Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-02, Vol.11 (3), p.362
Main Authors: Zhu, Qian, Luo, Yulin, Xu, Yue-Ping, Tian, Ye, Yang, Tiantian
Format: Article
Language:English
Subjects:
Agricultural drought
Agricultural production
Agronomy
Atmospheric water
AWD
Civil engineering
Climate change
Correlation coefficient
Correlation coefficients
Data collection
Datasets
Disasters
Drought
Drought index
Environmental monitoring
Evapotranspiration
Growing season
Mathematical analysis
Moisture content
Precipitation
Rain
Remote sensing
River basins
Rivers
SMAP
Soil moisture
Soil surfaces
Soil water
Soils
SWDI
Trends
Water deficit
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Summary:Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations are inaccessible in many areas of the world. Remote sensing techniques enrich the surface soil moisture observations at different tempo-spatial resolutions. In this study, the Level 2 L-band radiometer soil moisture dataset was used to estimate the Soil Water Deficit Index (SWDI). The Soil Moisture Active Passive (SMAP) dataset was evaluated with the soil moisture dataset obtained from the China Land Soil Moisture Data Assimilation System (CLSMDAS). The SMAP-derived SWDI (SMAP_SWDI) was compared with the atmospheric water deficit (AWD) calculated with precipitation and evapotranspiration from meteorological stations. Drought monitoring and comparison were accomplished at a weekly scale for the growing season (April to November) from 2015 to 2017. The results were as follows: (1) in terms of Pearson correlation coefficients (R-value) between SMAP and CLSMDAS, around 70% performed well and only 10% performed poorly at the grid scale, and the R-value was 0.62 for the whole basin; (2) severe droughts mainly occurred from mid-June to the end of September from 2015 to 2017; (3) severe droughts were detected in the southern and northeastern Xiang River Basin in mid-May of 2015, and in the northern basin in early August of 2016 and end of November 2017; (4) the values of percentage of drought weeks gradually decreased from 2015 to 2017, and increased from the northeast to the southwest of the basin in 2015 and 2016; and (5) the average value of R and probability of detection between SMAP_SWDI and AWD were 0.6 and 0.79, respectively. These results show SMAP has acceptable accuracy and good performance for drought monitoring in the Xiang River Basin.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11030362