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• Two-dimensional space theory is applied in soil salt content inversion. • Sentinel-1 and Sentinel-2 data are fused by two-dimensional space theory. • Sentinel-1 after H/a polarization is applied in soil salt content inversion. Multi-spectral satellite remote sensing is widely used in soil salinity monitoring by virtue of its wide coverage, fast update and low cost, but it is susceptible to cloudy weather, limiting the accuracy and application conditions of the inversion, while SAR (synthetic aperture radar) is hardly affected by cloud with strong penetration ability and enables almost round-the-clock, climate-wide monitoring of soil salinity. Two-dimensional feature space model, being easily interpretive and widely used in soil salinity monitoring, requires only some easily extracted parameters and can reduce other influences on soil salinity inversion. Therefore, in order to take full advantages of the SAR and multi-spectral satellite, achieve monitoring of soil salinity in more climatic conditions and longer time, and further improve soil salinity monitoring accuracy, this paper combine SAR-Spectral data with two-dimensional space theory to construct two-dimensional models for monitoring soil salinity. In this study, two-dimensional space models are constructed using Sentinel-1 and Sentinel-2 feature parameters, and models are evaluated with R2 with measured soil salinity data of the Shahaoqu irrigation area in Hetao irrigation district, Inner Mongolia. The results show that it is feasible to use the parameters extracted from Sentinel-1 after H/a polarization for constructing two-dimensional feature space models for the soil salinity inversion(R2 parameters of Sentinel-1 = 0.467,0.587,0.517), and the two-dimensional space models combining Sentinel-1/2 parameters are more accurate and stable in soil salinity monitoring than those using Sentinel-1 or Sentinel-2 alone((R parameters of Sentinel-2 = 0.320,0.517,0.341),(R2 parameters of Sentinel-1/2 = 0.507,0.545,0.562,0.512,0.557,0.568)). [ABSTRACT FROM AUTHOR]

• Radar image and spectral images were fused for monitoring soil salinity. • Soil salinity inversion models were built based on Sentinel-1/2 fusion and machine learning. • GS was used to fuse images of Sentinel-1 and Sentinel-2. In the application of satellite remote sensing data to salinity inversion in bare soil, multispectral satellites enjoy richer wavelength bands compared with synthetic aperture radar (SAR), but their vulnerability to cloudy weather limits the inversion accuracy. In contrast, SAR has stronger penetration, is less affected by cloudy weather, and can work around the clock. Therefore, in order to monitor soil salinity over a longer period of time and under various climatic conditions, and to further improve the accuracy of salinity inversion, we fused Sentinel-1 and Sentinel-2 images and investigated the feasibility of SAR and multispectral satellite image fusion in salinity inversion in this paper. And we conducted experiments in Hetao Irrigation Area, Inner Mongolia, China. Gram-Schmidt(GS) was used to fuse Sentinel-1 and Sentinel-2 images at the same time, and maximum-normalization was carried out on the DN value of the fused image. Then some remote indices conducted with the normalized DN, together with high relevant fused single bands were input into Back Propagation(BP), Support Vector Machine(SVM), Random Forest(RF) as independent variables after two-tailed significance test and Variable importance in projection(VIP) selection, for soil salinity inversion model construction. Finally, the inversion results of the study area were evaluated with R2, RMSE, RPD, RSS, and RPIQ. The results of this study showed that the bare soil salinity inversion based on the fusion of VV(Sentinel-1) and Sentinel-2 had a higher accuracy than that on the fusion of VH (Sentinel-1) and Sentinel-2 image and that without the fusion. Among the three machine learning models, random forest (RF) achieved the most satisfying results (R2 = 0.801, RMSE = 0.686), followed by SVM(R2 = 0.624, RMSE = 0.875) and BP(R2 = 0.613, RMSE = 0.918). This study demonstrated the feasibility of fusing Sentinel-1 radar images with Sentinel-2 multispectral images to improve the accuracy of soil salinity inversion, and constructed the soil salinity inversion model based on machine learning and GS fusion. [ABSTRACT FROM AUTHOR]