However, the broad band of these images may smooth out the essential but subtle characteristics in soil spectra, resulting in the inefficiency or inefficacy of the estimation from the multispectral imagery. Hyperspectral remote sensing overcomes the bandwidth restriction of multispectral imagery. The predictive model or indexes based on its narrow and spectrally continuous bands may detect the subtle variations in soil characteristics. Numerous studies have detected the spectral characteristics of materials [17]. [PERSON] et al. [18] and [PERSON] and [PERSON] [19] found that mid-infrared spectroscopy was optimal for estimating soil carbon content, total nitrogen, and soil pH. [PERSON] et al. [20] discovered that the key wavelengths of soil pH were restricted to the spectral regions beyond 1915 nm in carbonate land. [PERSON] and [PERSON] [21] found that CO\({}_{3}^{2-}\) had the clearest absorption at 2300-2350 nm. Hyperspectral remote sensing provides near-laboratory reflectance spectra for each pixel [22] and thus has the potential to monitor saline-alkali soils accurately in a large area. For example, hyperspectral imagery was successfully applied to estimate salt content with a model constructed from a soil salinity spectral index [23]. The HyMap imagery was used to detect and map irrigation-induced salinity with methods of matched filtering and mixture-tuned matched filtering [14]. As a result of the computational difficulties of the large data size and high spectral collinearity among the narrow bands, the estimation of saline-alkali soils using all hyperspectral bands is not productive and often results in spurious results due to noises introduced from highly correlated spectral bands. The partial least squares regression (PLSR) model was developed by [PERSON] [24] in the late 1960 sfor econometrics and then introduced as a tool to analyze data in chemical [25], ecological, [26] and spectral [27] applications. PLSR is an effective linear combination of multiple predictor variables on one or multiple dependent variables [28]. The model extracts several orthogonal factors from the original variables to explore the relationships between predictor and dependent variables [26]. Thus, the PLSR model is especially useful when the variables contain random noises. The PLSR model is also suitable for the numerous and collinear predictor variables [29; 30], which often result in erratic regression coefficients. PLSR is proven an effective method to relate spectra to soil properties [31; 32]. In the process of building the PLSR model, predictor (spectra) and dependent (soil properties) variables can construct optimum linear solutions without introducing excessive noise. This study tests the feasibility of the hyperspectral mapping of soil salinity and alkalinity assisted with the PLSR model and hyperspectral imager images (HSI images) in the northern Songnen Plain, a unique black soil region in China. For this purpose, the saline-alkali and non-affected soil samples were collected, and HSI images were acquired in the study area. The objectives are as follows: (1) to explore the properties of saline-alkali soils and the corresponding sensitive spectral wavelengths; (2) to estimate soil alkalinity and salinity quantitatively with soil properties and spectral bands; (3) to map the alkalinity and salinity of saline-alkali soils.