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Remote Sensing of Climate Change and Water Resources
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Remote Sensing of Climate Change and Water Resources

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 (30 June 2017) | Viewed by 143416

Special Issue Editors

Dr. Qiusheng Wu

E-Mail Website1 Website2
Guest Editor
Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
Interests: remote sensing; GIS; wetland hydrology; LiDAR; google earth engine
Special Issues, Collections and Topics in MDPI journals
Dr. Charles Lane

E-Mail Website
Guest Editor
Office of Research and Development, U.S. Environmental Protection Agency, 26 W. Martin Luther King Dr. MS 581, Cincinnati, OH 45268, USA
Interests: wetland ecology; systems ecology; landscape ecology; watershed management
Dr. Melanie Vanderhoof

E-Mail Website
Guest Editor
Research Geographer, U.S. Geological Survey, Geosciences and Environmental Change Science Center, DFC, MS980, Denver, CO 80225, USA
Interests: high-resolution and moderate-resolution remote sensing; wetlands; fire; image processing and analysis; object-based remote sensing; multi-source remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Chunqiao Song

E-Mail Website
Guest Editor
Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
Interests: lake hydrology and water resources; climate change; remote sensing of mountain glacier; limnology and wetland ecosystem; global change impacts on the third pole

Special Issue Information

Dear Colleagues,

Earth’s climate is changing, and multiple lines of evidence suggest significant warming in both the atmosphere and the oceans. The global surface temperature is increasing, the global sea level is rising, the ice is melting, and changes in the pattern of precipitation are bringing intense rainfall and floods to some areas and devastating droughts to others. As the human and financial costs of extreme weather rise, we must understand why global climate is changing and work hard to mitigate its worst impacts. One key challenge facing the scientific community is to combine a variety of data sources to better understand the global hydrosphere, its processes and interactions with the atmosphere, cryosphere, biosphere and lithosphere, and some aspects that may change.

Satellite remote sensing and associated airborne and in situ measurements have been crucial for advancing our understanding of the global climate system dynamics and its impacts. Since the 1960s, a wide array of active and passive satellite sensors have been launched and operated by various government and private agencies. These satellite sensors capture data of the planet Earth routinely in different parts of the electromagnetic spectrum with various spatial, temporal, and spectral resolutions. Widespread applications of remotely sensed data have led to dramatic improvements in technologies and methodologies for better monitoring the states and processes of the coupled atmosphere-land-ocean systems at various spatiotemporal scales.

This Special Issue aims to invite contributions from studies that focus on understanding how climate change may impact water resources and evaluating its impacts and threats using remote sensing observations from multi-scale platforms, e.g., in situ, airborne and various satellite platforms. Contributions that demonstrate the development of new models, techniques, data products and/or highlight the challenges of remote sensing in climate change studies are also encouraged. The range of topics includes, but is not limited to:

  • climate change
  • wetland ecosystems
  • water resources
  • lake water dynamics
  • sea-level change
  • ice and snow cover
  • cryosphere
  • soil moisture and precipitation
  • droughts effects
  • floods

Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.

Dr. Qiusheng Wu
Dr. Charles Lane
Dr. Melanie Vanderhoof
Dr. Chunqiao Song
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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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13 pages, 3293 KiB  
Article
Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States
by Yaping Xu, Lei Wang, Kenton W. Ross, Cuiling Liu and Kimberly Berry
Remote Sens. 2018, 10(2), 301; https://doi.org/10.3390/rs10020301 - 15 Feb 2018
Cited by 72 | Viewed by 13102
Abstract
Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The [...] Read more.
Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Mosaic of the three consecutive standardized soil moisture index (SSI) maps from 1 to 3 April 2015. Areas in yellow to red represent areas that are experiencing very dry conditions, indicating drought. (<b>b</b>) SSI map for the whole month of April 2015.</p> Full article ">Figure 2
<p>(<b>a</b>) Palmer drought severity index (PDSI) for April 2015. Areas in yellow and red represent areas that are experiencing dry conditions; (<b>b</b>) Normalized difference water index (NDWI) calculated for 01 to 03 April 2015. Likewise, areas in yellow and red represent areas that are experiencing low vegetation water content and therefore a dry condition.</p> Full article ">Figure 3
<p>(<b>a</b>) Scatter plot for April 2015. The correlation between SSI and PDSI is moderate (r = 0.52); (<b>b</b>) Scatter plot for 1 to 3 April 2015. The correlation between SSI and NDWI is strong (r = 0.56).</p> Full article ">Figure 4
<p>Scatter plot between SCAN values and SMAP values for the four anomaly stations with the R-squared values: Uapb-Earle station in Arkansas (for the year 2015), R-squared value was 0.1506; N Piedmont Arec station in Virginia (for 2016), R-square value was 0.2499; the Sellers Lake #1 station in Florida (for 2015), R-square value was 0.2827; and the Princeton #1 station in Kentucky (for 2015), R-square value was 0.3115.</p> Full article ">Figure 5
<p>SMAP and SCAN time plot comparison to identify the anomaly: (<b>a</b>) Uapb-Earle station in Arkansas (2085); (<b>b</b>) Sellers Lake #1 station in Florida (2012); (<b>c</b>) Princeton #1 station in Kentucky (2005); and (<b>d</b>) N Piedmont Arec station in Virginia (2039).</p> Full article ">Figure 5 Cont.
<p>SMAP and SCAN time plot comparison to identify the anomaly: (<b>a</b>) Uapb-Earle station in Arkansas (2085); (<b>b</b>) Sellers Lake #1 station in Florida (2012); (<b>c</b>) Princeton #1 station in Kentucky (2005); and (<b>d</b>) N Piedmont Arec station in Virginia (2039).</p> Full article ">
6364 KiB  
Article
Long-Term Water Storage Changes of Lake Volta from GRACE and Satellite Altimetry and Connections with Regional Climate
by Shengnan Ni, Jianli Chen, Clark R. Wilson and Xiaogong Hu
Remote Sens. 2017, 9(8), 842; https://doi.org/10.3390/rs9080842 - 14 Aug 2017
Cited by 29 | Viewed by 9210
Abstract
Satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) provides a quantitative measure of terrestrial water storage (TWS) change at different temporal and spatial scales. In this study, we investigate the ability of GRACE to quantitatively monitor long-term hydrological characteristics over [...] Read more.
Satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) provides a quantitative measure of terrestrial water storage (TWS) change at different temporal and spatial scales. In this study, we investigate the ability of GRACE to quantitatively monitor long-term hydrological characteristics over the Lake Volta region. Principal component analysis (PCA) is employed to study temporal and spatial variability of long-term TWS changes. Long-term Lake Volta water storage change appears to be the dominant long-term TWS change signal in the Volta basin. GRACE-derived TWS changes and precipitation variations compiled by the Global Precipitation Climatology Centre (GPCC) are related both temporally and spatially, but spatial leakage attenuates the magnitude of GRACE estimates, especially at small regional scales. Using constrained forward modeling, we successfully remove leakage error in GRACE estimates. After this leakage correction, GRACE-derived Lake Volta water storage changes agree remarkably well with independent estimates from satellite altimetry at interannual and longer time scales. This demonstrates the value of GRACE estimates to monitor and quantify water storage changes in lakes, especially in relatively small regions with complicated topography. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The map of Volta River basin in West Africa. Original map adapted from <a href="http://www.zef.de/publ_maps.html" target="_blank">http://www.zef.de/publ_maps.html</a>.</p> Full article ">Figure 2
<p>Total water storage changes from the Gravity Recovery and Climate Experiment (GRACE) over the Volta River basin as outlined in <a href="#remotesensing-09-00842-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>Principal component analysis (PCA)-derived spatial and temporal patterns of terrestrial water storage (TWS) variability (with annual and semiannual signals removed) over the Volta River basin. (<b>a</b>,<b>b</b>) are spatial patterns of the first two modes derived from PCA; (<b>c</b>,<b>d</b>) are corresponding temporal patterns. The percentages of the total variance explained by the first two principal components are 30.9% and 20.7%, respectively.</p> Full article ">Figure 4
<p>GRACE water storage changes and satellite altimetry water level changes for Lake Volta. Both time series in (<b>a</b>,<b>b</b>) have an increasing (2007–2010) and declining (2011–2015) rate; (<b>c</b>) is the comparison between GRACE and satellite altimetry at long-term time scale. We have removed the annual and semi-annual signals using least squares fitting. Please notice the different <span class="html-italic">y</span>-axis scales used in (<b>c</b>).</p> Full article ">Figure 5
<p>Global Precipitation Climatology Centre (GPCC) monthly precipitation over the Volta River basin with the climatologic average removed. The climatological precipitation is calculated by averaging the monthly precipitation of all the same months over a certain period (e.g., the 20-year period from January 1996 to December 2015). The red line is the nonseasonal precipitation anomaly smoothed with a Butterworth low-pass (below 0.5 cpy) filter.</p> Full article ">Figure 6
<p>GRACE TWS long-term change rates and GPCC mean precipitation anomalies over the Volta River basin during the periods of 2007–2010 and 2011–2015. (<b>a</b>) is TWS long-term change rates from 2007 to 2010 after P4M6 decorrelation filtering and 300 km Gaussian smoothing; (<b>b</b>) is mean precipitation anomalies from 2007 to 2010 without any smoothing filter; (<b>c</b>) is TWS long-term change rates from 2011 to 2015 after P4M6 decorrelation filtering and 300 km Gaussian smoothing; (<b>d</b>) is mean precipitation anomalies from 2011 to 2015 without any smoothing filter. Mean precipitation anomalies are the average values of precipitation with a certain period (e.g., 20 years) climatology removed.</p> Full article ">Figure 7
<p>Mass rates (January 2007–December 2010) in cm/year of equivalent water height. (<b>a</b>) Apparent long-term TWS change rates from GRACE after P4M6 decorrelation filtering and 300 km Gaussian smoothing; (<b>b</b>) Restored “true” long-term TWS change rates from constrained forward modeling after 300 iterations; (<b>c</b>) Predicted TWS change rates from model rates of (<b>b</b>); (<b>d</b>) Difference between observed and modeled apparent mass rates (i.e., (<b>a</b>–<b>c</b>)). Please notice the different color scale used in the four panels.</p> Full article ">Figure 8
<p>Mass rates (January 2011–December 2015) in cm/year of equivalent water height. (<b>a</b>) Apparent long-term TWS change rates from GRACE after P4M6 decorrelation filtering and 300 km Gaussian smoothing; (<b>b</b>) Restored “true” long-term TWS change rates from constrained forward modeling after 200 iterations; (<b>c</b>) Predicted TWS change rates from model rates of (<b>b</b>); (<b>d</b>) Difference between observed and modeled apparent mass rates (i.e., (<b>a</b>–<b>c</b>)). Please notice the different color scale used in the four panels.</p> Full article ">Figure 9
<p>Residuals between observed and apparent mass rates in constrained forward modeling. The residual is computed as the root mean square (RMS) value of difference between observed and modeled data at each grid point over the entire rectangle region shown in <a href="#remotesensing-09-00842-f007" class="html-fig">Figure 7</a>.</p> Full article ">Figure 10
<p>Residuals between observed and apparent mass rates in <a href="#remotesensing-09-00842-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 11
<p>GRACE water storage changes (equivalent water volume) with leakage correction and satellite altimetry water volume changes for Lake Volta. The red curve can be obtained by multiplying the red curve in <a href="#remotesensing-09-00842-f004" class="html-fig">Figure 4</a>c with both scale factor (~41.3) and the area of lake mask. The blue curve is the product of altimetry water level change (blue curve in <a href="#remotesensing-09-00842-f004" class="html-fig">Figure 4</a>c) with estimated lake area. Please notice that the <span class="html-italic">y</span>-axis scale in this figure is the same for GRACE and satellite altimetry data.</p> Full article ">
3651 KiB  
Article
100 Years of Competition between Reduction in Channel Capacity and Streamflow during Floods in the Guadalquivir River (Southern Spain)
by Patricio Bohorquez and José David Del Moral-Erencia
Remote Sens. 2017, 9(7), 727; https://doi.org/10.3390/rs9070727 - 14 Jul 2017
Cited by 20 | Viewed by 6016
Abstract
Reduction in channel capacity can trigger an increase in flood hazard over time. It represents a geomorphic driver that competes against its hydrologic counterpart where streamflow decreases. We show that this situation arose in the Guadalquivir River (Southern Spain) after impoundment. We identify [...] Read more.
Reduction in channel capacity can trigger an increase in flood hazard over time. It represents a geomorphic driver that competes against its hydrologic counterpart where streamflow decreases. We show that this situation arose in the Guadalquivir River (Southern Spain) after impoundment. We identify the physical parameters that raised flood hazard in the period 1997–2013 with respect to past years 1910–1996 and quantify their effects by accounting for temporal trends in both streamflow and channel capacity. First, we collect historical hydrological data to lengthen records of extreme flooding events since 1910. Next, inundated areas and grade lines across a 70 km stretch of up to 2 km wide floodplain are delimited from Landsat and TerraSAR-X satellite images of the most recent floods (2009–2013). Flooded areas are also computed using standard two-dimensional Saint-Venant equations. Simulated stages are verified locally and across the whole domain with collected hydrological data and satellite images, respectively. The thoughtful analysis of flooding and geomorphic dynamics over multi-decadal timescales illustrates that non-stationary channel adaptation to river impoundment decreased channel capacity and increased flood hazard. Previous to channel squeezing and pre-vegetation encroachment, river discharges as high as 1450 m3·s?1 (the year 1924) were required to inundate the same areas as the 790 m3·s?1 streamflow for recent floods (the year 2010). We conclude that future projections of one-in-a-century river floods need to include geomorphic drivers as they compete with the reduction of peak discharges under the current climate change scenario. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study area along the Guadalquivir River including the inundated area (light blue) on 24 February 2010 for <span class="html-italic">Q</span> = 1950 m<sup>3</sup>·s<sup>−1</sup> at Marmolejo dam (flow from right to left). Urban areas at risk and hydropower stations are highlighted in red and yellow, respectively. Aerial photography shows a small portion of flooded areas in Llanos del Sotillo. Our earlier work [<a href="#B1-remotesensing-09-00727" class="html-bibr">1</a>] presents a detailed analysis of the flood dynamics in the confined valley of 6 km length downstream of Marmolejo dam.</p> Full article ">Figure 2
<p>Hydrograph showing river discharges at: Zocueca (1); Encinarejo (2); Mengíbar (3); and Marmolejo (4) dams in February 2010.</p> Full article ">Figure 3
<p>Longitudinal profile of maximum water levels in the numerical flood simulation (solid blue line) on 24 February 2010 with 2000 m<sup>3</sup>·s<sup>−1</sup> streamflow in Marmolejo dam. Flow from left to right.</p> Full article ">Figure 4
<p>(<b>a</b>) Flooded area derived based on Lansat 5 data and corrected with flood photographs on 24 February 2010; (<b>b</b>) Numerical flood simulation superposed on Lansat wet perimeter. Colours indicate altitudes of water surface. Flow from right to left. The inundated town at the bottom is Villanueva de Reina. Stage indicators correspond with <a href="#remotesensing-09-00727-f003" class="html-fig">Figure 3</a>; (<b>c</b>) Photograph of the overtopped Valtodano dam (see location in panel <b>b</b>); (<b>d</b>) An example of botanical HWM-PSI. Person 1.85 m tall provides scale.</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) Flooded area derived based on Lansat 5 data and corrected with flood photographs on 24 February 2010; (<b>b</b>) Numerical flood simulation superposed on Lansat wet perimeter. Colours indicate altitudes of water surface. Flow from right to left. The inundated town at the bottom is Villanueva de Reina. Stage indicators correspond with <a href="#remotesensing-09-00727-f003" class="html-fig">Figure 3</a>; (<b>c</b>) Photograph of the overtopped Valtodano dam (see location in panel <b>b</b>); (<b>d</b>) An example of botanical HWM-PSI. Person 1.85 m tall provides scale.</p> Full article ">Figure 5
<p>(<b>a</b>) Rating curves at stream gauges (650 m downstream of Mengíbar dam); and (<b>b</b>) flooded area downstream of Mengíbar dam on 8 December 2010 with the local river discharge <span class="html-italic">Q</span> = 650 m<sup>3</sup>·s<sup>−1</sup> (flow from top to bottom). Gauge stations located on the second bridge.</p> Full article ">Figure 6
<p>(<b>a</b>) Simulated and observed inundation extent, together with slackwater deposits; (<b>b</b>) map of simulated velocity magnitude; and (<b>c</b>) contours of modelled water surface elevation.</p> Full article ">Figure 7
<p>Contours of water elevation from the numerical flood simulation (transparent colour) and inundation perimeter derived based on helicopter flood photograph (black line) on 24 February 2010 at the outlet confined valley crossing Marmolejo dam (red dot). Flow from right to the bottom.</p> Full article ">Figure 8
<p>Measured and simulated rating curves at stream gauges: (<b>a</b>) reservoir and foot of Marmolejo dam; and (<b>b</b>) Roman Bridge of Andújar city. Panel (<b>a</b>) includes a sensitivity study of the model results downstream of Marmolejo dam with variations in the Manning’s roughness coefficient.</p> Full article ">Figure 9
<p>Numerical flood simulation (coloured transparent contour) and flooded area derived based on Landsat/TerraSAR-X/helicopter data (solid line in black) on 24 February 2010. The location of Marmolejo dam and the stream gauge near Andújar city is also given. Flow from right to left.</p> Full article ">Figure 10
<p>(<b>a</b>) Flood frequency distribution and relative magnitude of the Guadalquivir River floods at Marmolejo dam (after [<a href="#B1-remotesensing-09-00727" class="html-bibr">1</a>]); and (<b>b</b>) flood frequency analysis of streamflow records using PeakFQ [<a href="#B43-remotesensing-09-00727" class="html-bibr">43</a>]. Estimates of flood magnitudes for several recurrence intervals (1–200 years) are also given.</p> Full article ">
800 KiB  
Article
Identification of Hazard and Risk for Glacial Lakes in the Nepal Himalaya Using Satellite Imagery from 2000–2015
by David R. Rounce, C. Scott Watson and Daene C. McKinney
Remote Sens. 2017, 9(7), 654; https://doi.org/10.3390/rs9070654 - 26 Jun 2017
Cited by 111 | Viewed by 15085
Abstract
Glacial lakes in the Nepal Himalaya can threaten downstream communities and have large socio-economic consequences if an outburst flood occurs. This study identified 131 glacial lakes in Nepal in 2015 that are greater than 0.1 km2 and performed a first-pass hazard and [...] Read more.
Glacial lakes in the Nepal Himalaya can threaten downstream communities and have large socio-economic consequences if an outburst flood occurs. This study identified 131 glacial lakes in Nepal in 2015 that are greater than 0.1 km2 and performed a first-pass hazard and risk assessment for each lake. The hazard assessment included mass entering the lake, the moraine stability, and how lake expansion will alter the lake’s hazard in the next 15–30 years. A geometric flood model was used to quantify potential hydropower systems, buildings, agricultural land, and bridges that could be affected by a glacial lake outburst flood. The hazard and downstream impacts were combined to classify the risk associated with each lake. 11 lakes were classified as very high risk and 31 as high risk. The potential flood volume was also estimated and used to prioritize the glacial lakes that are the highest risk, which included Phoksundo Tal, Tsho Rolpa, Chamlang North Tsho, Chamlang South Tsho, and Lumding Tsho. These results are intended to assist stakeholders and decision makers in making well-informed decisions with respect to the glacial lakes that should be the focus of future field studies, modeling efforts, and risk-mitigation actions. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study area showing glacial lakes and glaciers in Nepal along with major basins and subbasins adapted from ICIMOD (2011) [<a href="#B24-remotesensing-09-00654" class="html-bibr">24</a>].</p> Full article ">Figure 2
<p>Hazard classification flow chart for dynamic and self-destructive failures and the GLOF risk chart as a function of hazard and downstream impacts.</p> Full article ">Figure 3
<p>Distribution of lake size according to lake type and the distance to glacier for all lakes in Nepal.</p> Full article ">Figure 4
<p>Breakdown of hazard parameters that threaten the glacial lakes.</p> Full article ">Figure 5
<p>Distribution of risk, hazard, and downstream impacts classifications (pie charts) for each basin. The map shows all lakes classified as very high or high risk with hatched lines showing a sub-basin that has at least one lake classified as a very high risk.</p> Full article ">Figure 6
<p>Summary of downstream impacts showing the number of buildings, agriculture (km<sup>2</sup>), hydropower systems, and bridges threatened by GLOFs.</p> Full article ">Figure 7
<p>Distribution of potential flood volume (PFV) for all lakes.</p> Full article ">
8844 KiB  
Article
Land Cover, Land Use, and Climate Change Impacts on Endemic Cichlid Habitats in Northern Tanzania
by Margaret Kalacska, J. Pablo Arroyo-Mora, Oliver Lucanus and Mary A. Kishe-Machumu
Remote Sens. 2017, 9(6), 623; https://doi.org/10.3390/rs9060623 - 17 Jun 2017
Cited by 18 | Viewed by 12195
Abstract
Freshwater ecosystems are among the most threatened on Earth, facing environmental and anthropogenic pressures often surpassing their terrestrial counterparts. Land use and land cover change (LUCC) such as degradation and fragmentation of the terrestrial landscape negatively impacts aquatic ecosystems. Satellite imagery allows for [...] Read more.
Freshwater ecosystems are among the most threatened on Earth, facing environmental and anthropogenic pressures often surpassing their terrestrial counterparts. Land use and land cover change (LUCC) such as degradation and fragmentation of the terrestrial landscape negatively impacts aquatic ecosystems. Satellite imagery allows for an impartial assessment of the past to determine habitat alterations. It can also be used as a forecasting tool in the development of species conservation strategies through models based on ecological factors extracted from imagery. In this study, we analyze Landsat time sequences (1984–2015) to quantify LUCC around three freshwater ecosystems with endemic cichlids in Tanzania. In addition, we examine population growth, agricultural expansion, and climate change as stressors that impact the habitats. We found that the natural vegetation cover surrounding Lake Chala decreased from 15.5% (1984) to 3.5% (2015). At Chemka Springs, we observed a decrease from 7.4% to 3.5% over the same period. While Lake Natron had minimal LUCC, severe climate change impacts have been forecasted for the region. Subsurface water data from the Gravity Recovery and Climate Experiment (GRACE) satellite observations further show a decrease in water resources for the study areas, which could be exacerbated by increased need from a growing population and an increase in agricultural land use. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map of the study areas in Northern Tanzania. Blue circles represent the locations of the study areas at Lake Natron Springs, Chemka Springs, and Lake Chala. Other important points of references such as Mt. Kilimanjaro, Mt. Meru, Lake Manyara, the town of Bomang’ombe, Nyumba ya Mungu Dam and the city of Arusha are also illustrated.</p> Full article ">Figure 2
<p>Photographs of the fishes and their habitats. (<b>A</b>) Lake Natron Springs with flamingoes and Lake Natron in the background; (<b>B</b>) Shallow <span class="html-italic">Alcolapia</span> habitat on the Eastern side of Lake Natron; (<b>C</b>) Spawning pair of <span class="html-italic">A. alcalicus</span>; (<b>D</b>) Male <span class="html-italic">A. latilibris</span> scraping algae off substrate; (<b>E</b>) Sparring male <span class="html-italic">A. ndalalani</span>; (<b>F</b>) Chemka Springs; (<b>G</b>) Clear water of Chemka Springs; (<b>H</b>) <span class="html-italic">Ctenochromis</span> sp. with <span class="html-italic">Garra</span> sp. grazing algae; (<b>I</b>) Courting male <span class="html-italic">Ctenochromis</span> sp.; (<b>J</b>) Female <span class="html-italic">Ctenochromis</span> sp.; (<b>K</b>) Lake Chala; (<b>L</b>) Introduced <span class="html-italic">Coptodon rendalli</span>; (<b>M</b>) Male <span class="html-italic">Haplochromis</span> sp.; (<b>N</b>) <span class="html-italic">Haplochromis</span> sp. in the clear water of Lake Chala.</p> Full article ">Figure 3
<p>Natural land cover classes surrounding the freshwater habitats. (<b>A</b>) Lake Natron Springs: grass and trona; (<b>B</b>) Chemka Springs: closed canopy Riparian forest (<b>C</b>) Lake Chala: bushland.</p> Full article ">Figure 4
<p>Dates of Landsat TM, ETM+, and OLI used in the analysis.</p> Full article ">Figure 5
<p>Example of a false color composite (FCC), a tasseled cap transform (TC) and 31 year cloud mask (black) for the three study areas. (<b>A</b>) Lake Natron FCC; (<b>B</b>) Lake Natron TC; (<b>C</b>) Lake Natron cloud mask; (<b>D</b>) Chemka Springs FCC; (<b>E</b>) Chemka Springs TC; (<b>F</b>) Chemka Springs cloud mask; (<b>G</b>) Lake Chala FCC; (<b>H</b>) Lake Chala TC; (<b>I</b>) Lake Chala cloud mask.</p> Full article ">Figure 6
<p>Example of the tasseled cap time sequence for the Lake Chala study area. The differences between the natural land cover class (bushland) and modified classes of agriculture and mixed land use are illustrated.</p> Full article ">Figure 7
<p>Extent of the natural land cover classes (i.e., bushland at Lake Chala and closed canopy forest at Chemka Springs) surrounding the freshwater habitats for Lake Chala and Chemka Springs. (<b>A</b>) Lake Chala, 1987; (<b>B</b>) Lake Chala, 1995; (<b>C</b>) Lake Chala, 2000; (<b>D</b>) Lake Chala, 2015; (<b>E</b>) Chemka Springs, 1995; (<b>F</b>) Chemka Springs, 2015. For Lake Chala, the border between Tanzania and Kenya is shown in purple.</p> Full article ">Figure 8
<p>Natural vegetation patch size distributions for Lake Chala and Chemka Springs.</p> Full article ">Figure 9
<p>GRACE satellite derived time series of water equivalent height (cm) for the mascon encompassing (<b>A</b>) Lake Natron and; (<b>B</b>) Chemka Springs and Lake Chala; (<b>C</b>) Mascon grid cells representing water equivalent height from March 2015.</p> Full article ">Figure 10
<p>Changes in cropland extent and agriculture (rainfed and irrigated) for the (<b>A</b>) Natron and (<b>B</b>) Chemka/Chala mascons. Mean population density for the two mascons is also shown. The area covered by the mascons is illustrated in <a href="#remotesensing-09-00623-f009" class="html-fig">Figure 9</a>.</p> Full article ">
2437 KiB  
Article
Quantifying Streamflow Variations in Ungauged Lake Basins by Integrating Remote Sensing and Water Balance Modelling: A Case Study of the Erdos Larus relictus National Nature Reserve, China
by Kang Liang
Remote Sens. 2017, 9(6), 588; https://doi.org/10.3390/rs9060588 - 10 Jun 2017
Cited by 12 | Viewed by 5548
Abstract
Hydrological predictions in ungauged lakes are one of the most important issues in hydrological sciences. The habitat of the Relict Gull (Larus relictus) in the Erdos Larus relictus National Nature Reserve (ELRNNR) has been seriously endangered by lake shrinkage, yet the hydrological processes [...] Read more.
Hydrological predictions in ungauged lakes are one of the most important issues in hydrological sciences. The habitat of the Relict Gull (Larus relictus) in the Erdos Larus relictus National Nature Reserve (ELRNNR) has been seriously endangered by lake shrinkage, yet the hydrological processes in the catchment are poorly understood due to the lack of in-situ observations. Therefore, it is necessary to assess the variation in lake streamflow and its drivers. In this study, we employed the remote sensing technique and empirical equation to quantify the time series of lake water budgets, and integrated a water balance model and climate elasticity method to further examine ELRNNR basin streamflow variations from1974 to 2013. The results show that lake variations went through three phases with significant differences: The rapidly expanding sub-period (1974–1979), the relatively stable sub-period (1980–1999), and the dramatically shrinking sub-period (2000–2013). Both climate variation (expressed by precipitation and evapotranspiration) and human activities were quantified as drivers of streamflow variation, and the driving forces in the three phases had different contributions. As human activities gradually intensified, the contributions of human disturbances on streamflow variation obviously increased, accounting for 22.3% during 1980–1999 and up to 59.2% during 2000–2013. Intensified human interferences and climate warming have jointly led to the lake shrinkage since 1999. This study provides a useful reference to quantify lake streamflow and its drivers in ungauged basins. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Figure 1
<p>Location of the Erdos <span class="html-italic">Larus relictus</span> National Nature Reserve (ELRNNR).</p> Full article ">Figure 2
<p>Example of the remote sensing extracting water area of Bojiang Lake based on <span class="html-italic">MNDWI</span>. The date of used images in Figure a and b are 28/September/1995 and 28/August/2013, respectively. (<b>a-1</b>) and (<b>b-1</b>) show the false color composite images of Bojiang Lake by <span class="html-italic">SWIR2, SWIR1</span>, and <span class="html-italic">Green</span> bands. (<b>a-2</b>) and (<b>b-2</b>) show the calculated results of <span class="html-italic">MNDWI</span>. (<b>a-3</b>) and (<b>b-3</b>) show the lake water surface with the <span class="html-italic">MNDWI</span> threshold value of 0.35 (the black regions).</p> Full article ">Figure 3
<p>Variations of (<b>a</b>) average annual lake area, (<b>b</b>) annual anomaly and accumulative anomaly, (<b>c</b>) average annual lake volume, and (<b>d</b>) anomaly and accumulative anomaly of volume in the Erdos <span class="html-italic">Larus relictus</span> National Nature Reserve (ELRNNR).</p> Full article ">Figure 4
<p>Variations in the spatial distribution range of Bojiang Lake during different typical periods.</p> Full article ">Figure 5
<p>Variations of (<b>a</b>) annual average lake streamflow, (<b>b</b>) annual anomaly and accumulative anomaly of streamflow, (<b>c</b>) annual average precipitation, (<b>d</b>) annual anomaly and accumulative anomaly of precipitation, (<b>e</b>) annual average potential evapotranspiration, (<b>f</b>) annual anomaly and accumulative anomaly of potential evapotranspiration in the Bojiang Lake basin.</p> Full article ">
26144 KiB  
Article
Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data
by Haifeng Tian, Wang Li, Mingquan Wu, Ni Huang, Guodong Li, Xiang Li and Zheng Niu
Remote Sens. 2017, 9(6), 521; https://doi.org/10.3390/rs9060521 - 24 May 2017
Cited by 58 | Viewed by 10234
Abstract
Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. [...] Read more.
Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. To address this problem, we propose a novel method to monitor these changes using Sentinel-1A data. First, the Sentinel-1A water index (SWI) was built using a linear model and a stepwise multiple regression analysis method with Sentinel-1A and Landsat-8 imagery acquired on the same day. Second, water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016 were extracted by the threshold method utilizing time-series SWI data with an interval of 12 days. The results showed that the SWI threshold classification method could be applied to different regions during different periods with high quantity accuracy (approximately 99%). The water surface areas ranged between 1726.73 km2 and 3729.19 km2 during the study periods, indicating an extreme variability in the short term. The maximum and average values of the changed areas were 875.57 km2 (with a change rate of 35%) and 197.58 km2 (with a change rate of 8.2%), respectively, after 12 days. The changes in the mid-western region of Poyang Lake were more dramatic. These results provide baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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<p>Location of Poyang Lake: (<b>a</b>) location of Jiangxi Province, China; (<b>b</b>) location of Poyang Lake in Jiangxi Province; and (<b>c</b>) Poyang Lake and its surrounding environment.</p> Full article ">Figure 2
<p>Frequency histogram of SWI imagery on 27 September 2016. The dashed line represents water and the unbroken line represents non-water.</p> Full article ">Figure 3
<p>Spatiotemporal distribution of surface water in Poyang Lake based on the SWI from 24 May 2015 to 14 November 2016. The interval of the time-series was 12 days and there were 38 periods in total; however, five periods (4 August 2015; 8 November 2015; 23 June 2016; 29 July 2016; and 3 September 2016) were missing due to a lack of Sentinel-1A data. The yellow color represents non-water and the blue color represents water.</p> Full article ">Figure 4
<p>The area of Poyang Lake from 24 May 2015 to 14 November 2016, with intervals of 12 days.</p> Full article ">Figure 5
<p>The examined SWI classification accuracy based on OLI: (<b>a</b>) OLI true color (R band 4, G band 3, B band 2) imagery acquired on 27 September 2016 located in Poyang Lake; (<b>b</b>) classification results based on Sentinel-1A imagery acquired on 27 September 2016; (<b>c</b>) SWI classification accuracy based on OLI on 27 September 2016; (<b>d</b>) OLI true color imagery acquired on 9 September 2015 located in Poyang Lake; (<b>e</b>) classification results based on Sentinel-1A imagery acquired on 9 September 2015; (<b>f</b>) SWI classification accuracy based on OLI on 9 September 2015; (<b>g</b>) OLI true color imagery acquired on 2 February 2016 located in Taihu Lake, China; (<b>h</b>) classification results based on Sentinel-1A imagery acquired on 2 February 2016; and (<b>i</b>) SWI classification accuracy based on OLI on 2 February 2016.</p> Full article ">Figure 6
<p>Submergence ratios in Poyang Lake from 24 May 2015 to 14 November 2016. The red line shows the boundary of the sub-region. Poyang Lake was divided into Regions I–IV based on the submergence ratio to describe the changed characteristics in different regions.</p> Full article ">Figure 7
<p>The number of lakes with different areas that constitute the Poyang Lake. The black line represents the lakes with an area of 0.1–1 km<sup>2</sup>, the red line represents the lakes with an area of &gt;1–10 km<sup>2</sup>, and the blue line represents the lake with area of &gt;10–100 km<sup>2</sup>. The background color is used to distinguish the rainy season and dry season: the light green color represents the rainy season, and the light yellow color represents the dry season.</p> Full article ">Figure 8
<p>Water surface area extracted from SWI and OLI from 24 May 2015 to 14 November 2016.</p> Full article ">Figure 9
<p>MODIS classification accuracy based on OLI.</p> Full article ">Figure 10
<p>The surface features in OLI of the commission and omission of water extracted from SWI images: (<b>a</b>) and (<b>b</b>) OLI true color (R band 4, G band 3, B band 2) imagery acquired on 9 September 2015.</p> Full article ">Figure 11
<p>The spectral characteristic of misclassified zones and normal vegetation and water regarded as references in OLI on 9 September 2015.</p> Full article ">
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Article
Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations
by Shusen Wang, Fuqun Zhou and Hazen A. J. Russell
Remote Sens. 2017, 9(3), 256; https://doi.org/10.3390/rs9030256 - 10 Mar 2017
Cited by 30 | Viewed by 7142
Abstract
Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest [...] Read more.
Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate Experiment (GRACE) satellite observations. Compared with the observed Qpeak at a downstream hydrometric station, the model results have a correlation coefficient of 0.83 (p < 0.001) and a mean absolute error of 6.5% of the mean observed value of 28,400 m3·s?1 for the 12 study years (2003–2014). The results are compared with those for other basins to examine the difference in the major factors controlling the Qpeak. It was found that the temperature variations in the snowmelt season are the principal driver for the Qpeak in the Mackenzie River. In contrast, the variations in snow accumulation play a more important role in the Qpeak for warmer southern basins in Canada. The study provides a GRACE-based approach for basin-scale snow mass estimation, which is largely independent of in situ observations and eliminates the limitations and uncertainties with traditional snow measurements. Snow mass estimated from the GRACE data was about 20% higher than that from the Global Land Data Assimilation System (GLDAS) datasets. The model is relatively simple and only needs GRACE and temperature data for flood forecasting. It can be readily applied to other cold region basins, and could be particularly useful for regions with minimal data. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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<p>Map for the the Mackenzie River Basin (MRB) and the hydrometric station for river flow measurement (<b>A</b>), and its location in Canada’s landmass (<b>B</b>). The Red River Basin (RRB) is also shown on the map of Canada.</p> Full article ">Figure 2
<p>Flowchart for the modelling processes.</p> Full article ">Figure 3
<p>Diagram illustrating snow accumulation (S) and non-snow water (W) discharge for the Mackenzie River Basin in snow cover season. <span class="html-italic">t</span><sub>0</sub> and <span class="html-italic">t<sub>b</sub></span>: start and breakup time of the snow season; <span class="html-italic">TWS</span><sub>0</sub> and <span class="html-italic">TWS<sub>b</sub></span>: basin total water storage (S + W) at time <span class="html-italic">t</span><sub>0</sub> and <span class="html-italic">t<sub>b</sub></span>; <span class="html-italic">W<sub>n-s</sub></span>: non-snow water at time <span class="html-italic">t<sub>b</sub></span>; <span class="html-italic">S<sub>b</sub></span>: Snow Water Equivalent at <span class="html-italic">t<sub>b</sub></span>; <span class="html-italic">Q<sub>sum</sub></span>: accumulated basin water discharge in the snow season.</p> Full article ">Figure 4
<p>Hydroclimate characterization for the Mackenzie River Basin during the study period of 2003–2014. (<b>A</b>) Daily air temperature; (<b>B</b>) Accumulated precipitation during a year (blue represents snow; orange represents rain); (<b>C</b>) Total water storage change from the Gravity Recovery and Climate Experiment (GRACE) satellite observations; and (<b>D</b>) Daily river flow measured at the hydrometric station. More details on the data sources can be found in <a href="#sec3dot2-remotesensing-09-00256" class="html-sec">Section 3.2</a>. Red lines in all panels represent the mean values over the study period of 2003–2014. Shadowed areas represent the maximum and minimum variation ranges within the study years.</p> Full article ">Figure 5
<p>Comparison of model results for the Mackenzie River Basin. (<b>A</b>): modeled vs. observed total baseflow in winter; (<b>B</b>): modeled vs. observed daily peak surface runoff; (<b>C</b>): modeled vs. observed daily peak river flow.</p> Full article ">Figure 6
<p>Comparison of the Mackenzie River Basin (<b>A</b>) vs. the Red River Basin (<b>B</b>) for the relationships between peak surface runoff (<span class="html-italic">Q<sub>runoff</sub></span>) and snow water equivalent at spring break-up (<span class="html-italic">S<sub>b</sub></span>).</p> Full article ">Figure 7
<p>The time difference for the study period of available GRACE data between the dates for modeled peak snowmelt and observed peak river flow at the hydrometric station. The average is a 22 day offset.</p> Full article ">Figure 8
<p>The leave-one-out cross-validation (LOO-CV) results for peak river flow forecasts. The error bar is the difference between forecasted and observed peak river flows for each year.</p> Full article ">
12125 KiB  
Article
Seasonal Change in Wetland Coherence as an Aid to Wetland Monitoring
by Brian Brisco, Frank Ahern, Kevin Murnaghan, Lori White, Francis Canisus and Philip Lancaster
Remote Sens. 2017, 9(2), 158; https://doi.org/10.3390/rs9020158 - 15 Feb 2017
Cited by 59 | Viewed by 8782
Abstract
Water is an essential natural resource, and information about surface water conditions can support a wide variety of applications, including urban planning, agronomy, hydrology, electrical power generation, disaster relief, ecology and preservation of natural areas. Synthetic Aperture Radar (SAR) is recognized as an [...] Read more.
Water is an essential natural resource, and information about surface water conditions can support a wide variety of applications, including urban planning, agronomy, hydrology, electrical power generation, disaster relief, ecology and preservation of natural areas. Synthetic Aperture Radar (SAR) is recognized as an important source of data for monitoring surface water, especially under inclement weather conditions, and is used operationally for flood mapping applications. The canopy penetration capability of the microwaves also allows for mapping of flooded vegetation as a result of enhanced backscatter from what is generally believed to be a double-bounce scattering mechanism between the water and emergent vegetation. Recent investigations have shown that, under certain conditions, the SAR response signal from flooded vegetation may remain coherent during repeat satellite over-passes, which can be exploited for interferometric SAR (InSAR) measurements to estimate changes in water levels and water topography. InSAR results also suggest that coherence change detection (CCD) might be applied to wetland monitoring applications. This study examines wetland vegetation characteristics that lead to coherence in RADARSAT-2 InSAR data of an area in eastern Canada with many small wetlands, and determines the annual variation in the coherence of these wetlands using multi-temporal radar data. The results for a three-year period demonstrate that most swamps and marshes maintain coherence throughout the ice-/snow-free time period for the 24-day repeat cycle of RADARSAT-2. However, open water areas without emergent aquatic vegetation generally do not have suitable coherence for CCD or InSAR water level estimation. We have found that wetlands with tree cover exhibit the highest coherence and the least variance; wetlands with herbaceous cover exhibit high coherence, but also high variability of coherence; and wetlands with shrub cover exhibit high coherence, but variability intermediate between treed and herbaceous wetlands. From this knowledge, we have developed a novel image product that combines information about the magnitude of coherence and its variability with radar brightness (backscatter intensity). This product clearly displays the multitude of small wetlands over a wide area. With an interpretation key we have also developed, it is possible to distinguish different wetland types and assess year-to-year changes. In the next few years, satellite SAR systems, such as the European Sentinel and the Canadian RADARSAT Constellation Mission (RCM), will provide rapid revisit capabilities and standard data collection modes, enhancing the operational application of SAR data for assessing wetland conditions and monitoring water levels using InSAR techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Full article ">Figure 1
<p>(<b>a</b>) Study area, outlined in yellow, at the southern edge of the upper Ottawa Valley, Ontario. The yellow frame marks the location of the larger-scale map. (<b>b</b>) Enlarged view of the study area. Study wetlands are shown in orange, with wetlands with quantitative water level monitoring numbered in blue.</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Study area, outlined in yellow, at the southern edge of the upper Ottawa Valley, Ontario. The yellow frame marks the location of the larger-scale map. (<b>b</b>) Enlarged view of the study area. Study wetlands are shown in orange, with wetlands with quantitative water level monitoring numbered in blue.</p> Full article ">Figure 2
<p>Coherence as a function of time for marsh, swamp and open wetland types.</p> Full article ">Figure 3
<p>Coherence as a function of time for four structural classes.</p> Full article ">Figure 4
<p>Coherence as a function of time for four canopy closure classes.</p> Full article ">Figure 5
<p>Coherence as a function of time for three canopy health classes.</p> Full article ">Figure 6
<p>Wetlands Lake Clear West-4 and Lake Clear East-1 coherence profile for 2010/2011 showing abrupt changes in coherence due to sudden changes in water level.</p> Full article ">Figure 7
<p>Annual average coherence and standard deviation of 60 wetlands over three-year ice-off period (total of 180 points): (<b>a</b>) sorted by wetland type; (<b>b</b>) sorted by wetland structure.</p> Full article ">Figure 8
<p>Example of image coherence products for the Lake Clear test site: (<b>a</b>) 2011 data; (<b>b</b>) 2012 data; (<b>c</b>) 2013 data. Wetlands of note are numbered as indicated in <a href="#sec2dot2-remotesensing-09-00158" class="html-sec">Section 2.2</a>.</p> Full article ">Figure 9
<p>Standard deviation of annual coherence versus average annual coherence of the wetland polygons in the Lake Clear study site.</p> Full article ">
6656 KiB  
Article
Spatial-Temporal Characteristics and Climatic Responses of Water Level Fluctuations of Global Major Lakes from 2002 to 2010
by Chao Tan, Mingguo Ma and Honghai Kuang
Remote Sens. 2017, 9(2), 150; https://doi.org/10.3390/rs9020150 - 13 Feb 2017
Cited by 41 | Viewed by 7364
Abstract
As one of the most important geographical units affected by global climate change, lakes are sensitive to climatic changes and are considered “indicators” of climate and the environment. In this study, changes in the spatial-temporal characteristics of the water levels of 204 global [...] Read more.
As one of the most important geographical units affected by global climate change, lakes are sensitive to climatic changes and are considered “indicators” of climate and the environment. In this study, changes in the spatial-temporal characteristics of the water levels of 204 global major lakes are systematically analyzed using satellite altimetry data (Hydroweb product) from 2002 to 2010. Additionally, the responses of the major global lake levels to climatic fluctuations are analyzed using Global Land Surface Assimilation System (GLDAS) data (temperature and precipitation). The results show that the change rates of most global lakes exceed 0, which means that the lake levels of these lakes are rising. The change rates of the lake levels are between ?0.3~0.3 m/a, which indicates that the rate of change in the water-level of most lakes is not obvious. A few lakes have a particularly sharp change rate, between ?5.84~?2 m/a or 0.7~1.87 m/a. Lakes with increasing levels are mainly located in the mountain and plateau regions, and the change rates in the coastal highlands are more evident. The global temperatures rise by a change rate of 0.0058 °C/a, while the global precipitation decreases by a change rate of ?0.6697 mm/a. However, there are significant regional differences in both temperature and precipitation. In addition, the impact of precipitation on the water level of lakes is significant and straightforward, while the impact of temperature is more complex. A study of lake levels on a global scale would be quite useful for a better understanding of the impact which climate change has on surface water resources. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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<p>Classification of the change rates of lake levels.</p> Full article ">Figure 2
<p>The change rate of global temperature (<b>A</b>) and precipitation (<b>B</b>) from 2002 to 2010.</p> Full article ">Figure 3
<p>The trends in global temperature and precipitation.</p> Full article ">Figure 4
<p>Accumulated precipitation and trend changes of the lake level in the watershed data.</p> Full article ">
5209 KiB  
Article
Remote Sensing of Glacier Change in the Central Qinghai-Tibet Plateau and the Relationship with Changing Climate
by Linghong Ke, Xiaoli Ding, Wenkai Li and Bo Qiu
Remote Sens. 2017, 9(2), 114; https://doi.org/10.3390/rs9020114 - 29 Jan 2017
Cited by 36 | Viewed by 7898
Abstract
The widely distributed glaciers over the Qinghai-Tibet Plateau (QTP) represent important freshwater reserves and the meltwater feeds many major rivers of Asia. Glacier change over the QTP has shown high temporal and spatial variability in recent decades, and the driving forces of the [...] Read more.
The widely distributed glaciers over the Qinghai-Tibet Plateau (QTP) represent important freshwater reserves and the meltwater feeds many major rivers of Asia. Glacier change over the QTP has shown high temporal and spatial variability in recent decades, and the driving forces of the variability are not yet clear. This study examines the area and thickness change of glaciers in the Dongkemadi (DKMD) region over central QTP by exploring all available Landsat images from 1976 to 2013 and satellite altimetry data over 2003–2008, and then analyzes the relationships between glacier variation and local and macroscale climate factors based on various remote sensing and re-analysis data. Results show that the variation of glacier area over 1976–2013 is characterized by significant shrinkage at a linear rate of ?0.31 ± 0.04 km2·year?1. Glacier retreat slightly accelerated in the 2000s, and the mean glacier surface elevation lowered at a rate of ?0.56 m·year?1 over 2003–2008. During the past 38 years, glacier change in the DKMD area was dominated by the variation of mean annual temperature, and was influenced by the state of the North Atlantic Oscillation (NAO). The mechanism linking climate variability over the central QTP and the state of NAO is most likely via changes in the strength of westerlies and Siberian High. We found no evidence supporting the role of summer monsoons (Indian summer monsoon and East Asian monsoon) in driving local climate and glacier changes. In addition, El Niño Southern Oscillation (ENSO) may be associated with the extreme weather (snow storm) in October 1986 and 2000 which might have led to significant glacier expansion in the following years. Further research is needed to better understand the physical mechanisms linking NAO, ENSO and climate variability over the mid-latitude central QTP. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Full article ">Figure 1
<p>The study glaciers in the Dongkemadi (DKMD) region and the ICESat tracks (blue dots) shown on the false color composite of Landsat TM images (Bands 5, 4, and 2, for R, G, and B, respectively) (<b>a</b>); and the location of the study area in relation to three important atmospheric circulations in Asia (<b>b</b>). Glacier outlines in (<b>a</b>) are retrieved from the background TM images acquired on 30 August 2000.</p> Full article ">Figure 2
<p>Glacier area sensitivity to the NDSI (Normalized Difference Snow Index) threshold for different years of analysis. Scenes with part cloud contamination as indicated in <a href="#remotesensing-09-00114-t001" class="html-table">Table 1</a> are not included in this evaluation.</p> Full article ">Figure 3
<p>The variation of glacier area (<b>a</b>) in relation to annual mean temperature (<b>b</b>); annual total precipitation (<b>c</b>); North Atlantic Oscillation (NAO) (<b>d</b>); winter Southern Oscillation Index (SOI) (<b>e</b>); and Indian Monsoon Index (IMI) (<b>f</b>), during 1976–2013. The statistics is based on hydrological year (October to September). The linear trend in (<b>a</b>) is based on all observations excluding 1986, and the multiplication mark denotes average glacier area in different periods grouped by different colored dots, with the error bar showing the standard deviation of the mean value. For annual mean temperature and precipitation, linear trends are respectively shown for the two periods of 1976–1991 and 1992–2013.</p> Full article ">Figure 4
<p>Variation of mean annual temperature, mean elevation differences between ICESat and SRTM over the glacier and off-glacier areas between 2003 and 2008 (modified based on [<a href="#B34-remotesensing-09-00114" class="html-bibr">34</a>]).</p> Full article ">Figure 5
<p>The variation in mean temperature (T) and total precipitation (P) in the winter season in relation to glacier area changes.</p> Full article ">Figure 6
<p>Correlation coefficients between NAO and 2 m air temperature on time scales of: cold season (<b>a</b>); warm season (<b>b</b>); and annual (<b>c</b>), based on ERA reanalysis data (1976–2013). The black cross filling denotes significance at a confidence level of 95%, and the red dots represent the location of the study region.</p> Full article ">Figure 7
<p>Composite differences in the mean 500 hPa geopotential height (the upper row) and wind speed field (the lower row) in the: cold season (<b>a</b>,<b>c</b>); and warm season (<b>b</b>,<b>d</b>) between the group of years with low NAO and high NAO. Refer to <a href="#remotesensing-09-00114-t004" class="html-table">Table 4</a> for specific years selected. The black cross filling in (<b>a</b>,<b>b</b>) denotes significance at a confidence level of 95%, and the ret dots represent the location of the study region.</p> Full article ">Figure 7 Cont.
<p>Composite differences in the mean 500 hPa geopotential height (the upper row) and wind speed field (the lower row) in the: cold season (<b>a</b>,<b>c</b>); and warm season (<b>b</b>,<b>d</b>) between the group of years with low NAO and high NAO. Refer to <a href="#remotesensing-09-00114-t004" class="html-table">Table 4</a> for specific years selected. The black cross filling in (<b>a</b>,<b>b</b>) denotes significance at a confidence level of 95%, and the ret dots represent the location of the study region.</p> Full article ">
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Article
Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA
by Melanie K. Vanderhoof, Hayley E. Distler, Di Ana Teresa G. Mendiola and Megan Lang
Remote Sens. 2017, 9(2), 105; https://doi.org/10.3390/rs9020105 - 25 Jan 2017
Cited by 23 | Viewed by 9455
Abstract
Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring [...] Read more.
Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (?7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Graphical abstract

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Full article ">Figure 1
<p>(<b>A</b>) Worldview-3 imagery in natural color, and (<b>B</b>) Radarsat-2 (R2) and Worldview-3 (WV3) image extents by date in relation to the watershed boundary as shown using a light detection and ranging (lidar) digital elevation model (DEM; 2 m resolution). Copyright 2017 Digital Globe, Next View License.</p> Full article ">Figure 2
<p>Depressions derived from the dry condition light detection and ranging (lidar) digital elevation model (DEM), average wetness condition lidar DEM, as well as the enhanced topographic wetness index (ETWI), derived from the dry condition lidar DEM. In the ETWI, areas more likely to be inundated are blue, areas more likely to be non-inundated are red. Extent is defined by the extent of the dry condition lidar DEM data collection.</p> Full article ">Figure 3
<p>Flowchart of Radarsat-2 processing steps. SLC: Single Look Complex. DEM: digital elevation model.</p> Full article ">Figure 4
<p>Flowchart of accuracy assessment. WV3: Worldview-3. ETWI: enhanced topographic wetness index.</p> Full article ">Figure 5
<p>The extent of the depressions as derived from a dry versus an average wetness condition lidar digital elevation model (DEM).</p> Full article ">Figure 6
<p>An example of variation in forested surface water extent (non-forested areas are masked out on map products) as mapped with the six Radarsat-2 (R2) dates, Worldview-3 (WV3) imagery, depressions derived from the average wetness condition lidar, and the combined model. The depression filter has been applied to the surface water maps. Radarsat-2 shows relatively poor outputs, with poorest quality for dates with recent precipitation. The combined model shows the most coherent map of surface water extent. ETWI: enhanced topographic wetness index. Copyright 2017 Digital Globe, Next View License.</p> Full article ">Figure 7
<p>The effect of filtering the Radarsat-2 (R2; top, southern location) and Worldview-3 (WV3; bottom, northern location) outputs by the lidar-derived depressions. Copyright 2017 Digital Globe, Next View License.</p> Full article ">Figure 8
<p>The per-pixel fraction surface water as estimated from Worldview-3 (WV-3) imagery (<b>left</b>) over the Upper Choptank Watershed and the final surface water extent, as estimated from Worldview-3 imagery (<b>right</b>). DEM: Digital elevation model.</p> Full article ">Figure 9
<p>A second example of mapping forested surface water in which both Radarsat-2 (R2) and Worldview-3 (WV3) showed error in the outputs, but where the combined model showed a more coherent map of forested surface water extent. ETWI: enhanced topographic wetness index. Copyright 2017 Digital Globe, Next View License.</p> Full article ">Figure 10
<p>The mean decrease in node impurity which is indicative of variable relevance within the random forest models, (<b>A</b>) Radarsat-2 (9 April), (<b>B</b>) Radarsat-2 and Worldview-3 and (<b>C</b>) Radarsat-2, Worldview-3 and enhanced topographic wetness index (ETWI). The elements, <span class="html-italic">k</span><sub>1</sub> and <span class="html-italic">k</span><sub>2</sub> are the absorption difference parallel and diagonal in relation to the coordinate system.</p> Full article ">
4523 KiB  
Article
The Effect of Algal Blooms on Carbon Emissions in Western Lake Erie: An Integration of Remote Sensing and Eddy Covariance Measurements
by Zutao Ouyang, Changliang Shao, Housen Chu, Richard Becker, Thomas Bridgeman, Carol A. Stepien, Ranjeet John and Jiquan Chen
Remote Sens. 2017, 9(1), 44; https://doi.org/10.3390/rs9010044 - 6 Jan 2017
Cited by 23 | Viewed by 9959
Abstract
Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO2 sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common phenomena in many eutrophic lakes and can cause many environmental stresses, yet [...] Read more.
Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO2 sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common phenomena in many eutrophic lakes and can cause many environmental stresses, yet their effects on the net exchange of CO2 (FCO2) at large spatial scales have not been adequately addressed. We integrated remote sensing and Eddy Covariance (EC) technologies to investigate the effects that algal blooms have on FCO2 in the western basin of Lake Erie—a large lake infamous for these blooms. Three years of long-term EC data (2012–2014) at two sites were analyzed. We found that at both sites: (1) daily FCO2 significantly correlated with daily temperature, light, and wind speed during the algal bloom periods; (2) monthly FCO2 was negatively correlated with chlorophyll-a concentration; and (3) the year with larger algal blooms was always associated with lower carbon emissions. We concluded that large algal blooms could reduce carbon emissions in the western basin of Lake Erie. However, considering the complexity of processes within large lakes, the weak relationship we found, and the potential uncertainties that remain in our estimations of FCO2 and chlorophyll-a, we argue that additional data and analyses are needed to validate our conclusion and examine the underlying regulatory mechanisms. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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<p>The study area: western Lake Erie. The two eddy-covariance (EC) towers are marked with a black triangle and a rectangle for the Light House (LightH) site and Crib site, respectively. The black circles represent the locations where water samples were taken for chlorophyll-a extraction at 10–14 day intervals. A MODIS true color image shows the highly turbid water (the top-right) in western Lake Erie. The Crib site is more turbid than the LightH site because it is closer to the mouth of the Maumee River and the shore.</p> Full article ">Figure 2
<p>The estimated chlorophyll-a concentration from MODIS was compared with in situ field sampling: (<b>a</b>) each MODIS chlorophyll-a value was extracted from the pixel centered at field sampling location and compared with the sampled chlorophyll-a at that location of the same day, (<b>b</b>) all samples at different locations but within a month were averaged and compared with the average MODIS chlorophyll-a value of the same month at pixels centered on these locations.</p> Full article ">Figure 3
<p>The monthly mean chlorophyll-a concentration estimated based on MODIS images for April–November of 2012–2014 in western Lake Erie.</p> Full article ">Figure 4
<p>The daily average <span class="html-italic">F</span><sub>CO2</sub> at the Crib (CB) (<b>a</b>) and LightH sites; (<b>b</b>) in 2012–2014; and the monthly average <span class="html-italic">F</span><sub>CO2</sub> at the Crib; (<b>c</b>) and at the LightH sites ;(<b>d</b>) in 2012–2014.</p> Full article ">Figure 5
<p>Changes in 30-min <span class="html-italic">F</span><sub>CO2</sub> with photosynthetically active radiation (PAR) (<b>a</b>) air temperature (Ta) (<b>b</b>), and wind speed (U) (<b>c</b>) during algal bloom days (≥10 µg/L) and non-algal bloom days (&lt;10 µg/L) and changes at the LightH site and the Crib site. There appeared no obvious linear/nonlinear relationships between <span class="html-italic">F</span><sub>CO2</sub> and the three meteorological variable during both day and night periods. Sixty-five and sixty-one algal bloom days were observed for the LightH and Crib sites, respectively.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>c</b>) Changes of daily <span class="html-italic">F</span><sub>CO2</sub> with photosynthetically active radiation (PAR), air temperature (Ta), and wind speed (U) at the LightH and Crib sites during algal bloom months and non-algal bloom months. Significant correlations were observed during algal bloom months (<span class="html-italic">p</span> &lt; 0.05) but not for non-bloom months (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 7
<p>Changes in monthly mean chlorophyll-a during the study period at the Crib (<b>a</b>) and LightH (<b>b</b>) sites and inter-annual variations in mean chlorophyll-a (April–November) at the Crib (<b>c</b>) and LightH (<b>d</b>) sites.</p> Full article ">Figure 8
<p>Changes in the relationship between monthly mean chlorophyll-a and <span class="html-italic">F<sub>CO2</sub></span> over different years (2012–2014) at the LightH (<b>top</b>) and Crib sites (<b>bottom</b>). The relationship was stronger in 2013, which experienced larger algal blooms than that in other years.</p> Full article ">
9623 KiB  
Article
A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature
by Yongmin Yang, Jianxiu Qiu, Hongbo Su, Qingmei Bai, Suhua Liu, Lu Li, Yilei Yu and Yaoxian Huang
Remote Sens. 2017, 9(1), 43; https://doi.org/10.3390/rs9010043 - 6 Jan 2017
Cited by 12 | Viewed by 6827
Abstract
The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST) is a key variable in energy balance process and remotely sensed LST [...] Read more.
The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST) is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero) poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML) to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H) to analytically estimate the radiometric-convective resistance (rae) via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD) of H and latent heat flux (LE) from OSML are 34.5 W/m2 and 46.5 W/m2 at SMACEX site and 50.1 W/m2 and 67.0 W/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System). Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation over heterogeneous areas. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>A sketch of the trapezoid in the vegetation fractional cover and LST (VFC/LST) space.</p> Full article ">Figure 2
<p>Flow chart of One-Source Model for Land (OSML) implementation, where diamond-shaped boxes represent OSML input from meteorological observations and remote sensing retrievals, while light-gray rectangles represent intermediate variables or parameters, and the dark-gray rectangles represent the main procedures for solving r<sub>ae</sub> and consequently estimating surface heat fluxes.</p> Full article ">Figure 3
<p>The distribution of flux towers in the Walnut Creek catchment and the land use classifications.</p> Full article ">Figure 4
<p>The distribution of flux towers and the land use classifications in the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over Zhangye oasis. The yellow rectangular in the left figure shows the kernel experimental area in MUSOEXE, and the subset figure in the lower right shows the location of MUSOEXE (marked in red triangle) in the Heihe River Basin and in China.</p> Full article ">Figure 5
<p>Validation of: net radiation R<sub>n</sub> (<b>a</b>); soil heat flux G (<b>b</b>); latent heat flux LE (<b>c</b>); and sensible heat flux H (<b>d</b>) during Landsat overpass on Day of Year (DOY) 174 and 182 of 2002.</p> Full article ">Figure 6
<p>The spatial distribution of LE estimated using OSML and SEBS from Landsat-based retrievals on DOY 174 and 182.</p> Full article ">Figure 7
<p>The spatial distribution of H estimated using OSML and SEBS from Landsat-based retrievals on DOY 174 and 182.</p> Full article ">Figure 8
<p>Comparison of OSML- and SEBS-derived LE on: DOY 174 (<b>a</b>); and DOY 182 (<b>c</b>); and comparison of OSML- and SEBS-derived H: on DOY 174 (<b>b</b>); and DOY 182 (<b>d</b>) (for cornfields and soybean fields only, color shading indicates pixel density).</p> Full article ">Figure 9
<p>Intercomparison between r<sub>ae_OSML</sub>, r<sub>ae_SEBS</sub> for: (<b>a</b>) cornfields; and (<b>b</b>) soybean fields on DOY 182 of 2002 (color shading indicates pixel density).</p> Full article ">Figure 10
<p>Evaluation of r<sub>ae_OSML</sub> and r<sub>ae_SEBS</sub> against r<sub>ae_obs</sub>.</p> Full article ">Figure 11
<p>Validation of four energy balance components from OSML using tower-based observations at satellite overpass on DOY 192, DOY 215 and DOY 231 of 2012.</p> Full article ">Figure 12
<p>The spatial distribution of LE over MUSOEXE site based on OSML and SEBS from satellite-based variables at satellite overpass time on DOY 192, DOY 215 and DOY 231 of 2012.</p> Full article ">Figure 13
<p>The spatial distribution of H over MUSOEXE site based on OSML and SEBS from satellite-based variables at satellite overpass time on DOY 192, DOY 215 and DOY 231 of 2012.</p> Full article ">Figure 14
<p>Comparison of OSML- and SEBS-derived LE on: DOY 192 (<b>a</b>); DOY 215 (<b>c</b>); and DOY 231 (<b>e</b>); and comparison of OSML- and SEBS-derived H on: DOY 192 (<b>b</b>); DOY 215 (<b>d</b>); and DOY 231 (<b>f</b>) (for the kernel experimental area in MUSOEXE, color shading indicates pixel density).</p> Full article ">
5264 KiB  
Article
Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies of the Aral Sea and Lake Mead
by Alka Singh, Florian Seitz, Annette Eicker and Andreas Güntner
Remote Sens. 2016, 8(11), 953; https://doi.org/10.3390/rs8110953 - 16 Nov 2016
Cited by 12 | Viewed by 7091
Abstract
The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent [...] Read more.
The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent gravitational field can be observed through the Gravity Recovery and Climate Experiment (GRACE) gravimetric satellite mission, provided that the mass variation is above the sensitivity of GRACE. This study evaluates mass changes in prominent reservoir regions through three independent approaches viz. fluxes, storages, and gravity, by combining remote sensing products, in-situ data and hydrological model outputs using WaterGAP Global Hydrological Model (WGHM) and Global Land Data Assimilation System (GLDAS). The results show that the dynamics revealed by the GRACE signal can be better explored by a hybrid method, which combines remote sensing-based reservoir volume estimates with hydrological model outputs, than by exclusive model-based storage estimates. For the given arid/semi-arid regions, GLDAS based storage estimations perform better than WGHM. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Figure 1
<p>Study box for the Lake Mead region and the Aral Sea region. River discharge: 1 = Colorado River inflow, 2 = Virgin River, 3 = Muddy River, 4 = Colorado River outflow, 5 = Syr Darya and 6 = Amu Darya.</p> Full article ">Figure 2
<p>Runoff: (<b>left</b>) Lake Mead and (<b>right</b>) The Aral Sea.</p> Full article ">Figure 3
<p>Precipitation: (<b>left</b>) The Lake Mead region and (<b>right</b>) The Aral Sea region.</p> Full article ">Figure 4
<p>ET: (<b>left</b>) The Lake Mead region and (<b>right</b>) The Aral Sea region.</p> Full article ">Figure 5
<p>Mean reduced reservoir volume: (<b>left</b>) Lake Mead and (<b>right</b>) The Aral Sea.</p> Full article ">Figure 6
<p>Mean reduced Snow Water Equivalent: (<b>left</b>) The Lake Mead region and (<b>right</b>) The Aral Sea region.</p> Full article ">Figure 7
<p>Mean reduced Soil Moisture: (<b>left</b>) The Lake Mead region and (<b>right</b>) The Aral Sea region.</p> Full article ">Figure 8
<p>Gravity Recovery and Climate Experiment (GRACE)-derived trend of the equivalent water height (meter/year) between 2003 and 2014. The size of the study area was chosen according to the mascon grid. (<b>left</b>) The Lake Mead region is 3° × 3° where Lake Mead is located at the center. (<b>right</b>) The Aral Sea region is 4° × 6°covering the entire lake and two mascon grid cells.</p> Full article ">Figure 9
<p>GRACE-derived mass variations with the uncertainty range of the measurements as provided by GRACE Tellus: (<b>left</b>) The Lake Mead region, (<b>right</b>) The Aral Sea region</p> Full article ">Figure 10
<p>Lake Mead: (<b>top</b>) Net surface runoff of the lake: inflow–outflow and (<b>bottom</b>) Reservoir volume variation compared with the hydrological fluxes.</p> Full article ">Figure 11
<p>Lake Mead region (3° × 3°) mass variations observed by net fluxes, net storages, and GRACE: (<b>top</b>) Monthly mass variations and (<b>bottom</b>) Non-seasonal water storage variability. All time series in the lower panel have been reduced for their mean (i.e., mean value over the study period). The large numbers at the top of the figure are the periods of different mass evolution, discussed in <a href="#sec4dot2-remotesensing-08-00953" class="html-sec">Section 4.2</a>. Here symbol δ indicates derivative and <math display="inline"> <semantics> <mrow> <mo>∫</mo> </mrow> </semantics> </math> indicates integral of the signal.</p> Full article ">Figure 12
<p>Aral Sea region (4° × 6°) mass variations observed by net flux, net storages, and GRACE: (<b>top</b>) Monthly mass variations and (<b>bottom</b>) Non-seasonal water storage variability. All time series in the lower panel have been reduced for their mean (i.e., mean value over the study period). The large numbers at the top of the figure are the periods of different mass evolution, discussed in <a href="#sec4dot3-remotesensing-08-00953" class="html-sec">Section 4.3</a>. Here symbol δ indicates derivative and <math display="inline"> <semantics> <mrow> <mo>∫</mo> </mrow> </semantics> </math> indicates integral of the signal.</p> Full article ">Figure 13
<p>Total water storage (TWS) observed by GRACE compared with the best estimates and the reservoir volume: (<b>left</b>) The Lake Mead region and (<b>right</b>) The Aral Sea region.</p> Full article ">

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2 pages, 2942 KiB  
Erratum
Erratum: Tan C., et al. Spatial–Temporal Characteristics and Climatic Responses of Water Level Fluctuations of Global Major Lakes from 2002 to 2010. Remote Sens. 2017, 9, 150
by Chao Tan, Mingguo Ma and Honghai Kuang
Remote Sens. 2018, 10(2), 174; https://doi.org/10.3390/rs10020174 - 26 Jan 2018
Viewed by 3070
Abstract
In the published paper [1], the authors found some spelling mistakes.[...] Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Figure 1
<p>Classification of the change rates of lake levels.</p> Full article ">Figure 4
<p>Accumulated precipitation and trend changes of the lake level in the watershed data.</p> Full article ">
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