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

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

  • Email：wangjd.20b@igsnrr.ac.cn
  • Introduction：王俊栋，研究方向为生态遥感与区域生态。E-mail：wangjd.20b@igsnrr.ac.cn
WANG Jundong13,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

    Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China

SUN Zhigang1234,  
  • role： Corresponding author通信作者
  • Affiliation：

    Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100101, China

    Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China

  • Email：yangt@igsnrr.ac.cn
  • Introduction：杨婷，研究方向为GNSS遥感。E-mail：yangt@igsnrr.ac.cn
YANG Ting24*,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

ZHU Kangying13,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100101, China

    Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China

SHAO Changxiu124,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

PENG Jinbang13,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

LI Shiji13,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

WANG Weiying13,  
  • Affiliation：

    College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

GAO Yinan5,  
  • Affiliation：

    Institute of UAV Application Research, Tianjin and CAS, Tianjin 301800, China

YUE Huanyin6

resumen

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 (R2) 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.

palabra clave

remote sensing;soil salinity;CYGNSS;the bistatic radar equation;dielectric constant;Yellow River delta

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