## 3 Innovation
### Data Innovation
#### 3.2.1 Sensor Iteration for the Generation of a New Dataset
[59] utilized UAV multispectral imagery to map soil salinity in farmlands within the Hetao Irrigation District, suggesting that combining UAV optical imagery with ML algorithms holds significant promise for the quantitative estimation of soil properties. Although UAVs have been extensively employed in recent years for crop mapping, forest monitoring, and vegetation feature extraction, research on soil salinity monitoring and mapping using UAV hyperspectral images remains limited. [PERSON] et al. [60] argued that spaceborne remote sensing images often fail to achieve an optimal balance between spatial and spectral resolution, a deficiency that UAV hyperspectral imagery can address. Their team used UAV hyperspectral imagery in conjunction with near-surface sensor EMI data to conduct soil salinity inversion research in the eastern interlaced zone of the Aksu Oasis, demonstrating that airborne hyperspectral data outperformed GF-2 data in salinity inversion accuracy across three different land types (bare land, moderate vegetation cover, and high vegetation cover). Beyond optical remote sensing data, the Cyclone Global Navigation Satellite System (CYGNSS) offers a novel perspective for soil salinity inversion. CYGNSS satellites utilize signals from the Global Positioning System (GPS) to indirectly reflect soil moisture conditions by re-encoding microwave signals reflected from the Earth's surface. [PERSON] et al. [22] proposed a Soil Electrical Conductivity (EC) inversion model (CIG) based on the physical parameters of the incoherent component of the CYGNSS L-band scattering signal and validated it in the Yellow River Delta region of China. The prediction results showed that the CIG model was better than the optical index model. The model integrates the effects of vegetation, surface roughness, and auxiliary variables related to soil EC on Fresnel reflectivity and explores the interaction between the incidence angle of CYGNSS signal and soil EC. 2.2 Development of Novel Data Conversion Methods for Generating Indices or Images That Are Sensitive to Soil Salinity  
The development of novel data conversion methods aimed at generating indices or images that are sensitive to soil salinity represents a critical objective in the domain of soil salinity monitoring [30]. Rapid salinity monitoring using vegetation index models has become routine; however, the influence of hidden environmental and confounding factors within the vegetation response spectra cannot be completely eliminated [61]. These factors include varying geographic locations, soil types, vegetation, and conditions of saline soils. The capability of vegetation indices to estimate salinity was often inadequate, leading to increased uncertainty in soil salinity inversion results [62], as demonstrated in numerous case studies across different regions [63; 64]. To address the salt sensitivity issue of vegetation indices, researchers were focused on identifying more suitable local salt-sensitive bands to develop new spectral indices or mathematically adjust existingindices to extract more effective information regarding local vegetation physiology and soil environments, thereby improving inversion accuracy [65; 66]. For instance, environmental factors such as surface vegetation cover, straw cover, and regional soil types provided additional information that diminishes the correlation with soil salinity, rendering conventional vegetation indices--sensitive to moisture and canopy--less indicative of salinity. [PERSON] et al. [67] constructed two-dimensional (2D) and three-dimensional (3D) salinity indices using differential preprocessed Landsat 8 and Sentinel-2 images. The 3D index demonstrated a stronger correlation with the salinity of Lake Ebi in Xinjiang compared to the 2D index. To tackle the unsatisfactory inversion accuracy of surface soil salinity with vegetation residue post-harvest in agricultural fields in Alar, Xinjiang, [PERSON] et al.