Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

As the main expenditure item in water balance, evapotranspiration has an important impact on the surface ecosystem. Assessing the impact of changes in meteorological elements on evapotranspiration is essential to identify the spatiotemporal heterogeneity of hydrographic responses to climate changes. Based on the actual evapotranspiration (ET_a) product (GPR-ET) generated by Gaussian process regression (GPR), as well as temperature and precipitation datasets, our study employed various statistical analysis methods, including geographic detector, the center of gravity migration model, spatial variation coefficients, and partial differential models, to investigate the spatiotemporal variation in ET_a in China from 2000 to 2018. The analysis covered future trends in ET_a changes and the contribution of meteorological factors. Our results showed that the ET_a in northwest China had stronger spatial heterogeneity and the mean value was generally lower than that in the southeast. But the center of gravity of ET_a was shifting towards the northwest. In most areas, the future trend was expected to be inconsistent with the current stage. ET_a in the regions of north and west was mainly driven by precipitation, while its increase in southeast China was largely attributed to temperature. In addition to spatial variations, the joint enhancement effect of temperature and precipitation on ET_a exists. According to the contribution analysis, precipitation contributed more to the change in ET_a than temperature. These findings have enhanced our comprehension of the contribution of climate variability to ET_a changes, providing scientific proof for the optimization apportion of future water resources.

Global challenges such as climate change, ecological imbalance, and resource scarcity are closely related with land-use change. Arid land, which is 41% of the global land area, has fragile ecology and limited water resources. To ensure food security, ecological resilience, and sustainable use of land resources, there is a need for multi-scenario analysis of land-use change in arid regions. To carry this out, multiple spatial analysis techniques and land change indicators were used to analyze spatial land-use change in a typical inland river basin in arid Northwest China—the Tailan River Basin (TRB). Then, the PLUS model was used to analyze, in a certain time period (1980–2060), land-use change in the same basin. The scenarios used included the Natural Increase Scenario (NIS), Food Security Scenario (FSS), Economic Development Scenario (EDS), Water Protection Scenario (WPS), Ecological Protection Scenario (EPS), and Balanced Eco-economy Scenario (BES). The results show that for the period of 1980–2020, land-use change in the TRB was mainly driven by changes in cultivated land, grassland, forest land, and built-up land. For this period, there was a substantial increase in cultivated land (865.56 km²) and a significant decrease in forest land (197.44 km²) and grassland (773.55 km²) in the study area. There was a notable spatial shift in land use in the period of 1990–2010. The overall accuracy (OA) of the PLUS model was more than 90%, with a Kappa value of 85% and a Figure of Merit (FOM) of 0.18. The most pronounced expansion in cultivated land area in the 2020–2060 period was for the FSS (661.49 km²). This led to an increase in grain production and agricultural productivity in the region. The most significant increase in built-up area was under the EDS (61.7 km²), contributing to economic development and population growth. While the conversion of grassland area into other forms of land use was the smallest under the BES (606.08 km²), built-up area increased by 55.82 km². This presented an ideal scenario under which ecological conservation was in balance with economic development. This was the most sustainable land management strategy with a harmonized balance across humans and the ecology in the TRB study area. This strategy may provide policymakers with a realistic land-use option with the potential to offer an acceptable policy solution to land use.