Cultivated land is the basis of food security and an important component of the construction of ecological civilization. The sustainable use of cultivated land is an important issue in land resource management, and it is also an inevitable factor when addressing the contradiction between food demands and resource and environmental constraints. Cultivated land use is both a food production process and a water- and energy-intensive process. Therefore, sustainable use of cultivated land is important not only for cultivated land itself but also for the associated social, economic, and ecological impacts of water and energy input. Therefore, based on the water–land–energy–food nexus, this paper carries out a theoretical analysis of cultivated land use following the element–structure–function framework and builds an evaluation framework of the sustainable use of cultivated land. Finally, this paper selects appropriate evaluation indicators to evaluate the changes in element coordination and function trade-offs of cultivated land use in Quzhou County from 2000 to 2020; analyzes the key influencing factors in detail; and proposes future development directions. The results reflect the fact that the element coordination degree showed obvious continuous decline three times in a row, then a brief rise, and it finally stabilized at a high level, whereas the synergies between the functions decreased and then increased. This means that the sustainable use level of cultivated land in Quzhou County basically presents a good trend. At present, the obstacle that is restricting the efficient use of cultivated land and sustainable development is water, which should be improved by some measures in the future. The results of this evaluation have important theoretical and practical significance for identifying the characteristics of changes in cultivated land use and for guiding future sustainable use in Quzhou County and other regions.

Most of the current parameterization schemes for the Penman–Monteith–Leuning evapotranspiration (ET) model (PML) consider meteorological and energy factors and land use types, but the analysis of the effect of soil moisture (SM) changes on ET processes lacks sufficient attention. This paper proposes a parameterization scheme for the sensitive parameters of the PML model considering soil water content, i.e., coupling the land surface SM in the calculation of soil evaporation coefficient $f$ and coupling the SM of the root zone layer in the calculation of maximum stomatal conductance $g_{sx}$, respectively. The new parameterization scheme is validated at 13 flux sites worldwide and showed significant improvements in improving the correlation with measured values. Moreover, based on the analysis of the spatial distribution of soil evaporation and vegetation transpiration, and the correlation between SM and ET, the regional characteristics of the effect of SM on ET are further revealed. This study provides a new idea for conducting the fusion simulation of SM based on a PML model, which is useful for the subsequent development of the model.

A reasonable rain gauge network layout can provide accurate regional rainfall data and effectively support the monitoring, development and utilization of water resources. Currently, an increasing number of network design methods based on entropy targets are being applied to network design. The discretization of data is a common method of obtaining the probability in calculations of information entropy. To study the application of different discretization methods and different entropy-based methods in the design of rain gauge networks, this paper compares and analyzes 9 design results for rainy season rain gauge networks using three commonly used discretization methods (A1, SC and ST) and three entropy-based network design algorithms (MIMR, HT and HC) from three perspectives: the joint entropy, spatiality, and accuracy of the network, as evaluation indices. The results show that the variation in network information calculated by the A1 and ST methods for rainy season rain gauge data is too large or too small compared to that calculated by the SC method, and also that the MIMR method performs better in terms of spatiality and accuracy than the HC and HT methods. The comparative analysis results provide a reference for the selection of discrete methods and entropy-based objectives in rain gauge network design, and provides a way to explore a more suitable rain gauge network layout scheme.

Amidst global climate change and unsustainable human exploitation of water resources, water has emerged as a critical factor constraining global agricultural food production and ecological environments. Particularly in agricultural powerhouses like China, irrigation water accounts for a significant portion of freshwater resource utilization. However, the inefficiency of irrigation water usage has become a weak link in water resource management. To better assess irrigation water efficiency, an accurate estimation of regional irrigated areas is urgently needed. This study proposes a method for identifying actual irrigated areas based on remote sensing-derived evapotranspiration (ET) to address the challenge of accurately interpreting irrigated areas in tropical regions. Using Yunnan Province’s Yuanmou irrigation district as a case study, this research combined ground monitoring data and remote sensing data to identify actual irrigated areas through ET inversion and downscaling methods using the Penman–Monteith–Leuning (PML) model. In 2023, the total irrigated area interpreted from remote sensing in the study area was approximately 15,000 hm², with a comparison against validation points revealing an extraction error of 16%. The small error indicates that this method can effectively enhance the reliability of monitoring actual irrigated areas, thus providing valuable data support for agricultural irrigation water management.

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models.