Long-term evapotranspiration (ET) information is important for water-resource assessment in basins associated with large-scale water-transfer projects. This study characterized ET variability and ET–environment associations across grassland, cropland, and forestland in the Longchuan River Basin using multisource remote-sensing data from May to September during 2001–2020. Temporal ET variability was assessed using all MOD16 8-day composites overlapping this period, while 13 environmental variables were evaluated using pixel-wise Pearson correlations and land-use-specific ET estimation models were developed using selected predictors. Mean daily ET generally increased from May to August and declined in September. The long-term May–September mean daily ET was highest in forestland (3.12 mm d⁻¹), followed by the basin average (2.24 mm d⁻¹), cropland (1.87 mm d⁻¹), and grassland (1.33 mm d⁻¹). Gross primary productivity (GPP) was the predominant correlate across all three land-use types and remained predominant after removal of the monthly climatological cycle and further detrending. Validation R², RMSE, and NRMSE were 0.84, 0.23 mm d⁻¹, and 17.49% for grassland; 0.84, 0.32 mm d⁻¹, and 16.94% for cropland; and 0.78, 0.41 mm d⁻¹ and 13.22% for forestland, respectively. These findings provide a long-term baseline for land-use-specific ET assessment and water-resource management.

Drought is one of the major factors affecting terrestrial ecosystem functioning under climate change. However, the interactions among different drought indices and the response of gross primary productivity (GPP) to them remain unclear. This study used multi-source remote sensing data of China from 2003 to 2020, employing the standardized precipitation evapotranspiration index (SPEI), temperature condition index (TCI), soil moisture condition index (SMCI), and vegetation condition index (VCI) to characterize the spatiotemporal dynamics of drought and quantify the response mechanism between drought indices and GPP. Partial correlation analysis and structural equation modeling were used to distinguish the direct and indirect effects of different drought types on GPP. The results show that GPP exhibited a fluctuating upward trend from 2003 to 2020, ranging from 163.71 to 458.56 g C·m⁻². In 2005, GPP declined significantly across most regions of China relative to 2004, decreasing by 14.47% in North China, 13.93% in Central China, 12.05% in East China, 8.45% in South China, 4.21% in Northwest China, and 4.15% in Northeast China, whereas Southwest China exhibited a slight increase of 0.34%. Different types of drought exhibited distinct occurrence characteristics. SMCI exhibited a drought frequency of 44.35% and a mean duration of 75.64 days. Among the four indices, SPEI showed the lowest frequency (12.54%) and the highest intensity (0.14), whereas TCI exhibited both the highest frequency (60.22%) and the longest duration (98.46 days), and VCI yielded the lowest drought severity (1.96). The TCI was considered a major driver of GPP variations, showing a significant positive correlation with GPP in all regions (r = 0.45–0.88, p < 0.001). In contrast, the SPEI had a relatively weak impact on GPP. The “TCI→VCI → GPP” pathway represented the main pathway of drought–GPP interaction at the national scale. This study provides methods for assessing the response characteristics of GPP under future climate change.

Global climate change has posed a serious threat to agricultural production and food security. Crop growth models, with their excellent simulation and prediction capabilities, have become one of the important tools for guiding agricultural production and ensuring food security. This study provides a review of the research progress in crop growth models. The development history of the models is summarized into four stages, including process modeling, system simulation, model application, and algorithm expansion. According to different driving factors, the crop growth models can be categorized into solar radiation-driven, soil moisture content-driven, meteorological factor-driven, and integrated factor-driven models. In terms of the countries of development, the models mainly include those from the Netherlands, the United States, Australia, and China. Regarding applications, crop growth models are primarily applicable to adaptability assessment, agricultural resource and crop cultivation management, and climate change evaluation. Since their initial development, these models have enhanced their mechanistic nature through various approaches, such as incorporating surface mulching modules, considering the response of root water uptake to soil salt stress, and preliminarily introducing physiological regulation processes. By integrating with remote sensing technology, the spatial scale of the models has been expanded from the point scale to the regional or even global scale, and the accuracy of regional-scale yield estimation has been significantly improved through assimilation with remote sensing. Meanwhile, crop growth models have also been combined with intelligent algorithms to optimize irrigation scheduling, and to perform model parameter optimization. Looking forward, potential future research directions of crop growth models include extending the soil submodule from one-dimensional to two/three-dimensional water–heat–solute transport, moving toward a more mechanistic crop growth modeling, integration with remote sensing, and incorporating artificial intelligence. This study serves as a reference for further development and application of crop growth models, and provides technical support for the development of sustainable agriculture.