Soil Moisture in a Vegetation-Covered Area Using the Improved Water Cloud Model Based on Remote Sensing
2022
Lei, Junjie | Yang, Wunian | Yang, Xin
The vegetation canopy parameters in the water cloud model originate from the surface spectrum of the vegetation canopy and do not fully consider its spatial structure, biomass, and water content. This study resolved this problem using a new vegetation canopy water content calculation method to improve the water cloud model. Our results showed that the novel calculation method, which considers the vertical and horizontal structure, biomass, and the water content of the canopy, is suitable for obtaining a more accurate estimation of the vegetation canopy water content. Additionally, this improved water cloud model (IWCM) was used to retrieve the soil moisture content of often greenwood, deciduous forest, mixed forest-, composite shrub grass-, and grassland-covered areas. The tenfold cross-validation determination coefficient (R²) of the soil moisture content model was 0.43. The relative root mean square error (rRMSE) was 21.89%. When applied to deciduous forest, composite shrub grass, and grassland-covered areas, R² = 0.57 and rRMSE = 16.48% were obtained. When the IWCM was applied only to the composite shrub grass- and grassland-covered areas, the R² was 0.76, and rRMSE was 9.8%. Therefore, the IWCM provides new insights regarding the remote sensing quantitative inversion of soil moisture content in areas with high vegetation density and woodland cover, which can be applied in the future.
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