Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security.

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.

Soil salinity and water scarcity adversely affect maize productivity. The use of biochar could enhance soil quality and increase the salt and/or drought threshold tolerance and yield of maize. Therefore, a pot experiment was conducted with three biochar addition rates of 0% (BA₀), 5% (BA₅), and 10% (BA₁₀) concerning salinity stresses of 2.0 (WS₂) and 5.0 dS m⁻¹ (WS₅) under deficit irrigation comprising three levels at 60% (DI₆₀), 75% (DI₇₅), and 100% (DI₁₀₀) of the reference evapotranspiration. Compared with the BA₀ rate, the BA₁₀ rate improved soil properties and the growth, nutrient uptake, and yield of maize. The DI₁₀₀ × SW₂ × BA₁₀ combination improved plant growth, providing the highest yield (257.7 g/plant) and water productivity (WP) (2.92 kg/m³) values. However, the combination DI₆₀ × SW₅ × BA₀ decreased plant growth, offering the lowest yield (121.9 g/plant) and WP (1.96 kg/m³) values. While the former combination increased water and nutrient contents in the soil, the latter combination reduced their values. Considering the salt tolerance index, the yield was more tolerant to salinity stress under the BA₁₀ rate as a 3.60% decrease in yield was detected when soil salinity exceeded the threshold value (5.56 dS/m). The drought threshold was declined from 390 L under BA₀ to 280 L under BA₁₀ rate. The study suggests that biochar could mitigate the adverse effects of deficit irrigation and salinity stress on maize growth and productivity. Thus, biochar could be used in salt-affected soils or when irrigation water is of low quality to sustain maize production.

Effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. However, traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional rice yields. In this study, we employed four machine-learning techniques: partial least squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and back propagation neural network (BPNN). We combined these methods with multi-temporal rice NDVI (normalized difference vegetation index) data for rice yield estimation. Following an accuracy evaluation and a spatial analysis, the key findings of our study are as follows. (1) The RFR model emerged as the most accurate for rice yield estimation, achieving an R² of 0.65, an RMSE of 388.79 kg/ha, and an rRMSE of 4.48%. While PLSR and SVR demonstrated comparable accuracy, they were both inferior to RFR. (2) Using the top seven predictors with the highest importance rankings as inputs for the RFR model (NDVI values on the 6th, 17th, 33rd, 44th, 71st, 90th, and 106th days after the rice transplanting stage) achieved comparable accuracy while reducing information redundancy. (3) The proposed model demonstrated good spatial applicability (MI = −0.03) for rice yield estimation in Jiangsu, China. (4) A high spatial resolution yearly rice yield dataset (1 km) spanning from 2001 to 2020 was generated using the proposed model and is accessible on the Zenodo database. In conclusion, this study has demonstrated the efficacy of combining multi-temporal remote sensing data with machine-learning techniques for accurate rice yield estimation, thereby aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season.