SMAP SM and VOD retrievals (2016-2020)

The accuracy of global soil moisture (SM) and vegetation optical depth products at L-band (L-VOD) derived from the inversion of radiative transfer-based models is highly dependent on the input of soil temperature. The latter can be obtained from model-based temperature products. However, their performance as input for retrieval algorithms for global-scale SM and L-VOD has not been evaluated so far. To fill this gap, our research aims to evaluate four commonly used model-based soil temperature products as input to the SMAP-INRAE-BORDEAUX (SMAP-IB) algorithm for retrieving SM and L-VOD. More specifically, differences in SMAP-IB retrieval of SM and L-VOD were investigated based on four model-based soil temperature sources as input and four temperature configurations, including different <i>T</i>_<em>C</em> settings and <i>T</i>_<em>G</em> settings. We provide 16 group of SM and L-VOD products, and the code of TCA method.

基于辐射传输模型反演得到的L波段全球土壤湿度（soil moisture, SM）与植被光学厚度产品（L-band vegetation optical depth, L-VOD）的精度，高度依赖于土壤温度的输入。此类土壤温度可通过基于模型的温度产品获取，但目前尚未有研究评估其作为全球尺度SM与L-VOD反演算法输入的性能。为填补这一研究空白，本研究旨在评估四种常用的基于模型的土壤温度产品，将其作为SMAP-INRAE-BORDEAUX（SMAP-IB）反演算法的输入，以反演SM与L-VOD。具体而言，本研究基于四种基于模型的土壤温度数据源与四种温度配置（包括不同的T_C设置与T_G设置），探究了SMAP-IB反演得到的SM与L-VOD之间的差异。本数据集包含16组SM与L-VOD产品，以及TCA方法（TCA method）的代码。

搜集汇总

背景与挑战

背景概述

该数据集提供了2016-2020年间基于SMAP卫星数据的土壤湿度和植被光学深度反演产品，包含16组不同土壤温度配置下的SM和L-VOD数据，以及TCA方法的代码。数据集旨在评估不同土壤温度产品对SM和L-VOD反演精度的影响。