To understand the change in irrigation water requirement (IWR) in China under the changing climate conditions and grain planting structures, this paper focused on rice, wheat and maize and analyzed the IWR change in each county under the background of climate change and the adjustment of planting structure by means of the China Agro-Ecological Zones (China-AEZ) model. The results show that: (1) Climate change causes the increase of IWR. Compared with the 1961–1990 period, the average IWR of counties in China increased by 9.31 mm and 16.50% under the climate conditions of the 1981–2010 period. Regionally, the water consumption effect of climate change is stronger in water-poor areas than in water-rich areas. (2) Planting structure change causes the decrease of IWR. Compared with 1990, under the planting structure in 2014, the average IWR of counties in China decreased by 6.61 mm and 11.41%. Regionally, the water-saving effect of the change in planting structure in the north is stronger than that in the south. (3) With the change in climate conditions and planting structure, the average IWR of counties in China first increased and then decreased, with an overall increase of 2.06 mm and 3.56%. Regionally, the IWR in most parts of the country has shown a decreasing trend, while the IWR in northwestern and north China, where water resources are relatively scarce, has increased significantly. (4) Nationally, the change in IWR is dominated by climate change, and the overall trend is in the direction of water consumption. Therefore, adjusting the crop planting structure is a promising way to save irrigation water. To adapt to the pressures on water resources caused by climate change, the proportion of crops with water-saving advantages should be increased in the corresponding areas to achieve regional water savings. In northwest China, which mainly relies on irrigation water, many crops with low IWR should be planted, such as wheat and maize, while the planting proportion of rice should be reduced; furthermore, the planting proportion of wheat should be reduced, and the planting proportion of maize should be increased in north China. Finally, rice with high IWR should be more localized in central, southern and northeastern China.

A comprehensive set of 30-m resolution land coverage data of 2000 and 2010 was used for an analysis of the spatial heterogeneity of forest area change in early 21st century China. Four regression models were built to determine the current situation of the ‘forest transition’ in China. The results show that forest area in China has grown rapidly over this period such that total forest area has increased by 102,500 km² and forest cover has increased by 1.06%. Our results demonstrate the presence of a ‘U-shaped’ relationship, the so-called ‘forest transition’, between forest area change and per capita gross domestic product (GDP). We estimate that the inflection point in the Chinese ‘forest transition’ will be at a per capita GDP of 50,522 yuan. In the future, regions with lower elevations, or slope, should be the focus of attention because of dramatic recent forest changes. In particular, forest areas in the regions of the Xiaoxing’anling-Changbaishan Mountains and in South China have markedly decreased, and these are areas of concern. In the meantime, the government needs to strengthen the management of large-scale interconversions between forest and grassland.

Changes in grassland vegetation coverage (GVC) and their causes in the China–Mongolia–Russia Economic Corridor (CMREC) region have been a hot button issue regarding the ecological environment and sustainable development. In this paper, multi-source remote sensing (RS) data were used to obtain GVC from 2000 to 2023 based on random forest (RF) regression inversion. The nonlinear characteristics such as the number of mutations, magnitude of mutations, and time of mutations were detected and analyzed using the BFAST model. Driving factors such as climatic factors were introduced to quantitatively explain the driving mechanism of GVC changes. The results showed that: (1) RF model is the optimal model for the inversion of GVC in this region. The R² of the RF training set reached 0.94, the RMSE of the test set was 12.86%, the correlation coefficient between the predicted and actual values was 0.76, and the CVRMSE was 18.07%. (2) During the period of 2000–2023, the number of mutations in GVC ranged from 0 to 5, and there were at least 1 mutation in 58.83% of the study area. The years with the largest proportion of mutations was 2010, followed by 2016, accounting for 14.57% and 11.60% of all mutations, respectively. The month with the highest percentage of mutations was October, and followed by June, accounting for 31.73% and 22.19% of all mutations, respectively. (3) The sustained and stable positive effect was shown by precipitation on GVC before and after the maximum mutation. Wind speed was a negative effect on GVC in areas with more severe desertification, such as Inner Mongolia, China and parts of Mongolia. On the other hand, GVC was reduced by the wind speed before and after the maximum mutations. Therefore, to guarantee the ecological security of the CMREC, governments should formulate new countermeasures to prevent desertification in the region according to the laws of nature and strengthen international cooperation.