## 3 Innovation
### Data Innovation  
#### 3.2.1 Sensor Iteration for the Generation of a New Dataset  
Recent studies have begun to investigate the use of new types of sensors for detecting soil salinization phenomena. Over the past few decades, advancements in Earth observation technology have greatly increased the availability of remote sensing data, with continuous improvements in both temporal and spatial resolution [53]. For example, satellites such as Landsat, Sentinel, and WorldView are consistently enhancing their imaging capabilities, offering new opportunities for soil salinity inversion [54]. When utilizing image data, there is often a trade-off among temporal resolution, spatial resolution, spectral resolution, and radiometric resolution. By integrating images from various remote sensing platforms and employing different inversion techniques, it may be possible to capture crucial information for soil salinity assessments. For instance, [PERSON] et al. [55] and [PERSON] et al. [56] utilized multi-year crop reflectance data to evaluate soil salinity in the West San Joaquin Valley of the United States. This method assumes that the relationship between soil salinity and vegetation canopy reflectance remains relatively stable over a defined period (e.g., 4-7 years), allowing for effective separation from other stress factors affecting vegetation in the short term, such as water stress and pests. However, the optimal time span cannot be predetermined and necessitates a careful balance between the selected time window and local soil salinity correlations. Earlier research faced challenges due to the lower spatial resolution of available imagery, which hindered the precise capture of small-scale landscape changes, such as variations in farmland, crop distribution, and management practices [15]. The Sentinel-2 satellite sensor from the European Space Agency (ESA) provides free multispectral images with resolutions ranging from 10 m to 60 m, making it a promising choice for accurately describing soil salinity variability and landscape heterogeneity. For example, [PERSON] et al. [40] employed three years of Sentinel-2 data, along with common vegetation indices, to invert soil salinity in farmland in Lisbon, Portugal, concluding that this data could be valuable for future soil salinity monitoring projects in the region. While vegetation indices are typically derived from multispectral data, research on using high spatial resolution remote sensing imagery, such as GF-2 with a resolution of 3.2 m, for extracting features relevant to soil salinity inversion remains relatively scarce [57]. The PlanetScope satellite constellation, which offered a 3-m spatial resolution and neardaily revisit frequency, has initiated efforts toward high-precision soil salinity mapping in the Weiganhe-Kuche Oasis region of arid northwest China [58]. Spaceborne remote sensing technologies provide rapid and accurate assessment methods for soil salinity monitoring; however, there is still a gap in high spatial resolution salinity estimations. China's GF-5 satellite boasts impressive specifications, including a spectral resolution of 5 nm, 330 bands, and a signal-to-noise ratio of 700, yet its potential for soil salinity estimation remains underexplored. To investigate the applicability of GF-5 hyperspectral data for diagnosing soil salinity, [PERSON] et al. [18] proposed a strategy that integrates a guided method, fractional order derivative (FOD) technology, and a decision-level fusion model to reduce uncertainty in soil salinity estimations within the Ebinur Lake Oasis area of northwestern China. Additionally, UAV multispectral remote sensing technology has demonstrated potential for soil salinity monitoring. UAVs can carry a variety of sensors, including digital cameras, multispectral cameras, hyperspectral imagers, and LiDAR. Hyperspectral sensors onboard UAVs can capture images with ultra-high spatial resolution and offer operational flexibility, providing a fast and efficient method for soil salinity monitoring. [PERSON] et al.