Satellite observations of evapotranspiration (ET) have been widely used for water resources management in China. An accurate ET product with a high spatiotemporal resolution is required for research on drought stress and water resources management. However, such a product is currently lacking. Moreover, the performances of different ET estimation algorithms for China have not been clearly studied, especially under different environmental conditions. Therefore, the aims of this study were as follows: (1) to use multisource images to generate a long-time-series (2001–2018) daily ET product with a spatial resolution of 1 km × 1 km based on the Surface Energy Balance Algorithm for Land (SEBAL); (2) to comprehensively evaluate the performance of the SEBAL ET in China using flux observational data and hydrological observational data; and (3) to compare the performance of the SEBAL ET with the MOD16 ET product at the point scale and basin scale under different environmental conditions in China. At the point scale, both the models performed best in the conditions of forest cover, subtropical zones, hilly terrain, or summer, respectively, and SEBAL performed better in most conditions. In general, the accuracy of the SEBAL ET (rRMSE = 44.91 %) was slightly higher than that of the MOD16 ET (rRMSE = 48.72 %). In the basin-scale validation, both the models performed better than in the point-scale validation, with SEBAL obtaining results superior (rRMSE = 13.57 %) to MOD16 (rRMSE = 32.84 %). Additionally, both the models showed a negative bias, with the bias of the MOD16 ET being higher than that of the SEBAL ET. In the daily-scale validation, the SEBAL ET product showed a root mean square error (RMSE) of 0.92 mm d⁻¹ and an r value of 0.79. In general, the SEBAL ET product can be used for the qualitative analysis and most quantitative analyses of regional ET. The SEBAL ET product is freely available at https://doi.org/10.5281/zenodo.4243988 and https://doi.org/10.5281/zenodo.4896147 (Cheng, 2020a, b). The results of this study can provide a reference for the application of remotely sensed ET products and the improvement of satellite ET observation algorithms.

Due to containing an abundance of essential nutrients, straw has significant potential to mitigate carbon (C), nitrogen (N), phosphorus (P), and potassium (K) deficits in soil. However, a lack of comprehensive and systematic reviews on C, N, P, and K release and conversion from straw and on the impact of available nutrients in soils supplemented using straw-returning (SR) practices is noticeable in the literature. Therefore, we investigated straw decomposition, its nutrient release characteristics, and the subsequent fate of nutrients in soils. At early stages, straw decomposes rapidly and then gradually slows down at later stages. Nutrient release rates are generally in the K > P > C > N order. Nutrient fate encompasses fractions mineralized to inorganic nutrients, portions which supplement soil organic matter (SOM) pools, and other portions which are lost via leaching and gas volatilization. In future research, efforts should be made to quantitatively track straw nutrient release and fate and also examine the potential impact of coordinated supply-and-demand interactions between straw nutrients and plants. This review will provide a more systematic understanding of SR’s effectiveness in agriculture.

Irrigation and nitrogen fertilizer application are two important factors affecting yield and nitrogen loss in rice fields; however, the interaction effects of different irrigation schedules and combined management of nitrogen fertilizer application on yield and nitrogen loss in rice fields remain unknown. Therefore, we collected 327 sets of data on rice yield and 437 sets of data on nitrogen loss in rice fields from 2000 to 2021 and investigated the effects of different water-saving irrigation schedules, nitrogen application levels, and water–nitrogen couplings on rice yield, nitrogen use efficiency, and nitrogen loss (N₂O emissions, nitrogen runoff, nitrogen leaching, and ammonia volatilization) by meta-analysis using conventional flooding irrigation and no nitrogen treatment as controls. The results showed that alternate wet and dry irrigation and controlled irrigation had increasing effects on rice yield. Alternate wet and dry irrigation had a significant yield-increasing effect (average 2.57% increase) and dry cultivation significantly reduced rice yield with an average 21.25% yield reduction. Water-saving irrigation reduces nitrogen runoff and leaching losses from rice fields but increases N₂O emissions, and alternate wet and dry irrigation has a significant effect on increasing N₂O emissions, with an average increase of 67.77%. Most water-saving irrigation can increase nitrogen use efficiency. Among water-saving irrigation methods, the effect of controlled irrigation on increasing nitrogen use efficiency is 1.06%. Rice yield and nitrogen use efficiency both showed a trend of increasing then decreasing with nitrogen fertilizer application, and nitrogen loss gradually increased with the amount of nitrogen fertilizer input. Water–nitrogen coupling management can significantly reduce nitrogen loss in rice fields while saving water and increasing yield. Based on the analysis of the data in this study, when the irrigation amount was 300~350 mm and the nitrogen application amount was 200~250 kg/ha, the rice yield and nitrogen fertilizer use efficiency were at a high level, which corresponded to the irrigation schedule of controlled irrigation or alternating wet and dry irrigation in the literature. However, different rice-growing areas are affected by rainfall and land capability, etc. Further optimization and correction of the adapted water and fertilizer management system for paddy fields are needed. The optimal water–nitrogen pattern of this study can achieve high rice yield and reduce nitrogen loss.

Accurate ET₀ estimation is of great significance in effective agricultural water management and realizing future intelligent irrigation. This study compares the performance of five Boosting-based models, including Adaptive Boosting(ADA), Gradient Boosting Decision Tree(GBDT), Extreme Gradient Boosting(XGB), Light Gradient Boosting Decision Machine(LGB) and Gradient boosting with categorical features support(CAT), for estimating daily ET₀ across 10 stations in the eastern monsoon zone of China. Six different input combinations and 10-fold cross validation method were considered for fully evaluating model accuracy and stability under the condition of limited meteorological variables input. Meanwhile, path analysis was used to analyze the effect of meteorological variables on daily ET₀ and their contribution to the estimation results. The results indicated that CAT models could achieve the highest accuracy (with global average RMSE of 0.5667 mm d^-1, MAE of 4199 mm d^-1and Adj_R² of 0.8514) and best stability regardless of input combination and stations. Among the inputted meteorological variables, solar radiation(Rs) offers the largest contribution (with average value of 0.7703) to the R² value of the estimation results and its direct effect on ET₀ increases (ranging 0.8654 to 0.9090) as the station’s latitude goes down, while maximum temperature (T_max) showes the contrary trend (ranging from 0.8598 to 0.5268). These results could help to optimize and simplify the variables contained in input combinations. The comparison between models based on the number of the day in a year (J) and extraterrestrial radiation (Ra) manifested that both J and Ra could improve the modeling accuracy and the improvement increased with the station’s latitudes. However, models with J could achieve better accuracy than those with Ra. In conclusion, CAT models can be most recommended for estimating ET₀ and input variable J can be promoted to improve model performance with limited meteorological variables in the eastern monsoon zone of China.

In arid regions, irrigation scheduling optimization is efficient in coping with the shortage of agricultural water resources. This paper developed a simulation–optimization model for irrigation scheduling optimization for the main crop in an arid oasis, aiming to maximize crop yield and minimize crop water consumption. The model integrated the soil water balance simulation model and the optimization model for crop irrigation scheduling. The simulation model was firstly calibrated and validated based on field experiment data for maize in 2012 and 2013, respectively. Then, considering the distribution of soil types and irrigation districts in the study area, the model was used to solve the optimal irrigation schedules for the scenarios of status quo and typical climate years. The results indicated that the model is applicable for reflecting the complexities of simulation–optimization for maize irrigation scheduling. The optimization results showed that the irrigation water-saving potential of the study area was between 97 mm and 240 mm, and the average annual optimal yield of maize was over 7.3 t/ha. The simulation–optimization model of irrigation schedule established in this paper can provide a technical means for the formulation of irrigation schedules to ensure yield optimization and water productivity or water saving.