# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 4 Discussion  
We established soil salinity inversion models for different soil moisture levels to evaluate the influence of spatial heterogeneity of soil moisture on the accuracy of soil salinization inversion. The proposed salinity models based on drought levels provide more accurate classification results than traditional soil salinity models. Many studies have found an advantage in using remote sensing data to estimate soil salinity due to lithological and meteorological settings [29]. In the current study, we investigated the influence of soil moisture on the spectral characteristics of remote sensing images. Similar to the results reported by [PERSON] [30], soil salinity was underestimated when the soil relative humidity was higher than 50%. The likely reason is that the high soil moisture content affects the spectral reflectance of the optical remote sensing images. We observed underestimations of soil salinity in high-moisture regions, as expected. In contrast, we found a significant overestimation of soil salinity in regions with soil relative humidity less than 40%. Oversaturation was observed primarily in areas with bare soil and low vegetation density [31]. Therefore, soil moisture and the associated vegetation conditions can bias salinity assessment models.  
During the process of constructing soil moisture model, we found the soil relative humidity decrease gradually with the increasing of TVDI (Figure 5). The TVDI derived from the Landsat TM-8 OLI is highly correlated with the gauged soil moisture data which indicate that it is reasonable to obtain the spatial distribution characteristic of the soil humidity based on the remote sensing imagery and a soil moisture model. We also noticed that some training plots are not coherence with the simulated quadratic curve. That is partly because the bias between the locations of field survey soil moisture data with the correspondent pixels in the remote sensing imagery. The difference time of field survey and images derivations may also influence the in simulated results of the soil relative humidity. However, the TVDI derived from the remote sensing imagery could reflect the spatial distribution characteristics of the soil moisture in a large extent.  
We believe that our approach of emphasizing the impact of environmental factors on the inversion accuracy of soil salinity can improve the inversion accuracy of estimating soil properties from remote sensing data using limited field sampling. However, soil salinity is a complex process that integrates many environmental factors, such as soil type, soil retention capacity, vegetation phenology and so on. There are different soil types in the Songnen Plain, i.e., black soil, chernozem, meadow soil, dark brown soil etc. The spatial distribution of soil moisture are influenced by the different soil retention capacity and soil water suction for various soil types. However, the relationship between each element is still not clear enough. In the following research, we should further integrate the soil factors, the vegetation phenological factors, the agricultural production intervention factors and other factors to provide more accurate soil salinity data.