The Yellow River Basin and the Yangtze River Basin are the two most important watersheds in China, which consist of several key ecological function areas and are crucial in terms of economic contributions. The evaluation of the ecosystem service value and the quantitative acquisition of the regional ecological quality status are necessary for supporting the ecological protection and high-quality development of the two basins. By considering basic data and adopting different ecological function models, this study was carried out to evaluate the value of ecosystem services in the Yellow River Basin and the Yangtze River Basin from 2015 to 2018 in terms of provisioning services, regulating services, and cultural services. Additionally, analysis was conducted in combination with economic indicators. The results showed that there were great differences in the ecosystem patterns between the Yellow River Basin, where grassland accounted for 45% of land use, and the Yangtze River Basin, where forest accounted for 39% of land use. The values of the ecosystem services of the two basins had similar spatial distributions, with higher values upstream (west) followed by downstream (east) and lower values in the middle (central China). The total annual ecosystem value of the Yangtze River Basin was more than three times that of the Yellow River Basin. In addition, the ecosystem services value of most counties in both basins was higher than their GDP, and there was a positive trend of transforming ecological benefits into economic benefits in the Yangtze River Basin. This research provides a methodology for evaluating ecosystem valuation. The results are helpful for formulating and implementing eco-compensation and payments for ecosystem service policies among different regions in the basins, and the results lay a foundation for the spatial planning and high-quality development paths of key basin areas in China.

Under the pressure of limited arable land and increasing demand for food, improving the quality of existing arable land has become a priority to ensure food security. The Chinese government gives great importance to improving cropland productivity by focusing on the construction of high-standard farmland (HSF). The government puts forward the goal of constructing 1.2 billion mu (100 mu ≈ 6.67 hectares) of HSF by 2030. Therefore, how to apply remote sensing to monitor the ability to increase and stabilize yields in HSF project regions has become an essential task for proving the efficiency of HSF construction. Based on HSF project distribution data, Moderate Resolution Imaging Spectroradiometer (MODIS) data and Landsat-8 Operational Land Imager (Landsat8-OLI) data, this study develops a method to monitor cropland productivity improvement by measuring cropland productivity level (CPL), disaster resistance ability (DRA) and homogeneous yield degree (HYD) in the HSF project region. Taking China’s largest grain production province (Henan Province) as a case study area, research shows that a light use efficiency model that includes multiple cropping data can effectively detect changes in cropland productivity before and after HSF construction. Furthermore, integrated Landsat8-OLI and MODIS data can detect changes in DRA and HYD before and after HSF construction with higher temporal and spatial resolution. In 109 HSF project regions concentrated and distributed in contiguous regions in Henan Province, the average cropland productivity increased by 145 kg/mu; among the eight sample project regions, DRA was improved in seven sample project regions; and the HYD in all eight sample project regions was greatly improved (the degree of increase is more than 75%). This evidence from satellites proves that the Chinese HSF project has significantly improved the CPL, DRA and HYD of cropland, while this study also verifies the practicability of the three indices to monitor the efficiency of HSF construction.

As one of China’s most common vegetation types, grasslands comprise about 27.5% of its terrestrial area and 41% of its carbon storage. Since climate change (CC) and human activities (HA) have a great effect on grasslands, quantifying the contributions of CC and HA on grassland net primary productivity (NPP) is crucial in understanding the mechanisms of grassland regional carbon balances. However, current approaches, including residual trend, biophysical model and environmental background-based methods, have limitations on different scales, especially on the national scale of China. To improve assessment accuracy, modifications to the environmental background-based method were introduced in calculating the CC and HA contributions to the actual NPP (ANPP). In this study, the grassland ANPP in national nature reserves was defined as the environmental background value (PNPP), which was only affected by CC and without HA. The pixel PNPP outside the nature reserves could be replaced by the pixel PNPP in the nature reserve with the most similar habitat in the same natural ecological geographical division. The impact of HA on grassland ANPP (HNPP) could be identified by calculating the difference between PNPP and ANPP. Finally, the contributions of CC and HA to ANPP changes were assessed by the trends of ANPP, PNPP, and HNPP. The results showed that the average grassland ANPP significantly increased from 2001 to 2020. CC contributed 71.0% to ANPP change, whereas HA contributed 29.0%. Precipitation was the main contributor to grassland growth among arid and semi-arid regions, while temperature inhibited productivity in these areas. HA was the major cause of degradation in China’s grasslands, although the effects have declined over time. The research could provide support support for government decisions. It could also provide a new and feasible research method for quantitatively evaluating grasslands and other ecosystems.