Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021.

As a non-linear phenomenon that varies along with agro-climatic conditions alongside many other factors, Evapotranspiration (ET) process represents a complexity when be assessed especially if there is a data scarcity in the weather data. However, even under such a data scarcity, the accurate estimates of ET values remain necessary for precise irrigation. So, the present study aims to: i) evaluate the performance of six hybrid machine learning (ML) models in estimating the monthly actual ET values under different agro-climatic conditions in China for seven provinces (Shandong, Jiangsu, Zhejiang, Fujian, Jiangxi, Hubei, and Henan), and ii) select the best-developed model based on statistical metrics and reduce errors between predicted and actual ET (AET) values.

Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions - Utilizing multi-source remote sensing data and GTWR model.

Grasping current circumstances and influencing components of the synergistic degree regarding reducing pollution and carbon has been recognized as a crucial part of China in response to the protection of the environment and climate mitigation. With the introduction of remote sensing night-time light, CO emissions at multi-scale have been estimated in this study. Accordingly, an upward trend of "CO-PM" synergistic reduction was discovered, which was indicated by an increase of 78.