Water Resource Sustainability Management plays a vitally important role in ensuring sustainable development, especially in water-stressed arid regions throughout the world. In order to achieve sustainable development, it is necessary to study and monitor the water quality in the arid region of Central Asia, an area that is increasingly affected by climate change. In recent decades, the rapid deterioration of water quality in the Ebinur Lake basin in Xinjiang (China) has severely threatened sustainable economic development. This study selected the Ebinur Lake basin as the study target, with the purpose of revealing the response between the water quality index and water body reflectivity, and to describe the relationship between the water quality index and water reflectivity. The methodology employed remote sensing techniques that establish a water quality index monitoring model to monitor water quality. The results of our study include: (1) the Water Quality Index (WQI) that was used to evaluate the water environment in Ebinur Lake indicates a lower water quality of Ebinur Lake, with a WQI value as high as 4000; (2) an introduction of the spectral derivative method that realizes the extraction of spectral information from a water body to better mine the information of spectral data through remote sensing, and the results also prove that the spectral derivative method can improve the relationship between the water body spectral and WQI, whereby R² is 0.6 at the most sensitive wavelengths; (3) the correlation between the spectral sensitivity index and WQI was greater than 0.6 at the significance level of 0.01 when multi-source spectral data were integrated with the spectral index (DI, RI and NDI) and fluorescence baseline; and (4) the distribution map of WQI in Ebinur Lake was obtained by the optimal model, which was constructed based on the third derivative data of Sentinel 2 data. We concluded that the water quality in the northwest of Ebinur Lake was the lowest in the region. In conclusion, we found that remote sensing techniques were highly effective and laid a foundation for water quality detection in arid areas.

The surface water extraction algorithm based on satellite remote sensing images is advantageous as it is able to obtain surface water information in a relatively short time. However, when it is used to extract information on surface water in large-scale, long-time series and complex terrain areas, there will be a large number of misclassified pixels, and a large amount of image preprocessing work is required. The accuracy verification is time-consuming and laborious, and the results may not be accurate. The complex climatic and topographic conditions in Bosten Lake Basin make it more difficult to monitor and control surface water bodies. Therefore, based on the GEE (Google Earth Engine) cloud platform, and the studies of the effect of nine kinds of water indexes on the surface water extraction in Bosten Lake Basin, this paper adds a slope mask to remove misclassified pixels and finds the best extraction method of surface water extraction in the basin by means of accuracy verification and visual discrimination through continuous iteration of index threshold and slope mask threshold. The results show that when the threshold value is −0.25 and the slope mask is 8 degrees, the index WI2019 has the best effect on the surface water information extraction of Bosten Lake Basin, effectively eliminating the interference of shadow and snow. The effect of water extraction in the long-time series is discussed and it was found that the precision of water extraction in the long-time series is also better than other indexes. The effects of various indexes on surface water extraction under complex terrain are compared. It can quickly and accurately realize the long-time series of surface water extraction under large-area complex terrain and provides useful guiding significance for water resources management and allocation as well as a water resources ecological assessment of Bosten Lake Basin.

Nitrogen overload is one of the main reasons for the deterioration of surface water quality. Hence, monitoring nitrogen loadings is vital in maintaining good surface water quality. Increasingly, the use of spectral reflectance to monitor nitrogen concentration in water has shown potentials, but it poses some problems. Therefore, it is necessary to explore new methods of quantitative monitoring of nitrogen concentration in surface water. In this paper, hyperspectral data from surface water in the Ebinur Lake watershed are used to select sensitive bands using spectral transformation, the spectral index, and a coupling of these two methods. The particle swarm optimization support vector machine (PSO-SVM) model, constructed on the basis of sensitive bands, is used quantitatively to estimate the total nitrogen concentration in surface water and subsequently to verify its accuracy. The results show that the bands near 680, 850, and 940 nm can be used as sensitive bands for estimation of the total nitrogen concentration of surface water in arid regions. Compared with the best estimation models constructed by sensitive bands selected using the spectral transformation or the spectral index alone, the best model based on the coupling of these two measures is more accurate (R² = 0.604, Root Mean Square Error (RMSE) = 1.61 mg/L, Residual Prediction Deviation (RPD) = 2.002). This coupling method leads to a robust, accurate, and strong predictability model, and can contribute to improved quantitative estimation of water quality indexes of rivers in arid regions.

Bosten Lake is a crucial water source in arid northwest China, which has been maintaining the ecological balance of the southern Xinjiang region. It contributes to the sustainable development of the local economy and watershed ecology. Insufficient hydrological data for the basin, however, cause uncertainty in hydrological modeling and makes it difficult to calculate water availability and lake-water storage using conventional methods. Therefore, this article proposed a novel method to retrieve the lake’s water depth from Landsat-8 OLI and ICESat-2 satellite data using neural network (NN) model. Specifically, the Rayleigh-corrected top-of-atmosphere (TOA) reflectance ( $\rho _{\mathrm {rc}}$ ) in the 443–2300 nm range was employed as the input to the NN model for the retrieval of water depth. This avoids the requirement to correct the effects of aerosols. In addition, the water depth retrieved by the NN model was compared with a conventional dual-band ratio model (DBRM). To evaluate the accuracy of the corrected ICESat-2 photon data, the in situ water depths were compared with the corrected ICESat-2 photon data (considering water-level variations at different times), and 20 in situ water depth values near the ICESat-2 track were selected for verification. The results showed that 1) the in situ water depth and the corrected ICESat-2 photon data had the coefficient of determination ( $R^{2}$ ) values of 0.94; 2) NN inversion of bathymetry presented a feasible method of obtaining regional bathymetry, with $R^{2}$ of 0.87, root mean square error (RMSE) being 1.17 m, and mean absolute error (MAE) being 0.26 m. In contrast, DBRM generated the $R^{2}$ , RMSE, and MAE values of 0.74, 1.62, and 0.93 m, respectively; and 3) the water storage in Bosten Lake’s from April to September in 2022 ranged from $6.73\times 10^{9}$ to $7.50\times 10^{9}\,\,\text{m}^{3}$ . The findings can provide a strong scientific basis for the sustainable development and the rational allocation regional water resources.

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