Inversion of soil moisture in the Yuanmou hot-dry river valley area based on the PSO_GA-RBF neural network model

doi:10.13474/j.cnki.11-2246.2024.1101

Abstract

Abstract: Soil moisture has a significant impact on hydrological and climatic processes. A comprehensive and accurate understanding of soil moisture status is of great research value for hydrological simulation, ecological governance, and other related fields. In response to the soil moisture inversion issue in the Yuanmou hot-dry river valley area, a new soil moisture inversion model is constructed using the PSO_GA-combined optimized RBF neural network. The experiment utilizes Sentinel-1 radar data and Sentinel-2 optical data, and employs the water-cloud model suitable for low vegetation cover types in the study area to correct the vegetation scattering effects. The obtained VV and VH polarized soil backscattering coefficients and cross-polarization differences are incorporated into the constructed model, enabling the remote sensing inversion of soil volumetric water content in the hot-dry river valley area of Yuanmou county, Yunnan province. Comparisons and validation against measured soil volumetric water content data show a root mean square error of 0.55% m³/m³ and a coefficient of determination (R²) of 0.855, demonstrating a significant improvement in accuracy compared to traditional RBF neural network models.Correlational analysis is conducted between the inversion results and NDVI values, revealing a coefficient of determination (R²) of 0.512 7 between the two. This verifies the high precision of soil volumetric water content inversion based on Sentinel-1 radar image data, utilizing the water-cloud model and PSO_GA-combined optimized RBF neural network, validating the feasibility of large-scale soil moisture monitoring in hot-dry river valley areas.

Key words: soil moisture, Sentinel-1/2, radial basis function neural network, dry-hot valley, soil moisture inversion

CLC Number:

P237

DU Jinming, LUO Mingliang, BAI Leichao, WU Qiusheng. Inversion of soil moisture in the Yuanmou hot-dry river valley area based on the PSO_GA-RBF neural network model[J]. Bulletin of Surveying and Mapping, 2024, 0(11): 1-6.

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