Satellite-based precipitation monitoring at high spatial resolution is crucial for assessing the water and energy cycles at the global and regional scale. Based on the recently released 7th version of the Multi-satellite Precipitation Analysis (TMPA) product of the Tropical Rainfall Measuring Mission (TRMM), and the monthly precipitation data (3B43) are evaluated using observed monthly precipitation from 65 meteorological stations in Jiangsu Province, China, for the period 1998–2014. Additionally, the standardized precipitation index (SPI), which is derived by a nonparametric approach, is employed to investigate the suitability of the TRMM 3B43 precipitation data for drought monitoring in Jiangsu Province. The temporal correlations between observations and the TRMM 3B43 precipitation data show, in general, reasonable agreement for different time scales. However, in summer, only 50% of the stations present correlation coefficients that are statistically significant at the 95% confidence interval. The overall best agreement of TRMM 3B43 precipitation data at seasonal scale tends to occur in autumn (SON). The comparative analysis of the calculated SPI time series suggests that the accuracy of TRMM3B43 decreases with increasing time scale. Stations with significant correlation coefficients also become less spatially homogeneous with increasing time scale. In summary, the findings demonstrate that TRMM 3B43 precipitation data can be used for reliable short-term drought monitoring in Jiangsu province, while temporal-spatial limitations exist for longer time scales.

The accurate estimation of actual evapotranspiration can help improve the utilization of water resources and ease the ecological stress. Based on the generalized complementary principle proposed by Brutsaert in 2015, we used meteorological and hydrological data to estimate the actual evapotranspiration at a resolution of 1 km × 1 km between the years of 1961 and 2000 and also verified the model’s stability. In this study, we used the water balance equation to calibrate the parameters, coupled with the spatial simulation results of the meteorological elements in the actual evapotranspiration model. The estimation results of actual evapotranspiration show that the generalized complementary principle model had high estimation precision in this basin, with an average absolute error of 16.64 mm and an average relative error of 2.25%. With respect to spatial distribution, the average actual evapotranspiration over the years in the basin tended to have high and low distribution in the northern and southern parts of the basin, respectively. The actual evapotranspiration in the basin showed a decreasing trend over the period, with a rate of 24.1 mm/10 years. Correlation coefficient analysis showed that the percentage decreases in percentage sunshine and the decreases in the daily range of temperature were the main reasons for the decrease in actual evapotranspiration.

The dry‐wet climate conditions of an area are mainly determined by the water input (e.g., precipitation, P) and output (e.g., potential evapotranspiration, PET). Based on three dry‐wet indices and the differential equation, this study designed a new attribution method to quantify the contributions of P and PET to the dry‐wet change, so as to determine the dominant factors of the dry‐wet change over whole China (CHINA) and various hydroclimate regions (i.e., arid region, AR; semi‐arid region, SAR; semi‐humid region, SHR; and humid region, HR) during 1960–2018 and six interdecadal periods. Results showed that the overall weak drying in CHINA was dominated by an increase in PET, while climate humidification in AR and HR was dominated by an increase in P, climate drying in SHR was dominated by the reduced P, and the weak drying in SAR was co‐dominated by P and PET during 1960–2018. The dominant factors of dry‐wet change over CHINA and each hydroclimate region varied in different decades. Briefly, climate humidification in CHINA and various hydroclimate regions was basically dominated by P in different decades, and the drying was mainly dominated by P in the 1960s–1980s, but the contribution of PET to climate drying began to become prominent after the 1980s. Besides, we found that P contributed more to dry‐wet change than PET on the station and regional scales, but the contribution of P to climate drying on a national scale was underestimated to some extent due to the regional offset effect of the increase/decrease in P during 1960–2018, as well as in the 1990s. These findings implied that the importance of research on the regional or local scale, and equal attention should be paid to the effects of P and PET changes in the future drought or dry‐wet change research.

Tea is a special economic crop that is widely distributed in tropical and subtropical areas. Timely and accurate access to the distribution of tea plantation areas is crucial for effective tea plantation supervision and sustainable agricultural development. Traditional methods for tea plantation extraction are highly dependent on feature engineering, which requires expensive human and material resources, and it is sometimes even difficult to achieve the expected results in terms of accuracy and robustness. To alleviate such problems, we took Xinchang County as the study area and proposed a method to extract tea plantations based on deep learning networks. Convolutional neural network (CNN) and recurrent neural network (RNN) modules were combined to build an R-CNN model that can automatically obtain both spatial and temporal information from multitemporal Sentinel-2 remote sensing images of tea plantations, and then the spatial distribution of tea plantations was predicted. To confirm the effectiveness of our method, support vector machine (SVM), random forest (RF), CNN, and RNN methods were used for comparative experiments. The results show that the R-CNN method has great potential in the tea plantation extraction task, with an F1 score and IoU of 0.885 and 0.793 on the test dataset, respectively. The overall classification accuracy and kappa coefficient for the whole region are 0.953 and 0.904, respectively, indicating that this method possesses higher extraction accuracy than the other four methods. In addition, we found that the distribution index of tea plantations in mountainous areas with gentle slopes is the highest in Xinchang County. This study can provide a reference basis for the fine mapping of tea plantation distributions.