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Soil, Remote Sensing, and Statistics
Soil, Remote Sensing, and Statistics
Click me for full description on Remote Sensing.
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.
Graphic Abstract
Figure 1
(a) 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) SSI map for the whole month of April 2015.
Figure 2
(a) Palmer drought severity index (PDSI) for April 2015. Areas in yellow and red represent areas that are experiencing dry conditions; (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.
Figure 3
(a) Scatter plot for April 2015. The correlation between SSI and PDSI is moderate (r = 0.52); (b) Scatter plot for 1 to 3 April 2015. The correlation between SSI and NDWI is strong (r = 0.56).
Figure 4
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.
Figure 5
SMAP and SCAN time plot comparison to identify the anomaly: (a) Uapb-Earle station in Arkansas (2085); (b) Sellers Lake #1 station in Florida (2012); (c) Princeton #1 station in Kentucky (2005); and (d) N Piedmont Arec station in Virginia (2039).
Spatially Explicit Model for Statistical Downscaling of Satellite Passive Microwave Soil Moisture
Spatially Explicit Model for Statistical Downscaling of Satellite Passive Microwave Soil Moisture
Click me for full description on IEEE Transactions on Geoscience and Remote Sensing
Study Area and Technical Flow Chart
Downscaled SM map and original map. (a) Downscaled result with SESD. (b) Downscaled result with RF. (c) SMAP L3 at 9 km.
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