Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations
"> Figure 1
<p>(<b>a</b>) Cloud free MODIS LST, (<b>b</b>) the merged MODIS and AMSR-E LST, (<b>c</b>) the original MODIS LST in the zoomed area (inside the gold square of <a href="#remotesensing-11-01704-f001" class="html-fig">Figure 1</a>a), (<b>d</b>) the original merged LST in the zoomed region (inside the gold square of <a href="#remotesensing-11-01704-f001" class="html-fig">Figure 1</a>b), (<b>e</b>) the merged LST with the regular gap-filling method, and (<b>f</b>) the integrated LST with the GWR-based filling algorithm applied to fill the gaps and also downscale to the same resolution of the MODIS LST, during nighttime on 15 December 2008.</p> "> Figure 2
<p>Scatter plots of MODIS ascending (<b>a</b>) and descending (<b>b</b>), and AMSR-E ascending (<b>c</b>) and descending (<b>d</b>) LST against the SURFRAD observations. The RMS is the Root Mean Square (RMS) error, and R represents Pearson correlation coefficient, N stands for sample number. The black diagonal refers to 1:1 line and the pink line is the least square fit line.</p> "> Figure 3
<p>Scatter plots of GOES LST products at 1.5 h before noon (<b>a</b>) and 1.5 h after sunrise (<b>b</b>), and the retrieved AMSR-2 ascending (<b>c</b>) and descending (<b>d</b>) LST vs. the SURFRAD observations in 2015. The RMS and R are the same as in <a href="#remotesensing-11-01704-f002" class="html-fig">Figure 2</a>. The black diagonal refers to 1:1 line, the pink line is the least square fit line.</p> "> Figure 4
<p>(<b>a</b>) Cloud free MODIS LST at 5 km resolution, (<b>b</b>) the derived AMSR-E LST at 25 km resolution, (<b>c</b>) the merged MODIS and AMSR-E LST at 25 km resolution, and (<b>d</b>) the integrated LST from MODIS and AMSR-E with the GWR-based method applied to fill the gaps and also downscale to the same 5 km resolution as the MODIS LST, during daytime on 5 December 2008.</p> "> Figure 5
<p>(<b>a</b>) Cloud free MODIS LST at 5 km resolution, (<b>b</b>) the AMSR-E LST at 25 km resolution, (<b>c</b>) the merged MODIS and AMSR-E LST at 25 km resolution, and (<b>d</b>) the integrated LST from MODIS and AMSR-E with the GWR-based method applied to fill the gaps and also downscale to the same 5 km resolution as the MODIS LST, during nighttime on 2 June 2008.</p> "> Figure 6
<p>(<b>a</b>) Cloud free GOES LST with 4 km resolution at 1.5 h before noon, (<b>b</b>) the AMSR-2 ascending LST at 10 km resolution, (<b>c</b>) the merged GOES and AMSR-2 LST at 10 km resolution, and (<b>d</b>) the integrated GOES and AMSR-2 LST with the GWR-based method applied to fill the gaps and also downscale to the same 4 km resolution as the GOES LST, on 19 July 2013.</p> "> Figure 7
<p>(<b>a</b>) Cloud free GOES LST with 4 km resolution at 1.5 h after sunrise, (<b>b</b>) the AMSR-2 descending LST at 10 km resolution, (<b>c</b>) the merged GOES and AMSR-2 LST at 10 km resolution, and (<b>d</b>) the integrated LST from GOES and AMSR-2 with the GWR-based method applied to fill the gaps and also downscale to the same 4 km resolution as the GOES LST, on 27 September 2013.</p> "> Figure 8
<p>An example of daily proxy ESI with the integrated MODIS and AMSR-E LST input (the first column), as compared with the current ESI (the second column), and the weekly US Drought Monitor (USDM) drought map (the third column).</p> ">
Abstract
:1. Introduction
2. Data and Methods
2.1. Data Used
2.1.1. Satellite Data
- The MODIS daily Aqua LST product (MYD11C1) in version 5.1, with a spatial resolution of 5 km is used in this study. Only good quality LST data with an accuracy of less than 1 K is selected.
- The MODIS land covers Climate Modeling Grid (CMG) product in version 5.1 (Short Name: MCD12C1) provides the dominant land cover types at a spatial resolution of 0.05°.
- The Normalized Difference Vegetation Index (NDVI) data that were used in the microwave algorithm development is extracted from the MODIS 16-days NDVI composite (short name: MYD13C1), with a resolution of 0.05° [46]. The daily NDVI data used in the downscaling process were derived from the MODIS/Aqua surface reflectance product at 0.05° grid (short name: MYD09CMG) [47]. The gaps in the daily NDVI data are filled with the previously available data.
- The Geostationary Operational Environmental Satellite (GOES) monitors the weather conditions in the Unites States (U.S.). The GOES LST data used in this study, which is available at half-hour temporal resolution and 4 km spatial resolution, are derived from the GOES-13 imager observations at 3.9 µm and 11 µm channels while using the algorithms that Sun and Pinker [24] and Sun et al. [26] developed.
- The digital elevation model (DEM) data are derived from the National Elevation Dataset (NED) data [48] at a resolution of 100 m.
2.1.2. In-Situ Data
2.2. Methods
2.2.1. Physical Basis for Remote Sensing of LST from Passive Microwave
2.2.2. Algorithms for Retrieving LST from Passive Microwave Data
The Single-Channel Algorithm with the 36.5 V GHz
The Four-Channel Algorithm
A Proposed New Five-Channel Algorithm
2.3. Regression Tree Algorithm
2.4. Gap Filling and Downscaling Method
- (1)
- Aggregate NDVI and DEM to microwave resolution (25 km for AMSR-E and 10 km for AMSR-2). Here, we take the AMSR-E and MODIS as an example, NDVI5km and DEM5km denote the auxiliary variables at the MODIS pixel resolution, whereas NDVI25km and DEM25km represent the aggregated auxiliary variables at the AMSR-E pixel resolution.
- (2)
- Establish the non-stationary relationship between the AMSR-E 25 km LST with the same spatial resolution auxiliary data:LST25km = a025km(x,y) + a125km(x,y) NDVI25km + a225km(x,y) DEM25km + ε25km
- (3)
- Estimate the regression coefficients a0(x,y), a1(x,y), a2(x,y), and the error term ε via Gaussian distance weighting at the coarse microwave resolution.
- (4)
- Apply the regular gap-filling algorithm to both MODIS LST and AMSR-E LST, so that the first gap-free observations can be obtained.
- (5)
- The bi-cubic interpolation is used to interpolate the regression coefficients and the residual at coarse microwave resolution to MODIS 5km resolution a05km (x,y), a15km(x,y), a25km(x,y), and error term ε5km.
- (6)
- The final downscaled LST at 5 km resolution can be calculated while using the auxiliary variables (NDVI and DEM) at 5 km resolution in conjunction with the regression coefficients and the residual term at 5 km m resolution:LST5km = a05km(x,y) + a15km(x,y) NDVI5km + a25km(x,y) DEM5km + ε5km
3. Results
3.1. Results from the Calibrations
3.1.1. Results from the AMSR-E to MODIS Calibration
Implementation of the Single-Channel Algorithm
Implementation of the Four-Channel Algorithm
Implementation of the Proposed Five-Channel Algorithm
3.1.2. Results from the AMSR-2 to GOES Calibration
3.2. Results from the Validation against Ground Observations
3.3. Results from the Implementation to the Real Observations
4. Discussion
5. Summary
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Site No. | Site Location | Lat (N)/Lon(W) | Surface Type * |
---|---|---|---|
1 | Bondville, IL | 40.05/88.37 | Crop Land |
2 | Fort Peck, MT | 48.31/105.10 | Close Shrubland |
3 | Goodwin Creek, MS | 34.25/89.87 | Deciduous Forest |
4 | Table Mountain, CO | 40.13/105.24 | Crop Land |
5 | Sioux Falls, SD | 43.73/96.62 | Grass Land |
6 | Pennsylvania State University, PA | 40.72/77.93 | Mixed Forest |
Seasons | Correlation Coefficient | MAE (K) | RMS (K) | Sample Number |
---|---|---|---|---|
Spring | 0.89 | 4.73 | 5.99 | 215,184 |
Summer | 0.70 | 4.56 | 6.44 | 348,823 |
Fall | 0.67 | 4.45 | 6.54 | 341,448 |
Winter | 0.83 | 4.22 | 5.42 | 168,043 |
Seasons | Correlation Coefficient | MAE (K) | RMS (K) | Sample Number |
---|---|---|---|---|
Spring | 0.82 | 5.03 | 6.28 | 139,793 |
Summer | 0.75 | 3.56 | 5.34 | 255,269 |
Fall | 0.61 | 3.89 | 5.23 | 253,139 |
Winter | 0.74 | 4.51 | 6.01 | 105,963 |
Seasons | Correlation Coefficient | MAE (K) | RMS (K) | Sample Number |
---|---|---|---|---|
Spring | 0.90 | 3.21 | 4.12 | 215,184 |
Summer | 0.88 | 3.35 | 4.32 | 348,823 |
Fall | 0.90 | 2.89 | 3.87 | 341,448 |
Winter | 0.93 | 2.43 | 3.02 | 168,043 |
Seasons | Correlation Coefficient | MAE (K) | RMS Error (K) | Sample Number |
---|---|---|---|---|
Spring | 0.89 | 2.21 | 3.27 | 139,793 |
Summer | 0.92 | 2.03 | 2.40 | 255,269 |
Fall | 0.93 | 1.85 | 2.34 | 253,139 |
Winter | 0.87 | 2.43 | 3.27 | 105,963 |
Seasons | Methods | Correlation Coefficient | MAE (K) | RMS (K) | Sample Number |
---|---|---|---|---|---|
Spring | L | 0.92 | 2.95 | 3.95 | 215,184 |
RT | 0.99 | 0.16 | 0.83 | ||
Summer | L | 0.90 | 3.32 | 4.38 | 348,823 |
RT | 0.99 | 0.70 | 0.84 | ||
Fall | L | 0.91 | 2.92 | 3.98 | 341,448 |
RT | 0.99 | 0.70 | 0.78 | ||
Winter | L | 0.95 | 2.25 | 2.99 | 168,043 |
RT | 0.99 | 0.63 | 0.46 |
Seasons | Methods | Correlation Coefficient | MAE (K) | RMS (K) | Sample Number |
---|---|---|---|---|---|
Spring | L | 0.89 | 2.25 | 3.02 | 139,793 |
RT | 0.99 | 0.33 | 0.43 | ||
Summer | L | 0.91 | 1.88 | 2.55 | 255,269 |
RT | 0.99 | 0.41 | 0.52 | ||
Fall | L | 0.94 | 1.83 | 2.44 | 253,139 |
RT | 0.99 | 0.46 | 0.57 | ||
Winter | L | 0.88 | 2.52 | 3.35 | 105,963 |
RT | 0.99 | 0.39 | 0.64 |
Seasons | Methods | Correlation Coefficient | Mean Absolute Error (K) | Root Mean Squared Error (K) | Total Number of Instances |
---|---|---|---|---|---|
Spring | L | 0.93 | 2.96 | 3.83 | 382,965 |
RT | 0.985 | 1.18 | 1.64 | ||
Summer | L | 0.91 | 3.22 | 4.14 | 757,026 |
RT | 0.981 | 1.21 | 1.70 | ||
Fall | L | 0.92 | 2.88 | 3.70 | 897,344 |
RT | 0.990 | 1.048 | 1.44 | ||
Winter | L | 0.92 | 2.54 | 3.29 | 203,571 |
RT | 0.990 | 0.912 | 1.27 |
Seasons | Methods | Correlation Coefficient | Mean Absolute Error (K) | Root Mean Squared Error (K) | Total Number of Instances |
---|---|---|---|---|---|
Spring | L | 0.84 | 2.63 | 3.38 | 159,725 |
RT | 0.982 | 0.881 | 1.25 | ||
Summer | L | 0.84 | 2.33 | 3.07 | 550,810 |
RT | 0.977 | 0.815 | 1.17 | ||
Fall | L | 0.87 | 2.18 | 2.87 | 613,528 |
RT | 0.986 | 0.841 | 1.16 | ||
Winter | L | 0.82 | 2.83 | 3.86 | 195,493 |
RT | 0.982 | 0.902 | 1.29 |
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Sun, D.; Li, Y.; Zhan, X.; Houser, P.; Yang, C.; Chiu, L.; Yang, R. Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations. Remote Sens. 2019, 11, 1704. https://doi.org/10.3390/rs11141704
Sun D, Li Y, Zhan X, Houser P, Yang C, Chiu L, Yang R. Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations. Remote Sensing. 2019; 11(14):1704. https://doi.org/10.3390/rs11141704
Chicago/Turabian StyleSun, Donglian, Yu Li, Xiwu Zhan, Paul Houser, Chaowei Yang, Long Chiu, and Ruixin Yang. 2019. "Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations" Remote Sensing 11, no. 14: 1704. https://doi.org/10.3390/rs11141704