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With the development of human society, the balance between the minimum ecological instream flow requirement (MEIFR), which is an essential part of the ecological water demand in arid areas, and anthropogenic water depletion has received increasing attention. However, due to the lack of hydrological station data and river information on arid basins, previous researchers usually considered only the individual ecological water demand of rivers, lakes, or oases. To address this issue, a new method that combines river hydraulic parameters and the wet circumference obtained by an unmanned aerial vehicle (UAV) and remote sensing hydrological station (RSHS) technologies was applied to obtain the MEIFR and, then, systematically and quantitatively explore the balance from the perspective of the entire basin of Aiding Lake from 1990 to 2022, which is the lowest point of Chinese terrestrial territory. The results showed the following: (1) since 1990, the discharge of the seven rivers in the study area increased by 1–6%, and the MEIFR of these rivers increased by 15–100%; both quantities decreased by 3–5% from the upper to the lower reaches of the basin; (2) the surface area and water level of Aiding Lake decreased by 5% and 14%, respectively, but the MEIFR first decreased by 25% from 1990 to 2013 and, then, increased by 66.7% from 2013 to 2022; and (3) from 2011 to 2022, the MEIFR and anthropogenic water depletion exhibited a balance. Against the background of climate change, this research revealed that the MEIFR of the rivers in the Aiding Lake Basin have shown an upward trend over the past 30 years and quantitatively determined the above balance relationship and the period of its occurrence. This study supplied a method that could provide guidance for water resource management by decision-makers at a global level, thus helping achieve the sustainable development goals (SDGs). [ABSTRACT FROM AUTHOR]

The evolution of a glacial lake is a true reflection of glacial and climatic change. Currently, the study of glacial lakes in the Altai Mountains is mainly concerned with the application of high-resolution remote sensing images to monitor and evaluate the potential hazards of glacial lakes. At present, there is no rapid and large-scale method to monitor the dynamical variation in glacial lakes in the Altai Mountains, and there is little research on predicting its future tendency. Based on the supervised classification results obtained by Google Earth Engine (GEE), combined with an analysis of meteorological data, we analyzed the spatial and temporal variations in glacial lakes in the Altai Mountains between 2000 and 2020, and used the MCE-CA-Markov model to predict their changes in the future. According to the results, as of 2020, there are 3824 glacial lakes in the Altai Mountains, with an area of 682.38 km2. Over the entire period, the glacial lake quantity growth rates and area were 47.82% and 17.07%, respectively. The distribution of glacial lakes in this region showed a larger concentration in the north than in the south. Most glacial lakes had areas smaller than 0.1 km2, and there was minimal change observed in glacial lakes larger than 0.2 km2. Analyzing the regional elevation in 100 m intervals, the study found that glacial lakes were predominantly distributed at elevations from 2000 m to 3000 m. Interannual rainfall and temperature fluctuations in the Altai Mountains have slowed since 2014, and the trends for the area and number of glacial lakes have stabilized. The growth of glacial lakes in both number and surface area is expected to continue through 2025 and 2030, although the pace of change will slow. In the context of small increases in precipitation and large increases in temperature, in the future, glacial lakes with faster surface area growth rates will be located primarily in the southern Altai Mountains. [ABSTRACT FROM AUTHOR]

The potential impact of natural factors on the runoff of intermittent rivers and ephemeral streams (IRES) has been largely ignored in the Tarim Basin, China. A representative example is the Keriya River. To quantify the long-term dynamic variations in lower reach surface runoff of IRES, river length, defined as the distance between a selected fix point along the perennial river segment to its dynamic, ephemeral end, was used as an indicator. Using a total of 272 remote sensing images, we digitized and measured the distance (river length) between the center of Yutian County and the river's end point on each image, and then calculated monthly inter-annual and intra-annual variations in length of the lower Keriya River from 2000 to 2019. Hydrometeorological data were combined with descriptors of anthropogenic disturbances to assess the relative influence of natural factors and anthropogenic disturbances on lower reach river runoff. The results showed that intra-annual variations in river length fluctuated seasonally, with the minimum value occurring in June; two main peaks occurred in March and August. The minimum June value in river length was closely linked to an increase in agricultural water demand and a decrease in upper reach runoff. The August peak in river length was related to the peak values in upper reach runoff and agricultural water demand; upper reach runoff made a significant contribution because the former was about 20% more than the latter in summer. The March peak corresponded to elevated lower reach groundwater levels and to the melting of ice along river channels. Inter-annual variations in river length were due to inter-annual variations in upper reach runoff and middle reach agricultural water use which increased slightly during the study period. Inter-annual variations in frequency and amplitude of the fluctuations in river length were mainly controlled by changes in upper reach runoff. The minimum in river length in 2009 was consistent with the low in upper reach runoff of the Keriya River and other rivers in the Tarim Basin. The most significant factors controlling variations in river length are natural in origin. [ABSTRACT FROM AUTHOR]

• Spectral, texture, and structural information from UAV-based multispectral image were used to estimate SPAD values in leaves of walnut. • The accuracy of SPAD values estimation model constructed by combining remote sensing indices (spectral indices (SI), texture indices (TI) and structural indices (STI) are combined in pairwise or in full) is better than that of single remote sensing indices. • XGBoost model that combines spectral, texture, and structural indices provided the highest accuracy of SPAD values estimation. An important measure to describe the physiological status of vegetation is the chlorophyll concentration of the vegetation. For managing fields in precision agriculture, monitoring walnut growth, and estimating production, accurate determination of chlorophyll content is crucial. Spectral indices play a crucial role in the non-destructive and efficient monitoring of crop physiological parameters, especially in estimating chlorophyll content. However, spectral indices have low sensitivity to high chlorophyll levels and are susceptible to interference from background signals, which may result in decreased stability of the model. The unmanned aerial vehicle (UAV) captures high-resolution images that contain abundant spatial information, including texture and structural information. These spatial information can reflect crop canopy structure, may help to improve the estimation precision of crop chlorophyll content. However, research on utilizing drone-based spatial information for estimating crop chlorophyll content is relatively limited. The aim of this study is to explore the potential of integrating spectral, textural, and structural information to improve the accuracy of walnut leaf chlorophyll content estimation. This study used a drone equipped with a multispectral camera to capture images of walnut tree canopies. Based on these images, we extracted 17 spectral indices, 8 texture indices, and 5 structural indices. Then, we applied the Boruta algorithm to select the optimal spectral, texture, and structural indices, as well as their combinations. Finally, the SPAD (Soil and plant analyzer development) values estimation model for walnut leaves was established using the Decision Tree Regression (DTR), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) methods. The research findings indicate that the accuracy of SPAD values estimation model constructed by combining remote sensing indices (spectral indices (SI), texture indices (TI) and structural indices (STI) are combined in pairwise or in full) is better than that of single remote sensing indices. In the combined remote sensing indices models, the accuracy of the model constructed by the pairwise combination of three remote sensing indices is relatively limited. However, when using the combination of spectral indices, texture indices, and structural indices, the estimation accuracy of SPAD values for walnut leaves can be effectively improved, and this combination is considered the best way to estimate SPAD values. Furthermore, among the trio of SPAD values estimation models considered (namely DTR, RFR, and XGBoost), the XGBoost model exhibited superior performance. Notably, when combining SI+TI+STI in the construction of the XGBoost model, it demonstrated the highest level of accuracy in estimating SPAD values (Training: R2 T =0.95, RMSE T =1.08; Validation: R2 v = 0.72, RMSE v =2.13). The findings of this research elucidate that the incorporation of spatial information from UAV multispectral imagery facilitates the monitoring of physiological parameters in walnut trees. By integrating the spatial and spectral information of UAV multispectral imagery, a feasible and accurate estimation method has been provided for monitoring the chlorophyll content in walnut leaves. [ABSTRACT FROM AUTHOR]