Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China
<p>The spatial distribution of arid and humid regions and 893 monitoring stations in China.</p> "> Figure 2
<p>Locations of the 893 monitoring stations projected onto the land cover map of China. The green points represent soil moisture stations, and the red points represent stations with both SM and climate data. The classification standards for land cover are described in <a href="#sec2dot1dot5-remotesensing-10-01302" class="html-sec">Section 2.1.5</a>.</p> "> Figure 3
<p>Spatial distribution of the (<b>a</b>) Pearson correlation coefficient (R), (<b>b</b>) unbiased root mean square error (ubRMSE) between the SMAP and in situ SM at 893 SM stations in China (1 April 2016–30 September 2016). Locations of the 893 monitoring stations are projected onto slope gradient map of China.</p> "> Figure 4
<p>Spatial distribution of the (<b>a</b>) Pearson correlation coefficient (R), (<b>b</b>) unbiased root mean square error (ubRMSE) between 7-day averaged SMAP and in situ SM at 204 stations with both SM data and climate data in China (1 April 2016–30 September 2016).</p> "> Figure 5
<p>Custom regions in China for the evaluation of the SMAP L3 radiometer SM at the regional scale, including region 1–3.</p> "> Figure 6
<p>Time series plots of the SMAP L3 SM data and the in situ SM over (<b>a</b>) region 1, (<b>b</b>) region 2 and (<b>c</b>) region 3. Variable Soil Moisture and Precipitation represent the 7-day averaged SM and 7-day accumulated precipitation, respectively. 30 groups of data (i.e., SMAP SM, in situ SM and precipitation) were used from 1 April 2016 to 30 September 2016. It shows that the rainfall results in increases in SM, and SMAP SM can generally reflect these dynamics.</p> "> Figure 6 Cont.
<p>Time series plots of the SMAP L3 SM data and the in situ SM over (<b>a</b>) region 1, (<b>b</b>) region 2 and (<b>c</b>) region 3. Variable Soil Moisture and Precipitation represent the 7-day averaged SM and 7-day accumulated precipitation, respectively. 30 groups of data (i.e., SMAP SM, in situ SM and precipitation) were used from 1 April 2016 to 30 September 2016. It shows that the rainfall results in increases in SM, and SMAP SM can generally reflect these dynamics.</p> "> Figure 7
<p>Comparison between the SMAP L3 SM product and the in situ SM observations over (<b>a</b>) region 1, (<b>b</b>) region 2, (<b>c</b>) region 3. 30 pairs of 7-day averaged data were used from 1 April 2016 to 30 September 2016.</p> "> Figure 8
<p>Pearson correlation coefficient (R) between the weekly SWDI-SMAP and AWD at 204 monitoring stations with both SM and climate data. 30 pairs of 7-day averaged data were used from 1 April 2016 to 30 September 2016.</p> "> Figure 9
<p>Comparison between the SWDI-SMAP and AWD over (<b>a</b>) region 1, (<b>b</b>) region 2, (<b>c</b>) region 3. 30 pairs of 7-day averaged data were used from 1 April 2016 to 30 September 2016.</p> "> Figure 10
<p>Percentage of consistent (<b>a</b>) all drought events and (<b>b</b>) severe and extreme drought events between the SWDI-SMAP and AWD at 204 monitoring stations with both SM and climate data. 30 pairs of 7-day averaged data were used from 1 April 2016 to 30 September 2016.</p> "> Figure 11
<p>Difference between (<b>a</b>) all drought events and (<b>b</b>) severe and extreme drought events between the SWDI-SMAP and AWD at 204 monitoring stations with both SM and climate data. 30 pairs of 7-day averaged data were used from 1 April 2016 to 30 September 2016.</p> "> Figure 12
<p>Spatial distribution of the SWDI-SMAP across China for the month of June 2016 (regions A–E denote representative areas with drought differences between the SWDI-SMAP and VHI. There are severe and extreme drought in region A-C and moderate drought in region D on the SWDI-SMAP; however, the drought conditions are much lighter, or there is no drought, on the VHI. There is no drought in region E on SWDI-SMAP, but the drought condition is much more serious on the VHI).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.1.1. Study Area
2.1.2. SMAP SM Product
2.1.3. Ground Observation Databases
2.1.4. Soil Water Parameters
2.1.5. Auxiliary Data
2.2. Methodology
2.2.1. Soil Water Deficit Index (SWDI)
2.2.2. Atmospheric Water Deficit (AWD)
2.2.3. Vegetation Health Index (VHI)
3. Results
3.1. Evaluation of SMAP L3 Radiometer SM
3.2. Comparison of the SWDI-SMAP with AWD
3.2.1. Correlation between the SWDI-SMAP and AWD
3.2.2. Drought Events
3.3. Comparison of the SWDI-SMAP with VHI
4. Discussion
4.1. Analysis on Preformance of SMAP L3 SM Product
4.2. Analysis on Preformance of SWDI-SMAP
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No. of Sites | R | Bias (cm3·cm−3) | RMSE (cm3·cm−3) | ubRMSE (cm3·cm−3) | |
---|---|---|---|---|---|
Region 1 | 35 | 0.48 | −0.038 | 0.046 | 0.026 |
Region 2 | 76 | 0.82 | 0.020 | 0.025 | 0.012 |
Region 3 | 28 | 0.79 | −0.091 | 0.094 | 0.022 |
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Bai, J.; Cui, Q.; Chen, D.; Yu, H.; Mao, X.; Meng, L.; Cai, Y. Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China. Remote Sens. 2018, 10, 1302. https://doi.org/10.3390/rs10081302
Bai J, Cui Q, Chen D, Yu H, Mao X, Meng L, Cai Y. Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China. Remote Sensing. 2018; 10(8):1302. https://doi.org/10.3390/rs10081302
Chicago/Turabian StyleBai, Jueying, Qian Cui, Deqing Chen, Haiwei Yu, Xudong Mao, Lingkui Meng, and Yang Cai. 2018. "Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China" Remote Sensing 10, no. 8: 1302. https://doi.org/10.3390/rs10081302