Soil Moisture Inversion in Grassland Ecosystem Using Remote Sensing Considering Different Grazing Intensities and Growing Seasons

Abstract

Although vegetation community information such as grazing gradient, biomass, and density have been well characterized in typical grassland communities with Stipa grandis and Leymus chinensis as dominant species, their impact on the soil moisture (SM) inversion is still unclear. This study investigated the characteristics of a grassland vegetation community at different grazing gradients and growing seasons and its impact on SM inversion using remote sensing data. The water cloud model (WCM) was used for SM inversion, and both field and remote sensing data collected from 2019 to 2021 were used for calibration and prediction. The study found that the calibrated WCM achieved prediction results of SM inversion with average R2 values of 0.41 and 0.38 at different grazing gradients and growing seasons, respectively. Vegetation biomass and height were significantly correlated with vegetation indexes, and the highest model prediction accuracy was achieved for biomass and height around 121.1 g/m2 [102.3–139.9] and 18.6 cm [17.3–19.8], respectively. Generally, NDWI1 produced higher SM estimation accuracy than NDWI2. The growing season of vegetation also affects the accuracy of the WCM to retrieve SM, with the highest accuracy achieved in mid-growing season I. Therefore, the developed WCM with optimal height and biomass of vegetation communities can enhance the SM prediction capacity; it thus can be potentially used for SM prediction in typical grasslands.