AMSR-E/AMSR2土壤水分与植被光学厚度0.1°逐日产品（多通道协同反演算法，2002-2022）

土壤水分是全球观测系统提出的关键气候变量之一，在陆气相互作用中起着重要作用。植被光学厚度是微波辐射传输过程中衡量植被衰减特性的关键参数，在植被水力学、植被物候学和生物量研究领域中有着广泛应用。 本数据集基于AMSR-E和AMSR2 0.1° 分辨率的交叉定标亮度温度数据，使用多通道协同反演算法（MCCA）获得了全球第一套具有极化差异的多波段（C/X/Ku）植被光学厚度产品及土壤水分产品。该算法（MCCA）能综合考虑多个通道之间的物理关系，能同时反演出土壤水分和具有频率差异，极化差异的植被光学厚度。 本数据集使用了来自国际土壤水分观测网络和美国农业部发布的共25个土壤水分密集观测站网进行验证，结果表明，在目前公开的与AMSR-E/2相关的土壤水分数据集中，MCCA土壤水分的无偏均方根误差(ubRMSE)最小。此外，MCCA反演得到的具有频率和极化差异的植被光学厚度数据可为植被生理过程中的水通量研究提供新的见解。

Soil moisture is one of the key climate variables proposed by the Global Observing System, and plays a vital role in land-atmosphere interactions. Vegetation Optical Depth (VOD) is a key parameter for characterizing vegetation attenuation properties in microwave radiative transfer processes, and has been widely applied in research fields such as vegetation hydraulics, vegetation phenology, and biomass studies. This dataset is developed based on cross-calibrated brightness temperature data from AMSR-E and AMSR2 with a 0.1° spatial resolution, and the first global set of multi-band (C/X/Ku) Vegetation Optical Depth products and soil moisture products with polarization differences was obtained using the Multi-channel Collaborative Retrieval Algorithm (MCCA). The MCCA algorithm can comprehensively consider the physical relationships among multiple channels, and simultaneously retrieve soil moisture and Vegetation Optical Depth with both frequency and polarization differences. This dataset was validated using a total of 25 soil moisture dense observation networks from the International Soil Moisture Network and the United States Department of Agriculture. The results show that among the currently publicly available soil moisture datasets related to AMSR-E/2, the MCCA-retrieved soil moisture has the smallest unbiased root mean square error (ubRMSE). In addition, the Vegetation Optical Depth data with frequency and polarization differences retrieved by MCCA can provide new insights into water flux research in vegetation physiological processes.

搜集汇总

背景与挑战

背景概述

该数据集是基于AMSR-E和AMSR2传感器、采用多通道协同反演算法（MCCA）生成的全球土壤水分和植被光学厚度逐日产品，时间覆盖2002年至2022年，空间分辨率为0.1°至0.25°。其关键特点是提供了全球首套具有极化差异的多波段植被光学厚度数据，并同时反演土壤水分，验证表明在同类产品中误差最小，适用于植被水力学和陆气相互作用研究。