A well-designed rain gauge network can provide precise and detailed rainfall data for earth science research; meanwhile, satellite precipitation data has been developed to generate more real spatial features, which provides new data support for the improvement of ground station network design methods. In this paper, satellite precipitation data are introduced into the design of a rain gauge network and an optimized method for designing a rain gauge network that comprehensively considers the information content, spatiotemporality, and accuracy (ISA) of the data is proposed. After screening the potential stations, the average spatial information index of the rain gauge network, which is calculated from remote sensing data, is used to address the shortcomings of applying spatial information from single-use measurement data. Then, the greedy ranking algorithm is used to rank the order in which the rain gauges are added to the network. The results of the rain gauge network design in the upper reaches of the Chaobai river show that compared with two methods that do not consider spatiality or use only measured data to consider spatiality, the proposed method performs better in terms of the spatial layout and accuracy verification. This study provides new ideas and references for the design of hydrological station networks and explores the use of remote sensing data for the layout of ground-based station networks.

Continuous daily evapotranspiration (ET) monitoring at the field-scale is crucial for water resource management in irrigated agricultural areas in arid regions. Here, an integrated framework for daily ET, with the required spatiotemporal resolution, is described. Multi-scale surface energy balance algorithm evaluations and a data fusion algorithm are combined to optimally exploit the spatial and temporal characteristics of image datasets, collected by the advanced space-borne thermal emission reflectance radiometer (ASTER) and the moderate resolution imaging spectroradiometer (MODIS). Through combination with a linear unmixing-based method, the spatial and temporal adaptive reflectance fusion model (STARFM) is modified to generate high-resolution ET estimates for heterogeneous areas. The performance of this methodology was evaluated for irrigated agricultural fields in arid and semiarid areas of Northwest China. Compared with the original STARFM, a significant improvement in daily ET estimation accuracy was obtained by the modified STARFM (overall mean absolute percentage error (MAP): 12.9% vs. 17.2%; root mean square error (RMSE): 0.7 mm d⁻¹ vs. 1.2 mm d⁻¹). The modified STARFM additionally preserved more spatial details than the original STARFM for heterogeneous agricultural fields, and provided field-to-field variability in water use. Improvements were further evident in the continuous daily ET, where the day-to-day dynamics of ET estimates were captured. ET data fusion provides a unique means of monitoring continuous daily crop ET values at the field-scale in agricultural areas, and may have value in supporting operational water management decisions.

In Northern China, many regions and cities are located in semi-arid regions, and groundwater is even the only source of water to support human survival and social development. Affected by human activities, the Bashang (BS) region (including Zhangjiakou City and part of Xilin Gol League) have showed a significant decline in groundwater levels in recent years. However, in the BS region, the causes for the decline in groundwater level were not clear. In this study, we used time series of multi-source data Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) to analyze vegetation and groundwater changes based on linear regression models. The variation trends of NDVI (Normalized Difference Vegetation Index, derived from MODIS) and GWSA (groundwater storage anomaly, derived from GRACE and GLDAS) indicated the increasingly better vegetation in the agriculture planting areas, partially degraded vegetation in the grassland, and the declining groundwater level in the whole study region. In order to assess the impact of human-induced factors on vegetation and groundwater, the $RU{E}_{seasonal}$ calculation model was proposed based on RUE (rain use efficiency) in this study. The $RU{E}_{seasonal}$ calculation results showed that human-induced factors promoted the growth of vegetation in agricultural areas and accelerated the consumption of groundwater. In addition, we also obtained temporal and spatial distributions of human activities-affected regions. The area affected by human-induced factors in the south-central study area increased, which accelerated the decline in groundwater levels. From bulletin data, we found that the increasing tourists and vegetable production are respectively the most important factors for the increased consumption of urban water and agricultural water. Based on multi-source data, the influences of various human-induced factors on the ecological environment were explored and the area affected by human-induced factors was estimated. The results provide the valuable guidance for water resource management departments. In the BS region, it is necessary to regulate agricultural water use and strengthen residential water management.

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.

The southern part of the Hebei Province is one of China’s major crop-producing regions. Due to the continuous decline in groundwater level, agricultural water use is facing significant challenges. Precision agricultural irrigation management is undoubtedly an effective way to solve this problem. Based on multisource data (time series soil moisture active passive (SMAP) data, Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and evapotranspiration (ET), and meteorological station precipitation), the irrigation signal (frequency, timing and area) is detected in the southern part of the Hebei Province. The SMAP data was processed by the 5-point moving average method to reduce the error caused by the uncertainty of the microwave data derived SM. Irrigation signals can be detected by removing the precipitation effect and setting the SM change threshold. Based on the validation results, the overall accuracy of the irrigation signal detection is 77.08%. Simultaneously, considering the spatial resolution limitation of SMAP pixels, the SMAP irrigation area was downscaled using the winter wheat area extracted from MODIS NDVI. The analytical results of 55 winter wheat samples (5 samples in a group) showed that winter wheat covered by one SMAP pixel had an 82.72% growth consistency in surface water irrigation period, which can indicate a downscaling effectiveness. According to the above statistical analysis, this paper considers that although the spatial resolution of SMAP data is insufficient, it can reflect the change of SM more sensitively. In areas where the crop pattern is relatively uniform, the introduction of high-resolution crop pattern distribution can be used not only to detect irrigation signals but also to validate the effectiveness of irrigation signal detection by analyzing crop growth consistency. Therefore, the downscaling results can indicate the true winter wheat irrigation timing, area and frequency in the study area.

Accurate continuous daily evapotranspiration (ET) at the field scale is crucial for allocating and managing water resources in irrigation areas, particularly in arid and semi-arid regions. The authors integrated the modified perpendicular drought index (MPDI) as an indicator of water stress into surface energy balance system (SEBS) to improve ET estimation under water-limited conditions. The new approach fed with Chinese satellite HJ-1 (environmental and disaster monitoring and forecasting with a small satellite constellation) images was used to map daily ET on the desert-oasis irrigation fields in the middle of the Heihe River Basin. The outputs, including instantaneous sensible heat flux (H) and daily ET from the MPDI-integrated SEBS and the original SEBS model, were compared with the eddy covariance observations. The results indicate that the MPDI-integrated SEBS significantly improved the surface turbulent fluxes in water-limited regions, especially for sparsely vegetated areas. The new approach only uses one optical satellite data and meteorological data as inputs, providing a considerable operational improvement for ET mapping. Moreover, HJ-1 high-resolution data promised continuous daily ET at the field scale, which helps in understanding the corresponding relationships among field, crop, and water consumption. Such detailed ET information can greatly serve water resources management in the study area as well as other arid and semi-arid regions.