Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Dynamic monitoring of the spatial pattern of winter continuously flooded paddies (WFP) at regional scales is a challenging but highly necessary process in analyzing trace greenhouse gas emissions, water resource management, and food security. The present study was carried out to demonstrate the feasibility of extracting the spatial distribution of WFP through time series imagery of volumetric surface soil moisture content (θv) at the field scale (30 m). A trade-off approach based on the synergistic use of tasseled cap transformation wetness and temperature vegetation dryness index was utilized to obtain paddy θv. The results showed that the modeled θv was in good agreement with in situ measurements. The overall correlation coefficient (R) was 0.78, with root-mean-square ranging from 1.96% to 9.96% in terms of different vegetation cover and surface water status. The lowest error of θv estimates was found to be restricted at the flooded paddy surface with moderate or high fractional vegetation cover. The flooded paddy was then successfully identified using the θv image with saturated moisture content thresholding, with an overall accuracy of 83.33%. This indicated that the derived geospatial dataset of WFP could be reliably applied to fill gaps in census statistics.