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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Wiley-VCH
2024-11-01
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| Online Access: | https://doi.org/10.1002/rvr2.103 |
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| author | Xiangdong Qin Zhiguo Pang Jingxuan Lu |
| author_facet | Xiangdong Qin Zhiguo Pang Jingxuan Lu |
| author_sort | Xiangdong Qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2edd4e5f2df0429dbd23fbbe2c6cb978 |
| institution | OA Journals |
| issn | 2750-4867 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | River |
| spelling | doaj-art-2edd4e5f2df0429dbd23fbbe2c6cb9782025-08-20T01:59:01ZengWiley-VCHRiver2750-48672024-11-013439940710.1002/rvr2.103Estimation and analysis of vegetation parameters for the water cloud modelXiangdong Qin0Zhiguo Pang1Jingxuan Lu2Department of Drought Mitigation and Policy Research, Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources China Institute of Hydraulic and Hydropower Research Beijing ChinaDepartment of Drought Mitigation and Policy Research, Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources China Institute of Hydraulic and Hydropower Research Beijing ChinaDepartment of Drought Mitigation and Policy Research, Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources China Institute of Hydraulic and Hydropower Research Beijing ChinaAbstract 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.https://doi.org/10.1002/rvr2.103backscattering coefficientgradient descent algorithmobjective functionsoil moisturewater cloud model |
| spellingShingle | Xiangdong Qin Zhiguo Pang Jingxuan Lu Estimation and analysis of vegetation parameters for the water cloud model River backscattering coefficient gradient descent algorithm objective function soil moisture water cloud model |
| title | Estimation and analysis of vegetation parameters for the water cloud model |
| title_full | Estimation and analysis of vegetation parameters for the water cloud model |
| title_fullStr | Estimation and analysis of vegetation parameters for the water cloud model |
| title_full_unstemmed | Estimation and analysis of vegetation parameters for the water cloud model |
| title_short | Estimation and analysis of vegetation parameters for the water cloud model |
| title_sort | estimation and analysis of vegetation parameters for the water cloud model |
| topic | backscattering coefficient gradient descent algorithm objective function soil moisture water cloud model |
| url | https://doi.org/10.1002/rvr2.103 |
| work_keys_str_mv | AT xiangdongqin estimationandanalysisofvegetationparametersforthewatercloudmodel AT zhiguopang estimationandanalysisofvegetationparametersforthewatercloudmodel AT jingxuanlu estimationandanalysisofvegetationparametersforthewatercloudmodel |