Estimation and analysis of vegetation parameters for the water cloud model

Abstract The Water Cloud Model (WCM) plays a crucial role in active microwave soil moisture inversion applications. Empirical parameters are important factors affecting the accuracy of WCM simulation, but the current evaluation of empirical parameters only considers the forward simulation process, and insufficient consideration is given to the model inversion problem. This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function. The effectiveness of the method is then verified using measured data. Simultaneously, this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM, resulting in the following conclusions. First, blindly utilizing vegetation parameters recommended by previous model studies is not advisable. To ensure the accuracy of the simulation, it is necessary to adjust the vegetation parameters appropriately. Second, to ensure the ability of the WCM solving both forward and inverse problems, it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function. Third, soil backscatter simulations have an impact on the solution of vegetation parameters, and more accurate soil scattering models provide a better representation of the modeled vegetation. This study presents a dependable method for resolving the vegetation parameters of the WCM, thereby offering a valuable reference for the application of the model in surface parameter inversion research.

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Publication Details

Journal
River
Published
2024-11-01
DOI
https://doi.org/10.1002/rvr2.103
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00

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article

Estimation and analysis of vegetation parameters for the water cloud model

Jingxuan Lu, Xiangdong Qin, Zhiguo Pang
River
Soil Moisture and Remote Sensing
article

Estimation and analysis of vegetation parameters for the water cloud model

Jingxuan Lu, Xiangdong Qin, Zhiguo Pang
article en

Abstract

Abstract The Water Cloud Model (WCM) plays a crucial role in active microwave soil moisture inversion applications. Empirical parameters are important factors affecting the accuracy of WCM simulation, but the current evaluation of empirical parameters only considers the forward simulation process, and insufficient consideration is given to the model inversion problem. This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function. The effectiveness of the method is then verified using measured data. Simultaneously, this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM, resulting in the following conclusions. First, blindly utilizing vegetation parameters recommended by previous model studies is not advisable. To ensure the accuracy of the simulation, it is necessary to adjust the vegetation parameters appropriately. Second, to ensure the ability of the WCM solving both forward and inverse problems, it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function. Third, soil backscatter simulations have an impact on the solution of vegetation parameters, and more accurate soil scattering models provide a better representation of the modeled vegetation. This study presents a dependable method for resolving the vegetation parameters of the WCM, thereby offering a valuable reference for the application of the model in surface parameter inversion research.

RiverVol. 3(4)
China Institute of Water Resources and Hydropower Research (CN), Ministry of Water Resources of the People's Republic of China (CN)
National Natural Science Foundation of China
Life in Land, Clean water and sanitation
Openalex Percentile: Top 82%
Soil Moisture and Remote Sensing
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Estimation and analysis of vegetation parameters for the water cloud model — Jingxuan Lu, Xiangdong Qin, et al. · River (2024) | TGRS Research Map | TGRS