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FY-3E GNOS-R 全球土壤水分监测示意图。

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring.

机构信息

Key Laboratory of Space Weather, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China.

Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China.

出版信息

Sensors (Basel). 2023 Jun 22;23(13):5825. doi: 10.3390/s23135825.

DOI:10.3390/s23135825
PMID:37447675
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10347166/
Abstract

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm/cm. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

摘要

本文提出了一种适用于 FY-3E（风云三号 E 星）GNOS-R（GNSS 掩星探空仪 II-反射计）的有效的土壤水分反演方法。文中采用了完全针对 GNOS-R 设计的 LAGRS 模型来估计植被和地表粗糙度对地表反射率的影响。由于 LAGRS（陆面 GNSS 反射模拟器）模型是基于微波辐射传输方程模型的星载 GNSS-R（GNSS 反射计）模拟器，因此本文所提出的方法在反演过程中更多地考虑了物理散射特性。最终算法的开发还研究了 SMAP（土壤水分主动被动）的辅助信息，如植被含水量和粗糙度系数。首先，计算 GNOS-R 的 SR（地表反射率）数据并进行校准，然后获取植被粗糙度因子并消除两方面因素的影响。接收到菲涅尔反射率后，即可从该方法中反演出相应的土壤水分。结果表明，GNOS-R 数据反演的土壤水分与 SMAP 土壤水分具有很好的一致性，相关系数为 0.9599，均方根误差为 0.0483cm/cm。该方法成功地实现了全球尺度的土壤水分估算，并且基于之前开发的物理 LAGRS 模型。这展示了 GNOS-R 在土壤水分估算方面的巨大潜力。

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本文引用的文献

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