SMAP卫星土壤水分与植被光学厚度逐日产品（多通道协同反演算法，2015-2024）

土壤水分是地气交互作用的重要边界条件，是全球观测系统提出的关键气候变量之一；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物理量，在表征植被水分与生物量动态变化中具有重要作用。
本数据集使用多通道协同反演算法获取SMAP观测的土壤水分与植被光学厚度。该算法利用参数间的自约束关系与通道间的理论转换关系进行地表参数反演，反演过程不依赖于其他辅助数据，并适用于多种不同载荷配置。本数据集的土壤水分反演结果包含了融化期的土壤水分含量与冻结期的液态水含量；同时反演了水平和垂直两个极化的植被光学厚度，是全球第一套具有极化差异的L波段植被光学厚度产品。
本数据集基于国际土壤水分观测网络、美国农业部及研究室自建发布的共19个土壤水分密集观测站网（其中包含9个SMAP核心验证站点以及SMAP尚未使用的10个密集观测站点）以及被广泛使用的土壤气候分析网络SCAN进行验证，结果发现MCCA土壤水分反演结果精度优于其它SMAP产品。

Soil moisture is an important boundary condition for land-atmosphere interaction and one of the key climate variables proposed by the Global Observing System. Vegetation optical thickness is a physical quantity that measures the attenuation characteristics of vegetation during microwave radiative transfer, and plays a critical role in characterizing the dynamic changes of vegetation water content and biomass.

This dataset retrieves soil moisture and vegetation optical thickness from SMAP observations via a multi-channel collaborative inversion algorithm. The algorithm utilizes self-constraint relationships between parameters and theoretical conversion relationships between channels to invert surface parameters, without relying on any auxiliary data, and is applicable to various sensor payload configurations. The soil moisture inversion results of this dataset include soil moisture content during the thawing period and liquid water content during the freezing period; meanwhile, vegetation optical thicknesses under both horizontal and vertical polarizations are retrieved, making it the world's first set of L-band vegetation optical thickness products with polarization differences.

This dataset is validated using a total of 19 intensive soil moisture observation station networks, which comprise 9 SMAP core validation sites and 10 intensive observation sites not yet used by SMAP, sourced from the International Soil Moisture Network, the United States Department of Agriculture (USDA), and self-released datasets from the research laboratory, as well as the widely adopted Soil Climate Analysis Network (SCAN). Validation results demonstrate that the accuracy of the MCCA soil moisture inversion results outperforms other SMAP products.

搜集汇总

背景与挑战

背景概述

该数据集是基于SMAP卫星的逐日土壤水分和植被光学厚度产品，采用多通道协同反演算法（MCCA）生成，覆盖2015年至2024年，具有日时间分辨率和10km-100km空间分辨率。其关键特点包括：反演过程不依赖辅助数据，适用于多种载荷配置，提供了全球首套具有极化差异的L波段植被光学厚度产品，并经过广泛验证，精度优于其他SMAP产品。