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Although numerous land cover datasets can act as references for understanding land cover change in China, the inconsistencies between the datasets can also provide understanding. Previous studies on the consistency between land cover datasets have mostly focused on land cover type consistencies and have ignored data consistencies in land cover change. Therefore, we aim to analyse the consistencies in land cover changes through likelihood assessment methods. We compared the spatiotemporal changes in forest, grassland, cropland, and bare land in the Climate Change Initiative land cover dataset (CCI-LC), Moderate-resolution Resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), China’s National Land Use and Cover Change (CNLUCC), Globeland30 and Global Land Cover Fine Surface Covering 30 (GLC-FCS30) datasets in 2010. The results showed that the percentages and changes in each land cover type in MCD12Q1 were different from those in the other datasets. For example, the proportion of grassland in MCD12Q1 was the highest, reaching 48.04%. The places with high consistency were the places where the land cover types were concentrated, and the bare land had the highest consistency. However, the consistency of China’s land cover change was quite low, and the percentage of low consistency was more than 87% from 2000-2018. Comparison of the data with the global artificial impervious area (GAIA) and Hansen-Global Forest Change (Hansen-GFC) datasets showed that the percentage of high construction gain consistency (38.83%) was higher than the forest change consistency, and the percentage forest loss high consistency (8.85%) was lower than the forest gain high consistency (12.76%). The results not only provide a basis for the use of land cover datasets but also give a clearer understanding of the pattern of land cover changes.

Spatiotemporal characteristics of the trade-off among ecosystem services and its mechanism have been extensively studied. However, studies on the difference in spatiotemporal variation of ecosystem services trade-offs in various climate zones under climate change are limited. This study aims to explore the spatiotemporal characteristics and driving factors of trade-offs between crop production and soil conservation in different climate zones by employing a case study in various climate zones of the Shaanxi Province. We adopted statistical and GIS-based spatial analyses to explore the spatiotemporal characteristics of the trade-off and used gradient boosting decision tree to quantify the relative contribution of driving factors. Results showed that trade-off between crop production and soil conservation in a middle temperate zone (0.09) is significantly lower than in a warm temperate zone (0.18) and a subtropical zone (0.21). The trade-off clearly exhibits an upward trend from 1995 to 2018 in all climate zones. However, the increasing trend gradually weakened from the middle and warm temperate zones to the subtropical zone. Moreover, changes in the trade-off are spatially heterogeneous in all climate zones. Anthropogenic activities have larger relative contribution (62.27%–71.67%) to the trade-off than environmental factors in all climate zones. Climate zone type has an insignificant effect on the relative contributions of anthropogenic activities, whereas the relative contributions of most environmental factors are significantly different in various climate zones. Fertilization (relative contribution, 26.58%–37.66%), GDP per land area (relative contribution, 8.50%–20.49%), and population density (relative contribution, 9.77%–16.11%) are the most important anthropogenic driving factors in all climate zones. The environmental factors with greatest relative contribution in the middle temperate, warm temperate, and subtropical zones are accumulated temperature (13.40%), precipitation (26.64%), and accumulated temperature (9.63%), respectively. Thus, differences of spatiotemporal characteristics and driving factors of ecosystem services trade-off must be considered in the formulation of targeted polices for agroecosystem management in different climate zones.

Understanding monitor accurately and predicting drought conditions are necessary to prevent economic losses and natural disasters. Now, there is still a lack of comprehensive assessment of drought prediction in Xinjiang. In order to better predict the future drought trend, firstly we analyzed the trends of SPEI and the magnitude and frequency of drought in selected surface meteorological stations from 1960 to 2017. Secondly, we compared drought trends and spatial distribution characteristics of SPEI and PDSI. Based on historical data from 1960 to 2017, the paper predicted the trends of SPEI and PDSI in the next five years. The results show that the winter SPEI has risen significantly, and there was a SPEI value mutation in 1994. The Spring, Summer and Autumn SPEI show an insignificant downward trend. It is relatively stable on other time scales , and there is no significant sudden change. It is inferred thatthe increasing trend of wetting that may occur under different time scales at NRX in 2021, winter and annual scales will continue to be humidified, and spring, summer, and autumn will continue to be arid. Droughts in NRX occur at least once every three years, which is a high frequency event. The prediction results of the artificial neutral network (ANN) method based on multilayer feed forward network show that probability of humidification at NRX is more likely in the future, and the prediction results of wavelet and Hurst index are basically consistent with that of ANN method.