Paddy rice cropping systems play a vital role in food security, water use, gas emission estimates, and grain yield prediction. Due to alterations in the labor structure and the high cost of paddy rice planting, the paddy rice cropping systems (single or double paddy rice) have drastically changed in China in recent years; many double-cropping paddy rice fields have been converted to single-cropping paddy rice or other crops, especially in southern China. Few maps detect single and double paddy rice and cropping intensity for paddy rice (CIPR) in China with a 30 m resolution. The Landsat-based and effective flooding signal-based phenology (EFSP) method, which distinguishes CIPR with the frequency of the effective flooding signal (EF_e), was proposed and tested in China. The cloud/ice/shadow was excluded by bit arithmetic, generating a good observation map, and several non-paddy rice masks were established to improve the classification accuracy. Threshold values for single and double paddy rice were calculated through the mapped data and agricultural census data. Image processing (more than 684,000 scenes) and algorithm implementation were accomplished by a cloud computing approach with the Google Earth Engine (GEE) platform. The resultant maps of paddy rice from 2014 to 2019 were evaluated with data from statistical yearbooks and high-resolution images, with producer (user) accuracy and kappa coefficients ranging from 0.92 to 0.96 (0.76–0.87) and 0.67–0.80, respectively. Additionally, the determination coefficients for mapped and statistical data were higher than 0.88 from 2014 to 2019. Maps derived from EFSP illustrate that the single and double paddy rice systems are mainly concentrated in the Cfa (warm, fully humid, and hot summer, 49% vs. 56%) climate zone in China and show a slightly decreasing trend. The trend of double paddy rice is more pronounced than that of single paddy rice due to the high cost and shortages of rural household labor. However, single paddy rice fields expanded in Dwa (cold, dry winter, and hot summer, 11%) and Dwb (cold, dry winter, and warm summer, 9%) climate zones. The regional cropping intensity for paddy rice coincides with the paddy rice planting area but shows a significant decrease in south China, especially in Hunan Province, from 2014 to 2019. The results demonstrate that EFSP can effectively support the mapping of single and double paddy rice fields and CIPR in China, and the combinations of Landsat 7 and 8 provide enough good observations for EFSP to monitor paddy rice agriculture.

With the development of higher education, college students have become a new and important group for environmentally sustainable development. How to evaluate and strengthen the practical effect of ecological education is of great significance. Based on the survey data of 1579 university students, this study constructed a systematic index system of ecological education by using a hierarchical evaluation method. Moreover, OLS (Ordinary Least Squares), Oprobit, and intermediary effect models were used to analyze the influence relationship and mechanism of the two empirically and IV-Oprobit was used to solve the endogeneity problem. The results show the following: (1) Ecological education can effectively promote the sustainable behavior of university students, and the probability of university students implementing sustainable behavior increases by 10.7% with each unit increase in the level of ecological education. (2) Environmental value perception such as in individual economic value perception, social value perception, and health value perception are all important mediating channels of ecological education, among which health value perception has the strongest mediating effect. (3) Particulate Matter 2.5 (PM2.5) exposure positively moderates the impact of university students’ environmental value perception on their sustainable behavior. (4) There is a significant correlation between university students’ household registration, participation in environmental associations and hometown social networks, and sustainable behaviors. (5) The influence of ecological education on students’ behaviors shows heterogeneity in family household registration. Students from rural families have a higher level of environmental behavior and value perception than those from urban families. Moreover, they are more likely to exhibit behaviors under the influence of ecological education. The above results provide reference suggestions for the ecological education policy system and offer theoretical support and policy inspiration for promoting sustainable behaviors among global college students and enhancing the efficiency of ecological education in universities.

Evapotranspiration (ET) is a crucial parameter for agricultural management and the hydrologic cycle, and instantaneous satellite images are the primary data source for regional ET. The constant evaporative fraction method (EFO) is a common approach for converting short-time ET (ET_st) to daily ET (ET_day). However, EFO has some limitations due to simple assumptions, including the following: the short-time evaporative fraction (EF_st) equals the daily evaporative fraction (EF_day). This study proposed an improved evaporative fraction method (EFI) through theoretical derivation and data analysis without additional data requirements, enabling the accurate upscaling of ET_st to ET_day. The vapor pressure deficit and available energy were considered in EFI to describe the main effect factor and estimate the deviation between EF_st and EF_day, defining the deviation coefficient and potential deviation between EF_st and EF_day. EFI was tested through four aspects: different agricultural systems, various sites, two growth stages, and different sources of EF_st, comparing estimated ET_day from EFI and measured ET_day. EFI reduced the mean absolute percentage error (MAPE) of ET_day estimation from 23% to 16% when EF_st is derived from measured data compared to EFO. Similarly, the MAPE of ET_day estimation reduced from 38% to 31% when EF_st is derived from a remote sensing model (Surface Energy Balance Algorithm for Land, SEBAL). EFI performs better during the growing period than the fallow season, providing critical information for irrigation practices. Crop type is not a main control factor for the relationship between η (ratio between VPD and R_n-G) and EF_st, and EFI is adaptable to various agricultural systems. The encouraging results of EFI in different scenarios demonstrate its accuracy and robustness. Therefore, EFI is anticipated to upscale EF_st to EF_day, generating a more accurate ET on a regional scale through remote sensing technology.