5 May 2016 Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation
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Abstract
Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA’s Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.
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Gurjeet Singh, Gurjeet Singh, Rabindra K. Panda, Rabindra K. Panda, Binayak P. Mohanty, Binayak P. Mohanty, Raghavendra B. Jana, Raghavendra B. Jana, } "Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation", Proc. SPIE 9877, Land Surface and Cryosphere Remote Sensing III, 98772B (5 May 2016); doi: 10.1117/12.2222743; https://doi.org/10.1117/12.2222743
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