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引用本文:

DOI:

10.11834/jrs.20210466

收稿日期:

2020-10-19

修改日期:

2021-04-29

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基于CYGNSS数据的土壤盐分反演方法研究:以黄河三角洲为例
王俊栋1, 孙志刚1, 杨婷2, 朱康莹1, 邵长秀1, 彭金榜1, 李仕冀1, 王玮莹1, 高祎男3, 岳焕印4
1.中国科学院 地理科学与资源研究所 生态系统网络观测与模拟重点实验室;2.中国科学院 地理科学与资源研究所 黄河三角洲现代农业工程实验室;3.中国农业大学 资源与环境学院;4.天津中科无人机应用研究院
摘要:

土壤盐碱胁迫是植物生产力低下的关键因素,也是全球盐碱区可持续发展的瓶颈;如何较为高效可靠地获取区域土壤盐分信息是需要优先解决的问题。随着全球导航卫星系统反射测量GNSS-R(Global Navigation Satellite System Reflectometry)的迅速发展,运用星载GNSS-R测量区域范围的土壤盐分成为一种可能。全球飓风导航卫星系统CYGNSS(The Cyclone Global Navigation Satellite System)作为星载GNSS-R计划的重要组成部分之一,其卫星传感器使用的L波段能够敏感地获取土壤介电常数信息,为反演土壤盐分提供了理论基础。本文以CYGNSS作为主要数据源,选取土壤盐渍化十分严重且具有典型代表性的黄河三角洲区域作为研究区域,首次探讨CYGNSS反演土壤盐分的可行性,并建立了一套土壤盐分的反演方法。首先,利用基于相干信号的双基雷达方程对 CYGNSS 数据进行计算获取地表反射率,并校正地表反射率的地表粗糙度和植被衰减效应,计算得到土壤介电常数的幅值;然后,以改进的Dobson-S土壤介电常数模型为物理模型结合土壤水分主动-被动探测卫星SMAP(Soil Moisture Active Passive Mission)土壤水分产品数据为主要的辅助数据,构建一套土壤盐分反演方法,完成2020年5月份黄河三角洲高效经济生态区的土壤盐分反演;最后,利用实测电导率数值对反演结果进行真实性检验,决定系数(R2)为0.94、均方根误差(RMSE)为1.06 mS/cm,拟合精度较高。本研究成果表明运用CYGNSS反演土壤盐分具有一定的可行性,并为区域尺度上的土壤盐分提供一种新思路。

A remote sensing method for retrieving soil salinity based on CYGNSS data: Taking Yellow River Delta as an example
Abstract:

Soil saline-alkali stress is the key factor of low plant productivity and the bottlenecks of the sustainable development in global saline-alkali areas. Obtaining the regional soil salinity information both efficiently and reliably is a necessary problem to be solved. With the rapid development of Global Navigation Satellite System Reflectometry (GNSS-R), it provides a new opportunity to use spaceborne GNSS-R to retrieve soil salinity. The Cyclone Global Navigation Satellite System (CYGNSS) is one of the important components of the spaceborne GNSS-R mission, the L-band used by CYGNSS is very sensitively to the soil dielectric constant, which provides a theoretical basis for estimating soil salinity. In this paper, CYGNSS was taken as the main data source, and the Yellow River Delta region, a typical area with extremely salinization of soil, was selected as the research religion to discuss the feasibility of soil salinity estimation by CYGNSS for the first time, and a set of soil salinity retrieval method was established. Firstly, the surface reflectance was obtained by calculating CYGNSS data of coherent signals based on the bistatic radar equation, and then the surface roughness and vegetation attenuation effects of the surface reflectance were corrected to calculate the magnitude of the soil dielectric constant. Secondly, based on the improved Dobson-S soil dielectric constant model as the physical model and Soil Moisture Active Passive Mission(SMAP) soil moisture product as the main auxiliary data, a set of soil salinity retrieval method was constructed to complete the soil salinity estimation in the Yellow River Delta High-efficiency Ecological Economic Zone in May 2020. Finally, the result was verified by the ground measured conductivity value. It was found that the soil salinity derived from CYGNSS correlated well with the ground measured conductivity ( R2= 0.94, RMSE=1.06 mS/cm). The result of this study indicates that it is feasible to use CYGNSS to estimate soil salinity, and provides a new idea for soil salinity retrieval on a regional scale.

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