Xuegang Xing

The carbon budget of maize croplands has been widely studied across the Chinese Maize Belt. However, the effects of drought on ecosystem-scale carbon fluxes in rain-fed maize croplands remain poorly understood in Southwest China. To address this gap, we conducted long-term, continuous eddy covariance measurements of carbon fluxes at a rain-fed maize field in Guizhou Province from 2022 to 2025. During the maize growing season, the mean net ecosystem exchange (NEE) was −310 ± 21 g C m⁻², with over 50% of the total seasonal NEE occurring in July. Interannual variability in the NEE was primarily driven by prolonged dry spells during the reproductive stage. Maize cropland without straw return acted as a net C source when harvest removals were considered. We therefore recommend promoting straw return practices in the region to strengthen soil carbon sequestration capacity and enhance agroecosystem resilience to drought. And further research is needed to evaluate the role of straw return practices on the carbon budget in the area.

Dune migration is one of the main processes in arid lands’ geomorphology and is important for the design of windbreaks and sand fixation projects and for the monitoring of desertification dynamics. We conducted long-term continuous positioning monitoring of barchan dunes using RTK equipment and wind regime monitoring in the Sanlongsha dune field, which is located in the northern part of China’s Kumtagh Desert. We analyzed the wind energy environment of the study area, the migration characteristics of different positions in the barchan dune, and dune shape changes during different periods. We found that (1) comparing the differences in migration distance and direction measured at six positions in the barchan, there existed variations in barchan migration across these positions. (2) The shape changes at the left horn, right horn, and windward slope of barchans were larger than at the center of the leeward toe and brink, so the estimates based on measurements at these four positions had a weaker fit with the resultant drift potential (RDP) and a greater difference from the resultant drift direction (RDD). (3) The shape of the leeward slope on the barchan did not change much during dune migration, so the center of the leeward toe and brink measurements were closer to the actual dune migration distance and direction. Thus, we recommend using the center of the leeward toe or brink as the optimal measurement points to monitor barchan dune migration. This study will provide a reference for the more accurate measurement of barchan dune migration.

As the population has increased and the economy has developed in the Qaidam Basin, the demand for food and energy in the basin has increased, and the contradiction between economic development and ecological protection is gradually becoming prominent. In this study, the eco-environmental quality of the Qaidam Basin from 1986 to 2019 was evaluated and analyzed based on the Modified Remote Sensing Ecological Index (MRSEI) retrieved by the Google Earth Engine (GEE) and meteorological and socioeconomic auxiliary data. The results show that (1) the Qaidam Basin had a lower overall level of eco-environmental quality, with higher eco-environmental quality in the southeastern part of the basin and lower eco-environmental quality in the central and northwestern parts of the basin. (2) During the period of 1986 to 2019, the eco-environmental quality of the Qaidam Basin started to reverse in 2003; it decreased first and then increased, and the overall performance showed an upward trend over the past 34 years. The most obvious changes were in the northwestern, northeastern, southwestern and central parts of the basin. The eco-environmental quality continued to decline in the northwestern and rise in the northeastern and southwestern regions, and in the central part, it decreased first and then plateaued. (3) The eco-environmental quality of the Qaidam Basin was affected by both natural and human factors. From 1986 to 2019, the “warm and wet” climate in the basin promoted the growth of vegetation. Furthermore, the optimization of industrial structures alleviated the pressure of agriculture and livestock and jointly improved the ecological environment in the Qaidam Basin.

The Sanjiangyuan region is the source of the Yangtze, Yellow, and Lantsang rivers and is an important water conservation area in China. Due to the high altitude and cold climate in the region, the vegetation ecosystem has become very sensitive to environmental changes. In recent decades, due to the impact of climate change and human activities, the grassland degradation and desertification in this region have become very serious. In order to study the changes in aeolian desertification of grassland (ADG) in Sanjiangyuan, the Landsat images and GIS technology were used to monitor the dynamics of ADG from 1975 to 2015, and the driving factors behind this were analyzed. The results revealed that from 1975 to 2000, the area of ADG increased by 2855.8 km², and the growth rate was 114.23 km² a⁻¹. In contrast, the ADG was restored from 2000 to 2015, with a decrease of 1286.54 km² and a rate of 85.77 km² a⁻¹. The main reasons for the expansion of ADG in the early stage were the rising temperature, the fluctuation of precipitation and wind speed, and the increase in intensified human activities. The main reasons for the reversal of ADG in the later stage were the warming and humidification of the climate, the reduction in wind speed, and the reduction in human activities and restoration of grassland caused by the ecological protection project.

In recent decades, the vegetation of the Sanjiangyuan region has undergone a series of changes under the influence of climate change, and ecological restoration projects have been implemented. In this paper, we analyze the spatiotemporal dynamics of vegetation in this region using the satellite-retrieved normalized difference vegetation index (NDVI) from the global inventory modeling and mapping studies (GIMMS) and moderate resolution imaging and spectroradiometer (MODIS) datasets during the past 34 years. Specifically, the characteristics of vegetation changes were analyzed according to the stage of implementation of different ecological engineering programs. The results are as follows. (1) The vegetation in 65.6% of the study area exhibited an upward trend, and in 53.0% of the area, it displayed a large increase, which was mainly distributed in the eastern part of the study area. (2) The vegetation NDVI increased to differing degrees during stages of ecological engineering. (3) The NDVI in the western part of the Sanjiangyuan region is mainly affected by temperature, while in the northeastern part, the NDVI is affected more by precipitation. In the southern part, however, vegetation growth is affected neither by temperature nor by precipitation. On the whole region, vegetation growing is more affected by temperature than by precipitation. (4) The impacts of human activities on vegetation change are both positive and negative. In recent years, ecological engineering projects have had a positive impact on vegetation growth. This study can help us to correctly understand the impact of climate change on vegetation growth, so as to provide a scientific basis for the evaluation of regional ecological engineering effectiveness and the formulation of ecological protection policies.

The normalized difference vegetation index (NDVI) is a powerful tool for understanding past vegetation, monitoring the current state, and predicting its future. Due to technological and budget limitations, the existing global NDVI time-series data cannot simultaneously meet the needs of high spatial and temporal resolution. This study proposes a high spatiotemporal resolution NDVI fusion model based on histogram clustering (NDVI_FMHC), which uses a new spatiotemporal fusion framework to predict phenological and shape changes. Meanwhile, this model also uses four strategies to reduce error, including the construction of an overdetermined linear mixed model, multiscale prediction, residual distribution, and Gaussian filtering. Five groups of real MODIS_NDVI and Landsat_NDVI datasets were used to verify the predictive performance of the NDVI_FMHC. The results indicate that NDVI_FMHC has higher accuracy and robustness in forest areas (r = 0.9488 and ADD = 0.0229) and cultivated land areas (r = 0.9493 and ADD = 0.0605), while the prediction effect is relatively weak in areas subject to shape changes, such as flooded areas (r = 0.8450 and ADD = 0.0968), urban areas (r = 0.8855 and ADD = 0.0756), and fire areas (r = 0.8417 and ADD = 0.0749). Compared with ESTARFM, NDVI_LMGM, and FSDAF, NDVI_FMHC has the highest prediction accuracy, the best spatial detail retention, and the strongest ability to capture shape changes. Therefore, the NDVI_FMHC can obtain NDVI time-series data with high spatiotemporal resolution, which can be used to realize long-term land surface dynamic process research in a complex environment.