Reference evapotranspiration (ET_ref) is a key parameter of hydro-meteorological studies as well as water resource planning. In this study, we adopted the Penman–Monteith FAO 56 model to estimate ET_ref and through the differential equation and detrending method to determine sensitivities and the contributions of four meteorological parameters to ET_ref based on daily weather data from 60 stations of Jiangsu province during 1961–2015. Results reveal that ET_ref and its trends in the three sub-regions of the Jiangsu province had a significant spatial heterogeneity. A significant decreasing tendency of ET_ref (p < 0.001) was observed in the Huaibei region, while a slightly increasing tendency was identified in the Jianghuai and Sunan regions. These changes of ET_ref were caused by a significant increasing trend in air temperature (TA) and significant decreasing trends in wind speed (WS), sunshine duration (SD) as well as a non-significant change trend in actual vapor pressure (VP). However, the VP was the meteorological parameter to which ET_ref was most sensitive, whereas ET_ref was more sensitive to TA and SD in the summer but less so in the winter; the least sensitive factor, WS, had the opposite trend. Across the whole region, WS contributed most to ET_ref, followed by SD, while the positive contribution of TA to ET_ref could not offset the negative contributions of WS and SD. Although the effect of VP on changes in ET_ref is small, it could not be ignored, especially in the winter. The reverse relationship between increasing TA and decreasing ET_ref, namely the “evaporation paradox,” occurred in Jiangsu province. Thus, the outcomes of this study will contribute to thorough insight into the response to changes in ET_ref to the provincial water planning and management in eastern China.

The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination ( $R_{CV}^2$ ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models.

A sensitivity analysis of the responses of crops to the chosen production adaptation options under regional climate change was conducted in this study. Projections of winter wheat production for different sowing dates and cultivars were estimated for a major economic and agricultural province of China from 2021 to 2080 using the World Food Study model (WOFOST) under representative concentration pathways (RCPs) scenarios. A modeling chain was established and a correction method was proposed to reduce the bias of the resulting model-simulated climate data. The results indicated that adjusting the sowing dates and cultivars could mitigate the influences of climate change on winter wheat production in Jinagsu. The yield gains were projected from the chosen sowing date and cultivar. The following actions are recommended to ensure high and stable yields under future climate changes: (i) advance the latest sowing date in some areas of northern Jiangsu; and (ii) use heat-tolerant or heat-tolerant and drought-resistant varieties in most areas of Jiangsu rather than the currently used cultivar. Fewer of the common negative effects of using a single climate model occurred when using the sensitivity analysis because our bias correction method was effective for scenario data and because the WOFOST performed well for Jiangsu after calibration.

The authors wish to make the following corrections to their paper [1].