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Estimating Effective Roughness Parameters of the L-MEB Model for Soil Moisture Retrieval Using Passive Microwave Observations From SMAPVEX12

机译:使用SMAPVEX12的被动微波观测估计L-MEB模型用于土壤水分反演的有效粗糙度参数

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Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m/m if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.
机译:尽管人们一直在努力改进现有的土壤水分反演算法,但是从被动微波观测中估算土壤水分的能力仍然受到准确建模观测到的微波信号问题的困扰。本文着重于估计来自生物圈(L-MEB)模型的L波段微波发射的有效表面粗糙度参数,以改善从被动微波观测获得的土壤水分。来自加拿大进行的SMAP验证实验2012的数据用于开发和验证用于估算有效粗糙度参数的简单模型。结果表明,L-MEB粗糙度参数可以根据经验与所观察到的亮度温度和植被的叶面积指数相关。这些结果表明,粗糙度参数可以补偿粗糙度和植被效应。使用留一法交叉验证还表明,该模型能够准确估计L-MEB模型反演所需的粗糙度参数。为了证明粗糙度参数化的有用性,将模型的性能与更传统的粗糙度公式进行了比较。结果表明,如果在土壤水分反演算法中采用本研究提出的粗糙公式,则土壤水分反演误差可降低至0.054 m / m。

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