential changes of the snow regime due to global warming and climate variability. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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<p>(<b>A</b>) Location of Sierra Nevada Mountain in Spain; (<b>B</b>) Sierra Nevada Mountain range, limits of the Natural and National Parks (dark and light blue respectively) and 1500 m a.s.l. elevation line (black line); (<b>C</b>) Location of the control area monitored by terrestrial photography at Durcal hillside.</p> Full article ">Figure 2
<p>Landsat scenes, TM (grey dots) and ETM+ (black crosses) analyzed throughout the study period 2000–2013 over Sierra Nevada Mountain. Available Terrestrial Photography (TP) used as validation dataset (red dots) over the control area throughout the validation period (2009–2013).</p> Full article ">Figure 3
<p>(<b>A</b>) Evolution of the averaged snow cover fraction in Sierra Nevada Mountain above the threshold altitude of 1000 m throughout the study period (2000–2013) from both binary (grey dots) and fractional (black crosses) algorithms, and (<b>B</b>) the associated standard deviation values, together with their difference.</p> Full article ">Figure 4
<p>Distribution of the mean and standard deviation of the SCF (m<sup>2</sup> m<sup>−2</sup>) over the study area during the study period 2000–2013 using a binary and a fractional algorithm.</p> Full article ">Figure 5
<p>(<b>A</b>) Selected examples of different stages during the snow season: accumulation—complete cover (10 January 2011), beginning of the spring melting (16 June 2013) and end of the melting (29 June 2009), using the Landsat (binary and fractional) methodologies and TP at the control area. (<b>B</b>) Evolution of the three metrics (precision, recall and accuracy) used during the validation period (2009–2013) for both methodologies (right); distribution function (boxplot, the central line and the upper and lower edges of the box represent the median, 75th and 25th percentiles respectively; the black whiskers extend to the extreme values considered in the analysis; and the red crosses are the outliers) of each metric for each method (left).</p> Full article ">Figure 6
<p>Evolution of SCF over the control area throughout the validation period (2009–2013) (<b>right</b>). Dispersion graph comparing the evaluated SCF obtained from Landsat with the validation dataset obtained calculated using TP (<b>left</b>).</p> Full article ">Figure 7
<p>Left: Temporal evolution of the average SCF value at the Sierra Nevada range during the study period for the whole study area (<b>A</b>) and distributed within 250 m-range elevation bands (<b>B</b>). Right: Monthly distribution of the average SCF values (black dots) and monthly mean value (red line) for each case.</p> Full article ">Figure 8
<p>(<b>A</b>) Cumulative distribution function (cdf) of the average SCF in the study area for the 2000–2013 period. (<b>B</b>) Spatial distribution of selected percentiles (5th, 10th, 25th, 50th, 75th, 90th and 95th) of the pixel-SCF cdfs.</p> Full article ">Figure 9
<p>(<b>A</b>) Temporal evolution of the fraction of mixed pixels at the Sierra Nevada range during the study period for the whole study area (number of mixed pixels/total pixels) and (<b>B</b>) distributed within 250 m-range elevation bands (number of mixed pixels/pixels in the elevation band).</p> Full article ">
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Article
Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China
by Gongxue Wang, Lingmei Jiang, Shengli Wu, Jiancheng Shi, Shirui Hao and Xiaojing Liu
Remote Sens. 2017, 9(10), 983; https://doi.org/10.3390/rs9100983 - 22 Sep 2017
Cited by 17 | Viewed by 7312
Abstract
Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary [...] Read more.
Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R2 values ranging from 0.46 to 0.80. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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<p>Land cover types and fractional snow cover (FSC) validation regions of China. OLI: Operational Land Imager.</p> Full article ">Figure 2
<p>FY-2 binary snow cover algorithm (phase 2).</p> Full article ">Figure 3
<p>Comparison of results from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and FY-2E/F VISSR over the main part of China on 19 February 2014: (<b>a</b>) the false color image of Terra MODIS Band 6, 5, and 4; (<b>b</b>) FSC from Terra MODIS using the multiple end-member spectral mixture analysis (MESMA); (<b>c</b>) daily FSC from the FY-2E VISSR using the linear spectral mixture analysis; and (<b>d</b>) daily FSC from the FY-2F VISSR using the linear spectral mixture analysis.</p> Full article ">Figure 4
<p>Scatter plots of the FY-2E (<b>a</b>) and FY-2F (<b>b</b>) FSC results versus the Terra MODIS FSC over the main part of China on 19 February 2014.</p> Full article ">Figure 5
<p>Comparison of results from Landsat-8 OLI and FY-2E/F VISSR of the region L1 on 20 January 2014: (<b>a</b>) the false color composite of Landsat-8 OLI Band 6, 5, and 3 at Path 134/Row 37; (<b>b</b>) the 0.05° FSC map from Landsat-8 OLI; (<b>c</b>) the FY-2E VISSR FSC map at 03:00 UTC; and (<b>d</b>) the FY-2F VISSR FSC map at 03:30 UTC.</p> Full article ">Figure 6
<p>Comparison of results from Landsat-8 OLI and FY-2E/F VISSR of the region L2 on 28 January 2014: (<b>a</b>) the false color composite of Landsat-8 OLI Band 6, 5, and 3 at Path 142/Row 37; (<b>b</b>) the 0.05° FSC map from Landsat-8 OLI; (<b>c</b>) the FY-2E VISSR FSC map at 04:00 UTC; and (<b>d</b>) the FY-2F VISSR FSC map at 04:30 UTC.</p> Full article ">Figure 7
<p>Comparison of results from Landsat-8 OLI and FY-2E/F VISSR of the region L3 on 21 February 2014: (<b>a</b>) the false color composite of Landsat-8 OLI Band 6, 5, and 3 at Path 118/Row 30; (<b>b</b>) the 0.05° FSC map from Landsat-8 OLI; (<b>c</b>) the FY-2E VISSR FSC map at 02:00 UTC; and (<b>d</b>) the FY-2F VISSR FSC map at 02:30 UTC.</p> Full article ">Figure 8
<p>Comparison of results from Landsat-8 OLI and FY-2E/F VISSR of the region L4 on 30 January 2014: (<b>a</b>) the false color composite of Landsat-8 OLI Band 6, 5, and 3 at 124/Row 29; (<b>b</b>) the 0.05° FSC map from Landsat-8 OLI; (<b>c</b>) the FY-2E VISSR FSC map at 02:00 UTC; and (<b>d</b>) the FY-2F VISSR FSC map at 02:30 UTC.</p> Full article ">Figure 9
<p>Comparison of results from Landsat-8 OLI and FY-2E/F VISSR of the region L5 on 10 January 2014: (<b>a</b>) the false color composite of Landsat-8 OLI Band 6, 5, and 3 at 144/Row 30; (<b>b</b>) the 0.05° FSC map from Landsat-8 OLI; (<b>c</b>) the FY-2E VISSR FSC map at 05:00 UTC; and (<b>d</b>) the FY-2F VISSR FSC map at 05:30 UTC.</p> Full article ">
2837 KiB  
Article
Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF) Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra Fractional Snow Cover Products
by Xiaoduo Pan, Xin Li, Guodong Cheng, Rensheng Chen and Kuolin Hsu
Remote Sens. 2017, 9(8), 774; https://doi.org/10.3390/rs9080774 - 29 Jul 2017
Cited by 10 | Viewed by 6353
Abstract
Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including [...] Read more.
Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including snowfall, snow water equivalent and snow depth along with fractional snow cover (FSC) data extracted from Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra to evaluate the effects of climate change on snow over the Heihe River Basin (HRB), a typical inland river basin in arid northwestern China from 2000 to 2013. We utilized Empirical Orthogonal Function (EOF) analysis and Mann-Kendall/Theil-Sen trend analysis to evaluate the results. The results are as follows: (1) FSC, snow water equivalent, and snow depth across the entire HRB region decreased, especially at elevations over 4500 m; however, snowfall increased at mid-altitude ranges in the upstream area of the HRB. (2) Total snowfall also increased in the upstream area of the HRB; however, the number of snowfall days decreased. Therefore, the number of extreme snow events in the upstream area of the HRB may have increased. (3) Snowfall over the downstream area of the HRB decreased. Thus, ground stations, WRF simulations and remote sensing products can be used to effectively explore the effect of climate change on snow at the watershed scale. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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<p>Study region and observation sites. The blue dots represent the locations of the China Meteorological Administration (CMA) stations. The numbers 1–16 represent the following station IDs: 52267, 52378, 52436, 52446, 52533, 52546, 52576, 52633, 52645, 52652, 52657, 52661, 52674, 52737, 52754, and 52765, respectively.</p> Full article ">Figure 2
<p>Comparison of daily snowfall between the weather research and forecast model (WRF) simulation and observational data (<b>a</b>) at the Dabenying site; (<b>b</b>) at CMA stations.</p> Full article ">Figure 3
<p>Spatial distribution of annual mean values from 2000 to 2013: (<b>a</b>) precipitation; (<b>b</b>) 2m temperature; (<b>c</b>) snowfall; (<b>d</b>) snow water equivalent (SWE); (<b>e</b>) snow depth; (<b>f</b>) fractional snow cover (FSC).</p> Full article ">Figure 4
<p>Altitudinal profiles of annual mean values from 2000 to 2013: (<b>a</b>) precipitation; (<b>b</b>) 2m temperature; (<b>c</b>) snowfall; (<b>d</b>) SWE; (<b>e</b>) snow depth; (<b>f</b>) FSC.</p> Full article ">Figure 5
<p>Annual mean values across the Heihe River Basin (HRB) and its sub-basins from 2000 to 2013: (<b>a</b>) precipitation; (<b>b</b>) 2m temperature; (<b>c</b>) snowfall; (<b>d</b>) SWE; (<b>e</b>) snow depth; (<b>f</b>) FSC.</p> Full article ">Figure 6
<p>Annual mean values by altitude from 2000 to 2013: (<b>a</b>) precipitation; (<b>b</b>) 2m temperature; (<b>c</b>) snowfall; (<b>d</b>) SWE; (<b>e</b>) snow depth; (<b>f</b>) FSC.</p> Full article ">Figure 7
<p>Mean annual number of snow days from 2000 to 2013.</p> Full article ">Figure 8
<p>Annual precipitation, snowfall and 2m temperature from CMA station observation data from 1960 to 2010. Station IDs from (<b>a</b>) to (<b>p</b>) are 52267, 52378, 52436, 52446, 52533, 52546, 52576, 52633, 52645, 52652, 52657, 52661, 52674, 52737, 52754, and 52765, respectively.</p> Full article ">Figure 9
<p>The first Empirical Orthogonal Function (EOF) spatial patterns (<b>a1</b>–<b>a6</b>) and the first principal component (PC) time series (<b>b1</b>–<b>b6</b>) from 2000 to 2013 for (<b>a1</b>,<b>b1</b>) precipitation; (<b>a2</b>,<b>b2</b>) 2m temperature; (<b>a3</b>,<b>b3</b>) snowfall; (<b>a4</b>,<b>b4</b>) SWE; (<b>a5</b>,<b>b5</b>) snow depth; (<b>a6</b>,<b>b6</b>) FSC.</p> Full article ">
2976 KiB  
Article
Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery
by Daniel Kępski, Bartłomiej Luks, Krzysztof Migała, Tomasz Wawrzyniak, Sebastian Westermann and Bronisław Wojtuń
Remote Sens. 2017, 9(7), 733; https://doi.org/10.3390/rs9070733 - 15 Jul 2017
Cited by 25 | Viewed by 9023
Abstract
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation [...] Read more.
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of the study area. (<b>a</b>) Overview with denoted extent of the camera view and the polygon used as the classification mask. (<b>b</b>) Zoom to the classification area with superimposed vegetation map. The detailed characteristics of specific plant communities are described in Wojtuń et al. 2013 [<a href="#B42-remotesensing-09-00733" class="html-bibr">42</a>].</p> Full article ">Figure 2
<p>Time-lapse camera set: (<b>a</b>) Internal view; (<b>b</b>) Placement near the Fugleberget summit; (<b>c</b>) View from the camera (16 June 2016) with marked location of all the Ground Control Points (GCP) used in the orthorectification process.</p> Full article ">Figure 3
<p>Workflow for the images obtained from the time-lapse camera on the example of picture from 4 May 2016: (<b>a</b>) raw photo from the camera; (<b>b</b>) picture after the orthorectification process; (<b>c</b>) picture superimposed on reference scene. Marked characteristic terrain features recognized on both pictures by SURF algorithm (“ptsOriginal”—features on the reference scene; “ptsDistorted”—the same features identified on the input image (4 May 2016)) and used in alignment process compensating the camera movements; (<b>d</b>) picture embedded in geographical space (Landsat 8 scene from 28 April 2016 in the background) with marked GCPs used in georeferencing process; (<b>e</b>) Results of pixel classification into snow/snow-free surfaces using threshold value in blue band, clipped to the study area; (<b>f</b>) processed picture with superimposed the land cover map used in spatial analysis.</p> Full article ">Figure 4
<p>Classification example for 5 June 2016. (<b>a</b>) Orthoimage zoomed to classification area; (<b>b</b>) Classification result with threshold value in blue band set to 140; (<b>c</b>) Frequency histogram of RGB values with characteristic bimodal distribution—note that the distance between modal values is largest for the blue band.</p> Full article ">Figure 5
<p>Results of weekly (<b>a</b>) snow depth and (<b>b</b>) snow water equivalent manual measurements in the Fuglebekken catchment in 2013/14, 2014/15 and 2015/16 in boxplot format [<a href="#B68-remotesensing-09-00733" class="html-bibr">68</a>].</p> Full article ">Figure 6
<p>Evolution of SCE in Fuglebekken catchment obtained from the time-lapse camera images in spring (<b>a</b>) 2014, (<b>b</b>) 2015 and (<b>c</b>) 2016; each blue point represents a classified image, daily meteorological data are shown in the background.</p> Full article ">Figure 7
<p>Snow cover distribution in the Fuglebekken catchment based on the 2014–2016 data set in specific melting stages: early (~72% snow coverage), advanced (~33%) and late (~4%). (<b>a</b>) Results of SCE superimposition for three years. (<b>b</b>) Surface percentage of snow coverage during the three stages.</p> Full article ">Figure 8
<p>Relationship between date of snowmelt on manual soundings sites and SWE measured there during the maximum accumulation time that occurred: (<b>a</b>) 28 April in 2014, (<b>b</b>) 20 April in 2015 and (<b>c</b>) 2 April in 2016. Red horizontal line indicate the date of advanced stage occurrence (~33% SCE), blue line is a linear regression.</p> Full article ">Figure 9
<p>Evolution of snow coverage on various types of land cover in Fuglebekken catchment at approximately 5 day intervals in spring: (<b>a</b>) 2014, (<b>b</b>) 2015 and (<b>c</b>) 2016. Blue bars represent SCE in the whole study area. (<b>d</b>) The share of individual land cover types in the study area. Two tundra vegetation types covering the lowest percentage of study area were omitted in spatial analysis: ornitocoprophilus (O) tundra (0.5%) and geophytic initial (GI) tundra (0.2%).</p> Full article ">
8025 KiB  
Article
Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring
by Hiroyuki Tsutsui and Takashi Maeda
Remote Sens. 2017, 9(6), 523; https://doi.org/10.3390/rs9060523 - 25 May 2017
Cited by 7 | Viewed by 6024
Abstract
Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave [...] Read more.
Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave remote sensing, very few studies have estimated the seasonal GrIS snow depth using this technique. In this study, to estimate seasonal snowpack on GrIS, we investigated the microwave property and optimum physical parameters. We used our microwave radiative transfer model to create a lookup table and a simple satellite retrieval algorithm to estimate seasonal snow depth on GrIS in spring, based on the microwave satellite brightness temperature from AMSR-E and AMSR2. Our research suggests there is potential for estimating snow depth based solely on GrIS passive microwave remote sensing data. We validated these estimates against in situ snow depths at several sites and compared them with the snow spatial distributions over the entire GrIS of several major products (ERA-interim, MAR ver. 5.3.1 and GLDAS-CLM) that evaluate snow depth. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Figure 1

Figure 1
<p>Locations of field observation sites.</p> Full article ">Figure 2
<p>Observed snow stratigraphy and snow temperature in snow pits at the QH3 site on 30 July 2011. The symbols show the snow classification for various layers after Fierz et al. [<a href="#B43-remotesensing-09-00523" class="html-bibr">43</a>], as indicated in the figure legend (Aoki et al. [<a href="#B45-remotesensing-09-00523" class="html-bibr">45</a>]).</p> Full article ">Figure 3
<p>Comparison of the cost of (<b>a</b>) the three-snow-layer models (Cost 3 layer): Average of 1st layer 0.01, 0.03, 0.05, 0.08, and 0.10 m) and (<b>b</b>) the two-snow-layer models (Cost 2 layer), associated with variation in emissivity from the ice sheet at the QH3 site. The vertical axis shows cost. The horizontal axis gives the assumed emissivity from the ice sheet (0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, and 1.00). Snow structure reflects the six cases described in the text, having upper layer (Z1)/bottom layer (Z2) values of 0.5/0.5 m, 0.6/0.4 m, 0.7/0.3 m, 0.8/0.2 m, 0.9/0.1 m and 0.95/0.05 m). Other values were set, as follows: snow particle radius (upper layer: R1 = 0.85 mm; and bottom layer: R2 = 2.95 mm), snow density (upper layer: 0.10 g/cm<sup>3</sup>; and bottom layer: 0.25 g/cm<sup>3</sup>), and snow water content (SWC = 3 vol %).</p> Full article ">Figure 4
<p>Comparison of costs for various surface conditions on the ice sheet over the wide satellite footprint covering the QH3 site. The vertical axis shows cost. The horizontal axis shows the assumed emissivity from the ice sheet (0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, and 1.00), while variables of the Advanced Integral Equation Model (AIEM), kσ (normalized surface root mean square height) ranges from 0.2 to 1.0 in 0.2 intervals, and kL (correlation length) ranges from 1.2 to 2.0 in 0.2 intervals. This yields the 25 cases of the AIEM plotted here for each emissivity. Snow structure reflects the six cases defined in the text, having upper/bottom layer thicknesses of 0.5/0.5 m, 0.6/0.4 m, 0.7/0.3 m, 0.8/0.2 m, 0.9/0.1 m, and 0.95/0.05 m. Snow particle radius (upper layer: R1 = 0.85 mm and bottom layer: R2 = 2.95 mm), snow density (upper layer: 0.10 g/cm<sup>3</sup> and bottom layer: 0.25 g/cm<sup>3</sup>), and snow water content (SWC = 3 vol %) had fixed values.</p> Full article ">Figure 5
<p>Comparison of costs for various values of snow density, snow particle radius, and snow water content over the wide satellite footprint covering the QH3 site: (<b>a</b>) snow density, (<b>b</b>) snow particle radius for the upper layer (R1), (<b>c</b>) snow particle radius for the bottom layer (R2), and (<b>d</b>) snow water content (SWC).</p> Full article ">Figure 6
<p>Variation in penetration depth associated with snow water content in snowpack in the range from 3 vol % to 8 vol %, while other parameters have optimum values (emissivity from ice sheet is 0.95, snow layer thickness is 0.95 mm in the upper layer and 0.05 m in the bottom layer, snow particle radius is 0.85 mm in the upper layer and 2.95 mm in the bottom layer, snow density is 0.10 g/cm<sup>3</sup> in the upper layer and 0.25 g/cm<sup>3</sup> in the bottom layer.</p> Full article ">Figure 7
<p>Flow path of the simple snow retrieval algorithm used for estimating snow depth on the Greenland Ice Sheet (GrIS). LUT = lookup table; AMSR-E = Advanced microwave scanning radiometer aboard the NASA Aqua satellite; AMSR2 = Advanced microwave scanning radiometer aboard the GCOM-W1 satellite.</p> Full article ">Figure 8
<p>Comparison between estimated snow depth and in situ snow depth at 19 sites for an emissivity value of 0.95 for the Greenland ice sheet.</p> Full article ">Figure 9
<p>Comparison between the estimated snow depth and the in situ snow depth for various emissivity values (0.80, 085, 0.90, 0.95, and 1.00) at the 19 sites to assess the reliability of the optimum emissivity value of 0.95 for the Greenland ice sheet.</p> Full article ">Figure 10
<p>Daily spatial resolution on 30 July 2011 for (<b>a</b>) the daily surface snow water equivalent (SWE; m) from ERA-interim, with 0.75° spatial resolution; (<b>b</b>) the daily surface accumulation of snow (kg/m<sup>2</sup>) from GLDAS-CLM, with 1° spatial resolution; (<b>c</b>) the daily surface mass balance (SMB; mm WE/day; WE = water equivalent) from MAR ver. 5.3.1 based on NCEPv1; (<b>d</b>) daily snowpack height above ice (m) from MAR ver. 5.3.1 based on NCEPv1, with 0.2° spatial resolution; (<b>e</b>) the monthly surface mass balance (SMB; mm WE/month) from MAR ver. 5.3.1, as a reference; and (<b>f</b>) daily estimated snow depth (cm) from this study, with 0.25° spatial resolution.</p> Full article ">Figure 11
<p>Daily spatial resolution on 11 June 2014 for (<b>a</b>) the daily surface snow water equivalent (SWE; m) from ERA-interim, with 0.75° spatial resolution; (<b>b</b>) the daily surface accumulation of snow (kg/m<sup>2</sup>) from GLDAS-CLM, with 1° spatial resolution; (<b>c</b>) the daily surface mass balance (SMB; mm WE/day; WE = water equivalent) from MAR ver. 5.3.1 based on NCEPv1; (<b>d</b>) daily snowpack height above ice (m) from MAR ver. 5.3.1 based on NCEPv1, with 0.2° spatial resolution; (<b>e</b>) the monthly surface mass balance (SMB; mm WE/month) from MAR ver. 5.3.1, as a reference; and (<b>f</b>) daily estimated snow depth (cm) from this study, with 0.25° spatial resolution.</p> Full article ">Figure 12
<p>Daily spatial resolution on 21 May 2014 for (<b>a</b>) the daily surface snow water equivalent (SWE; m) from ERA-interim, with 0.75° spatial resolution; (<b>b</b>) the daily surface accumulation of snow (kg/m<sup>2</sup>) from GLDAS-CLM, with 1° spatial resolution; (<b>c</b>) the daily surface mass balance (SMB; mm WE/day; WE = water equivalent) from MAR ver. 5.3.1 based on NCEPv1; (<b>d</b>) daily snowpack height above ice (m) from MAR ver. 5.3.1 based on NCEPv1, with 0.2° spatial resolution; (<b>e</b>) the monthly surface mass balance (SMB; mm WE/month) from MAR ver. 5.3.1, as a reference; and (<b>f</b>) daily estimated snow depth (cm) from this study, with 0.25° spatial resolution.</p> Full article ">Figure 13
<p>Elevation (m) from the elevation product of the National Oceanic and Atmospheric Administration (NOAA).</p> Full article ">Figure 14
<p>Output of (<b>a</b>) the daily surface snow water equivalent (SWE; m) from ERA-interim, (<b>b</b>) the daily surface accumulation of snow (kg/m<sup>2</sup>) from GLDAS-CLM, (<b>c</b>) the daily surface mass balance (SMB, mm WE/day; WE = water equivalent) from MAR ver. 5.3.1 based on NCEPv1, (<b>d</b>) snowpack height above ice (m) from MAR, and (<b>e</b>) estimated snow depth (cm) from this study at 19 in situ observation sites.</p> Full article ">Figure 15
<p>Standardized anomaly (SA) index for various in situ snow depth products (maximum, mean, and minimum values), (<b>a</b>) SWE in ERA-interim, (<b>b</b>) snow accumulation in GLDAS-CLM, (<b>c</b>) SMB in MAR ver. 5.3.1, (<b>d</b>) snow height above ice in MAR ver. 5.3.1; and (<b>e</b>) estimated snow depth in this study.</p> Full article ">
7031 KiB  
Article
Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China
by Jinlong Gao, Xiaodong Huang, Xiaofang Ma, Qisheng Feng, Tiangang Liang and Hongjie Xie
Remote Sens. 2017, 9(5), 475; https://doi.org/10.3390/rs9050475 - 12 May 2017
Cited by 20 | Viewed by 5824
Abstract
It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model [...] Read more.
It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 °C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot). Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Graphical abstract

Graphical abstract
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<p>The topography of the Qinghai Province and locations of meteorological stations.</p> Full article ">Figure 2
<p>Flow chart of the snow disaster early warning model.</p> Full article ">Figure 3
<p>The cloud-free snow cover product on 15 February 2008 (<b>A</b>) and on 25 February 2015 (<b>B</b>) in Qinghai Province.</p> Full article ">Figure 4
<p>The spatial distributions of risk factors: slope (<b>A</b>); snow-covered days (<b>B</b>); mean annual temperature (<b>C</b>); maximum snow depth (<b>D</b>); and per capita GDP (<b>E</b>); as well as the administrative divisions in Qinghai Province (<b>F</b>).</p> Full article ">Figure 5
<p>Potential snow disaster risk at 500 m pixel scale resolution in Qinghai (white spaces are non-grassland areas and slopes &gt;50°).</p> Full article ">Figure 6
<p>Back propagation artificial neural network (BP-ANN) simulated versus actual livestock mortalities: (<b>a</b>) simulation based on the 23 training samples; (<b>b</b>) test simulation of the established model using the five testing samples; (<b>c</b>) validation simulation of the established model using the five validation samples and (<b>d</b>) simulation based on all 33 samples.</p> Full article ">Figure 7
<p>The simulation results of a snow disaster in mid-February 2008 in the winter and spring pastures in Qinghai Province (white spaces are non-grassland ages, slope &gt;50°, and summer grazing grassland).</p> Full article ">Figure 8
<p>The simulation results of a snow disaster in late February 2015 in the winter and spring pastures in Qinghai Province (white spaces are non-grassland areas, slope &gt;50°, and summer grazing grassland).</p> Full article ">Figure 9
<p>The Landsat 7 TM false color image of a snow disaster in mid-February 2008 (<b>A</b>) and the Landsat 8 OLI false color image of a snow disaster in late February 2015 (<b>B</b>), and the administrative divisions in Qinghai Province (<b>C</b>).</p> Full article ">
5574 KiB  
Article
Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2
by Eunsang Cho, Samuel E. Tuttle and Jennifer M. Jacobs
Remote Sens. 2017, 9(5), 465; https://doi.org/10.3390/rs9050465 - 10 May 2017
Cited by 20 | Viewed by 6576
Abstract
For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals [...] Read more.
For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Graphical abstract

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Full article ">Figure 1
<p>Overview map of the study region in the North Central U.S. with 1176 watersheds outlined and overlain snow cover classification.</p> Full article ">Figure 2
<p>Date for which data are available by sensor and time series of weekly maximum snow water equivalent (SWE) from the Special Sensor Microwave/Imager (SSM/I) (blue), the Special Sensor Microwave Imager Sounder (SSMIS) (cyan), the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) (black), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (red), according to snow classifications.</p> Full article ">Figure 3
<p>Yearly biases (bars) and R<sup>2</sup> (points) of AMSR-E and AMSR2 with SSMIS according to three snow classification.</p> Full article ">Figure 4
<p>Boxplots of SSM/I and SSMIS SWE for different bins of AMSR-E SWE in three snow classes.</p> Full article ">Figure 5
<p>Boxplots of AMSR-E and AMSR2 SWE for different bins of AMSR-E SWE in three snow classes.</p> Full article ">Figure 6
<p>(<b>a</b>) Bias maps between AMSR-E (2007 to 2011) and (<b>b</b>) AMSR2 (2012 to 2016) SWE relative to SSMIS SWE in winter season (1 November–30 April).</p> Full article ">Figure 7
<p>(<b>a</b>) Normalized bias maps between AMSR-E (2007 to 2011) and (<b>b</b>) AMSR2 (2012 to 2016) SWE relative to SSMIS SWE in winter season (1 November–30 April).</p> Full article ">
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