River discharge is of great significance in the development of water resources and ecological protection. There are several large ungauged catchments around the word still lacking sufficient hydrological data. Obtaining accurate hydrological information from these areas is an important scientific issue. New data and methods must be used to address this issue. In this study, a new method that couples unmanned aerial vehicle (UAV) data with the classical slope–area method is developed to calculate river discharges in typical ungauged catchments. UAV data is used to obtain topographic information of the river channels. In situ experiments are carried out to validate the river data. Based on slope–area method, namely the Manning–Strickler formula (M–S), Saint-Venant system of equivalence (which has two definitions, S-V-1 and S-V-2), and the Darcy–Weisbach equivalence (D–W) are used to estimate river discharge in ten sections of the Tibet Plateau and Dzungaria Basin. Results show that the overall qualification rate of the calculated discharge is 70% and the average Nash–Sutcliffe efficiency coefficient is 0.97, indicating strong practical application in the study area. When the discharge is less than 10 m³⁄s, D–W is the most appropriate method; M–S and S-V-1 are better than other methods when the discharge is between 10 m³⁄s and 50 m³⁄s. However, if the discharge is greater than 50 m³⁄s, S-V-2 provides the most accurate results. Furthermore, we found that hydraulic radius is an important parameter in the slope–area method. This study offers a quick and convenient solution to extract hydrological information in ungauged catchments.

Calcium is an essential macronutrient in soils and plays an important role in the structure and function of an ecosystem. In this study, we selected the Huajiang dry-hot valley region in southwest China as our research object, aiming to comprehend the soil calcium distribution characteristics of different altitudes and vegetation types in this karst dry-hot valley region. The results showed that the mean value of total soil calcium content in the karst dry-hot valley was 13.00 ± 3.28 g·kg⁻¹, and the mean value of the proportion of exchangeable calcium content to total calcium was 50.31%. In the vertical profile, total soil and exchangeable calcium contents decreased with increasing soil depth. With increasing altitude, total soil and exchangeable calcium contents increased. Among the different vegetation types, the total and exchangeable calcium contents of crops were higher than the three natural vegetation types of forest, scrub, and grassland, and the soil calcium content of forest was the lowest. Total soil and exchangeable calcium content in the karst dry-hot valley were affected by rock exposure rate, vegetation coverage, soil thickness, soil organic matter and soil pH. In addition, the unique environmental gradient characteristics at different elevations in the dry-hot valley area may redistribute soil calcium, and the positive vegetation succession may cause a gradual decrease of soil calcium content in the area.

Research into global water resources is challenged by the lack of ground-based hydrometric stations and limited data sharing. It is difficult to collect good quality, long-term information about river discharges in ungauged regions. Herein, an approach was developed to determine the river discharges of 24 rivers in ungauged regions on the Tibetan Plateau on a long-term scale. This method involved coupling the Manning–Strickler formula, and data from an unmanned aerial vehicle (UAV) and the Gaofen-2, SPOT-5, and Sentinel-2 satellites. We also compared the discharges calculated by using the three satellites’ data. Fundamental information about the rivers was extracted from the UAV data. Comparison of the discharges calculated from the in-situ measurements and the UAV data gave an R² value of 0.84, an average NSE of 0.79, and an RMSE of 0.11 m³/s. The river discharges calculated with the GF-2 remote sensing data and the in-situ experiments for the same months were compared and the R², RMSE, and the NSE were 0.80, 1.8 m³/s, and 0.78, respectively. Comparing the discharges calculated over the long term from the measured in-situ data and the SPOT-5 and Sentinel-2 data gave R² values of 0.93 and 0.92, and RMSE values of 2.56 m³/s and 3.16 m³/s, respectively. The results showed that the GF-2 and UAV were useful for calculating the discharges for low-flow rivers, while the SPOT-5 or the Sentinel-2 satellite gave good results for high-flow river discharges in the long-term. Our results demonstrate that the discharges in ungauged tributaries can be reliably estimated in the long-term with this method. This method extended the previous research, which described river discharge only in one period and provided more support to the monitoring and management of the tributaries in ungauged regions.

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg⁻¹) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R²), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.