# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 1 Introduction  
Soil salinization describes the process of soluble salts accumulating on the soil surface. It is the most common type of land degradation in arid, semi-arid, and sub-humid areas, representing an environmental issue of common concern worldwide and a source of ecological problems. Soil salinization is the result of a combination of natural conditions (such as hydrology, topography, and climate), farming practices, and irrigation practices. It typically occurs in areas with dry climates, low rainfall, high surface evaporation, and high levels of soluble salts. Elevated levels of soluble salts (such as Na\({}^{+}\), Cl\({}^{-}\)) in the soil are a major threat to agricultural production worldwide [1, 2]. Even in small amounts, soluble salts can cause a decline in soil fertility and inhibit the growth of crops, resulting in greatly reduced crop yields [3]. In addition, the pH value of the soil indicates the intensity of soil salinity, regulating the abundance of toxic substances such as aluminum and manganese and essential nutrients, e.g., phosphorus, calcium, magnesium, and potassium [4]. Soil salinity resulted in taking land out of agricultural production, a decline in animal husbandry, the deterioration of the ecological environment, and adverse impacts on the economy and social development. Remote sensing techniques have several advantages over traditional field sampling and electromagnetic induction methods for measuring soil salinity, such as low cost, large extent, and repeated coverage. Many techniques have been used to measure the spatial distribution of soil salinity using multi-source satellite images, such as MODIS, Landsat 8-OLI, Sentinel-1 A/B, and Hyperion [3, 5, 6]. For example, [PERSON] et al. established a soil salinity model in the western San Joaquin Valley (WSJV), California, USA, based on time-series data [3]. [PERSON] et al. developed a regional soil salinity prediction model using multiple linear regression, Landsat7 ETM+ canopy reflectivity, and the canopy response salinity index (CRSI) [7]. [PERSON] et al. produced accurate regional salinity maps using partialleast-squares regression (PLSR) and spectral indices derived from Sentinel-2 multi-spectral instrument (MSI) data [6]. Previous studies found that many environmental factors influenced the spectral characteristics of saline and alkaline lands, such as soil organic matter, soil type, land use, rainfall, temperature, evapotranspiration, and vegetation cover [8; 9; 10; 11; 12]. Many studies have integrated these factors with the spectral features of remote sensing imagery to create salinity inversion models and improve the accuracy of salinity monitoring. [PERSON] et al. developed an empirical salinity simulation model using spectral features, precipitation, evapotranspiration, and a digital elevation model (DEM) [13]. [PERSON] et al. established an improved surface salinity model consisting of spectral indices, spatial location, and land cover type [14]. These study showed that the accuracy of salinization inversion have been affected by environmental indicators such as climate, evapotranspiration, and groundwater [15; 16]. The spatial heterogeneity of soil moisture is a manifestation of the changes in these environmental indicators, which will significantly affect the spectral characteristics of remote sensing images and the accuracy of soil salinity inversion results [17; 18]. However, few studies focus on the influences of the spatial heterogeneity of soil moisture on the inversion accuracy of the soil salinity. As far as we known, this is the only study to construct a quantitative retrieval model of soil salinity according to the gradients of soil moisture and discuss the influences of the spatial variation of the soil moisture on the uncertainty of the soil salinity inversion based on field gauged samples. The main objective of the current study is to analyze the influence of the spatial heterogeneity of soil moisture on the inversion accuracy of soil salinity.