CYGNSS土壤盐分反演方法研究：以黄河三角洲为例
A remote sensing method for retrieving soil salinity based on CYGNSS： Taking the Yellow River Delta as an example
- 2023年27卷第2期 页码：351-362
纸质出版日期： 2023-02-07
DOI： 10.11834/jrs.20210466
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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（
R
2
）为0.88、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。
Abstract
Soil saline-alkali stress is the key factor of low plant productivity and the bottleneck of sustainable development in global saline-alkali areas. Obtaining regional soil salinity information both efficiently and reliably is a necessary problem to be solved. The rapid development of global navigation satellite system reflectometry (GNSS-R) 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
and the L-band used by CYGNSS is very sensitive 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 extreme salinization of soil
was selected as the research religion to discuss the feasibility of soil salinity estimation by CYGNSS for the first time. A set of soil salinity retrieval methods was established.
We proposed a physical model that took CYGNSS as the main data with some other auxiliary data fused. First
the surface reflectance was obtained by calculating the 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. Second
based on the improved Dobson-S soil dielectric constant model as the physical model and the Soil Moisture Active Passive Mission (SMAP) soil moisture product as the main auxiliary data
a set of soil salinity retrieval methods 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 retrieved from the CYGNSS data correlated well with the ground-measured conductivity
with a coefficient of determination (
R
2
) equal to 0.88 and a Root Mean Squared Error (RMSE) equal to 1.06 mS/cm. Therefore
a high-precision soil salinization map of the Yellow River Delta was made by kriging interpolation based on the estimation result
which showed an obvious trend that soil salinity decreased gradually from coastal to inland at the regional scale with a strong spatial heterogeneity itself.
In this paper
a physical model for soil salinity estimation based on the bistatic radar equation and dielectric constant model was proposed using CYGNSS as the main data source. The results of this study indicated that it is feasible to use CYGNSS to estimate soil salinity and proved the sensitivity of the L-band to soil salinity
providing a new idea for soil salinity retrieval on a regional scale. In future studies
the method of multisource data fusion can be considered to transform the estimation results from point data to planar area for expression
and a new validation method of high precision is needed due to the strong spatial heterogeneity of soil salinity.
关键词
遥感土壤盐分CYGNSS双基雷达方程介电常数黄河三角洲
Keywords
remote sensingsoil salinityCYGNSSthe bistatic radar equationdielectric constantYellow River Delta
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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
中国农业大学 资源与环境学院
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