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, a...

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Bibliographic Details
Main Authors: Xiangdong Qin, Zhiguo Pang, Jingxuan Lu
Format: Article
Language:English
Published: Wiley-VCH 2024-11-01
Series:River
Subjects:
Online Access:https://doi.org/10.1002/rvr2.103
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Summary: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.
ISSN:2750-4867