## 4 Discussion
### Spectral Sensitivity of HSI Image to Soil Alkalinity and Salinity  
Normally, soil reflectance is inevitably affected by particle size, minerals, moisture, color and organic matter [15; 46]. For the air-dried and sieved soil used in the PLSR model, minerals and organic matter were the primary influences. The typical zonal soils are black soil and chernozem that are characterized with high soil organic matter [47]. According to [PERSON] [48], organic matter was one of the important influencing factors of pH and color association. The alkaline soil has high pH and low organic matter, which was in grayish color. The short VIS spectra can optimally detect the soil color and reflect the variation-related soil color more effectively than longer wavelengths [49; 50]. As a result, the PLSR models based on the HSI-resampled spectra from 505 nm to 956 nm to estimate soil pH exhibited good performance in this study. Similar results are reported in other studies [51; 52; 34]. Figure 4: Alkaline and saline classifications of soils in accordance with pH-EC levels. This case can be attributed to (1) the good correlation between the soil alkalinity and soil color; and (2) a decrease in the soil reflectance with increasing soil alkalinity in the VIS-NIR wavelengths. Compared with our previous research [16], the wavelengths of HSI images (505-956 nm) included the optimal band of OLI image for estimating soil pH. So, the PLSR models constructed with these wavelengths did well in estimating soil pH. There were other salts contribute to soil salinity (EC) in the study area [16], which were not related to soil color and reflectance when the salts were not accumulated in soil surface. However, the wavelengths of HSI image (505-956 nm) didn't include the optimal band of OLI image for estimating soil EC. Moreover, samples collected in new sampling sites were used to build PLSR models. High soil salinity is associated with the presence of sodium carbonate or sodium bicarbonate (i.e., maximum EC = 153.00 dS/m) in the soil of this study area, which can be represented as absorption troughs near 1930 and 2000 nm. The absence of the two absorption troughs might be attributed to the low performance of EC estimation. So, the accuracy of the PLSR model in estimating soil EC was much lower than that in estimating soil pH, as well as soil EC estimating in our previous research [16]. For these reasons, the wavelengths of HSI images cannot be better used to construct PLSR models to detect soil salinity in this study. Using HSI images to estimate soil alkalinity and salinity can be attributed to the sensitivity of spectral reflectance to these soil properties, especially in certain wavelengths. Past studies [53] have used the PLSR regression coefficients to approximate the strength of sensitivity for each soil factor, such as soil pH [35; 51; 54] and salts [27]. In this study, three HSI bands in green, red, and NIR regions had the most important contribution to soil pH and EC estimation. Green band 21 (band center: 505.88 nm), NIR band 108 (band center: 896.89 nm), and red band 76 (band center: 696.84 nm) were sensitive to pH estimation. Green band 21 (band center: 505.88 nm), red band 73 (band center: 682.78 nm), and NIR band 109 (band center: 904.88 nm) were sensitive to EC estimation. These bands were between 350-1300 nm wavelengths, which were related to the absorption and reflectance features resulted from organic matter content of black soil and saline-alkali soils [55; 56] in Songnen Plain. Therefore, it is possible to estimate soil alkalinity and salinity from the HSI image. Based on HyMap, [PERSON] et al. [27] successfully constructed an artificial neural network (ANN) and PLSR with narrow bands in the range of 400-2450 nm to map soil salinity. On the basis of multispectral sensor data in the visible, near infrared and short wavelength infrared regions, multivariate adaptive regression splines (MARSs) and PLSR [50; 57; 58] were successfully used to map soil salinity.