## 3 Innovation
### Data Innovation
#### 3.2.1 Sensor Iteration for the Generation of a New Dataset
[68] calculated new salinity indices using optical bands from Sentinel-2 multispectral data and Sentinel-1 radar data (including backscattering coefficients for VV and VH) alongside bands 2-B, 3-B, 4-B, and the Enhanced Residues Soil Salinity Index (ERSSI). This index utilized the SWIR1 band, sensitive to low soil moisture and residues, instead of the NIR band, thereby enhancing the spectral sensitivity to calcium and sulfate ions under low soil moisture conditions. Different remote sensing images possess varying observation dimensions and imaging principles, leading to differences in spatial and temporal resolution, spectral band ranges, and other attributes [15]. Consequently, the information provided by multi-source remote sensing images is complementary. Vegetation indices are typically estimated using reflectance from a limited number of bands, resulting in insufficient and delayed information regarding soil salinity. To mitigate the impact of vegetation cover on salinity inversion accuracy, [PERSON] et al. [69] investigated the potential of image fusion techniques to generate synthetic soil images (SYSI) by combining multi-period raw soil images with images of bare soil pixels. This approach aimed to minimize the effect of vegetation on soil salinity inversion by integrating the salinity reproduction period with the SYSI. Different remote sensing imaging cycles capture distinct information about the ground surface during the vegetation growing season; for example, Sentinel-2 and MODIS offer higher spatial and temporal resolution than Landsat 8, although Landsat 8 provides more available optical bands for soil salinity monitoring studies. To maximize the spatial and temporal information from remote sensing images derived from various sources, some researchers have employed diverse spatial and temporal fusion strategies [70]. [PERSON] et al. [70] utilized four commonly used spatial and temporal fusion algorithms to merge Sentinel-2, Landsat 8, and MODIS images, testing the applicability of the fused images for soil salinity inversion in the Weiku Oasis Intergradient Zone in Xinjiang. The results indicated that the fused images from the four algorithms enhance salinity inversion accuracy, with subtle differences primarily observed in agricultural areas. SAR, known for its strong penetration capabilities, is less affected by clouds and rain, providing an opportunity to monitor soil salinity and improve inversion accuracy across a broader range of temporal and climatic conditions. [PERSON] et al. [71] explored the fusion of Sentinel-1 and Sentinel-2 images, investigating the potential application of SAR combined with multi-spectral satellite images in salinity inversion. The correlation between the fused images and soil salinity improved, with bands 4, 5, and 8 showing significant correlation with measured salinity. While ground spectroscopy enables accurate soil salinity analysis, this method was not suitable for regional inversion. Therefore, achieving the goal of obtaining high-precision soil salinity information rapidly over large areas remains a challenge. [PERSON] et al. [15] investigated a novel method for fusing regional ground spectra with Sentinel-2 MSI image data using differential coefficients. In contrast to correction coefficients based on the average values of samples, differential coefficients enabled more accurate correction and fusion by leveraging the differences among samples. This approach offered a fresh perspective for monitoring soil salinity.