射频干扰抑制的SMOS卫星 L波段多角度亮温产品（2010-2021）

SMOS是全球首颗具备多角度L波段亮温观测能力的卫星，是监测全球土壤水分时空分布的重要工具。由于L波段射频干扰（Radio frequency interference, RFI）的广泛存在，以及二维综合孔径技术引起的视场混叠（Aliasing）影响，SMOS卫星的多角度亮温数据存在不同程度的缺失或者异常情况。
本数据集基于欧空局官方发布的Level-1C级亮温产品SCLF1C，该产品按照15 km间隔、二十面体斯奈德等积（Icosahedral Snyder Equal Area projection，ISEA）投影方式的DGG（discrete global grid）网格存储，使用赵天杰等人提出的双步回归方法（Two-Step Regression）优化或重建受视场混叠和射频干扰影响的多角度亮温数据，并重采样至EASE-GRID 2.0 投影方式（含25 km和36 km两种网格分辨率）。
将本数据集应用于多时相多角度土壤水分反演算法，发现在全球多个土壤水分密集观测站网内，本数据集反演的土壤水分精度优于其他SMOS亮温产品，尤其在中国地区等射频干扰严重地区的有效反演数据量显著增多。本数据集将为土壤水分、冻融状态、植被水分、地上生物量、积雪密度等长时序的L波段陆表参数产品研发提供更为可靠的数据支撑。

SMOS is the world's first satellite equipped with multi-angle L-band brightness temperature observation capabilities, serving as a critical tool for monitoring the spatiotemporal distribution of global soil moisture. Due to the widespread presence of L-band Radio Frequency Interference (RFI) and the field-of-view aliasing caused by 2-dimensional synthetic aperture technology, the multi-angle brightness temperature data from SMOS exhibits varying degrees of missing or abnormal values.

This dataset is developed based on the official Level-1C brightness temperature product SCLF1C released by the European Space Agency (ESA). This product is stored in a discrete global grid (DGG) using the Icosahedral Snyder Equal Area (ISEA) projection with a 15 km spacing. The Two-Step Regression method proposed by Zhao Tianjie et al. is employed to optimize or reconstruct the multi-angle brightness temperature data affected by field-of-view aliasing and RFI, and the data is then resampled to the EASE-GRID 2.0 projection with two grid resolutions of 25 km and 36 km, respectively.

When applied to multi-temporal and multi-angle soil moisture retrieval algorithms, this dataset has been proven to yield soil moisture products with higher accuracy than other SMOS brightness temperature products across multiple dense global soil moisture observation networks. Notably, the volume of valid retrieval data increases significantly in regions with severe RFI such as China. This dataset will provide more reliable data support for the development of long-time series L-band land surface parameter products, including soil moisture, freeze-thaw state, vegetation water content, aboveground biomass, and snow density.

搜集汇总

背景与挑战

背景概述

本数据集是针对SMOS卫星L波段多角度亮温数据的优化产品，专注于抑制射频干扰和视场混叠影响，覆盖2010年至2021年。它基于欧空局官方数据，通过双步回归方法重建亮温数据，并重采样至标准投影，显著提升了在射频干扰严重地区（如中国）的土壤水分反演精度和数据量，为陆表参数研发提供可靠支撑。