Maintaining optimal ecological security is a serious issue in the Chinese Loess Plateau (CLP). Remote sensing ecological indexes (RSEI) of three main tableland regions of the CLP were calculated based on spectral information provided by remote sensing imaging satellites between 2000 and 2018. We were able to use RSEI values to systematically evaluate the temporal and spatial variation in the regional ecological environment and determine the influential factors that mainly associated with these changes. The results showed that between 2000 and 2018, the ecological environment improved, remained stable, and deteriorated, respectively, in the Gansu, Shaanxi, and Shanxi tablelands. Regions with poor or fair RSEIs were concentrated around the main river basins, while regions with moderate RSEIs were associated with poor ecological conditions and poor areas. The significant spatiotemporal variation in RSEI indicates that the ecological system in this region is relatively fragile. We also observed that natural factors such as the temperature, potential evapotranspiration, and precipitation had the greatest influence on the overall ecological quality. The rapid increase in the regional population and human activity played an important role in the variation in the regional RSEI. This research will provide important information on controlling regional soil erosion and ecological restoration in the CLP.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R²), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty. In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the R square (R²), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R² was selected from the models with a prediction accuracy of R² > 0.5 for the specific meteorological stations to estimate its NEE. 4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R² > 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with https://doi.org/10.6084/m9.figshare.20485563.v1.