Soil respiration (R_s) represents the largest flux of CO₂ from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global R_s from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in R_s, we showed that land-cover change played a more important role in regulating R_s changes in temperate and boreal regions during 2000–2014. Significant changes in R_s occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global R_s patterns and highlight the importance of land-cover change in driving global and regional R_s changes.

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km², with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. To address this problem, we propose a novel method to monitor these changes using Sentinel-1A data. First, the Sentinel-1A water index (SWI) was built using a linear model and a stepwise multiple regression analysis method with Sentinel-1A and Landsat-8 imagery acquired on the same day. Second, water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016 were extracted by the threshold method utilizing time-series SWI data with an interval of 12 days. The results showed that the SWI threshold classification method could be applied to different regions during different periods with high quantity accuracy (approximately 99%). The water surface areas ranged between 1726.73 km² and 3729.19 km² during the study periods, indicating an extreme variability in the short term. The maximum and average values of the changed areas were 875.57 km² (with a change rate of 35%) and 197.58 km² (with a change rate of 8.2%), respectively, after 12 days. The changes in the mid-western region of Poyang Lake were more dramatic. These results provide baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake.

Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration ( R _s ) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with R ² of 0.72 and RMSE of 0.96 g·C·m ⁻² ·day ⁻¹ . The performance was also good when predicting across-site ( R ² = 0.75) and seasonal patterns ( R ² = 0.92) over the 58 sites with available data being longer than two years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RF-estimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and R _s . This study indicates the importance of considering the influence of water-related variables and R _s in the estimation of NEE at the global scale.

Background: Vegetation in the Mongolian Plateau is very sensitive to climate change, which has a significant impact on the regulation of terrestrial carbon cycle. Methods: We analyzed spatio-temporal changes of both growing season and the seasonal Normalized Difference Vegetation Index (NDVI) using simple linear trend analysis. Besides, correlation analysis was applied to explore the climate factors’ effects on vegetation growth at temporal and spatial scale. Potential effects of human factors on vegetation growth were also explored by residual trend analysis. Results: The results indicated that vegetation growth showed a greening trend in the Mongolian Plateau over the past 30 years. At the temporal scale, the growing season NDVI showed an insignificant increasing trend (at a rate of 0.0003 yr⁻¹). At the spatial scale, a large region (53.8% of the whole Mongolian Plateau) with an increasing growing season NDVI, was primarily located in the southern and northern parts of the plateau. The correlation analysis suggested that temperature and precipitation were the main limiting factors that affected vegetation growth in spring and the growing season, respectively. The residual trend analysis showed that human activities primarily stimulated the growth of grasslands and shrublands, while croplands displayed a decreasing trend due to human disturbances, implying that anthropogenic factors may lead to croplands abandonment in favor of grasslands restoration. Our results provided detailed spatial and temporal changes of vegetation growth, and explored how climate and human factors affected vegetation growth, which may offer baseline data and scientific suggestions for local land and resources management, and facilitate the sustainable development of the terrestrial ecosystems.

Karst rocky desertification (KRD) has become the primary ecoenvironmental problem in the karst regions of southwest China. The rapid and efficient acquisition of exposed bedrock fractions (EBF) is crucial for the monitoring and assessment of KRD degree and distribution within the highly heterogeneous landscapes. Remote-sensing indices provide a useful method for the quick mapping of the EBF at large scales. The currently available rock indices, however, are faced with insensitivity to bedrock change characteristics, which greatly limits their performances and suitability. To address this problem, we proposed a novel karst bare-rock index (KBRI) that applies shortwave-infrared (SWIR) and near-infrared (NIR) bands from Landsat-8 OLI imagery to maximally distinguish between exposed bedrock and other land cover types in southwest China. A linear regression model was thus established between KBRI and the EBF derived from in situ measurements. The model developed here was then validated with an independent experiment and applied over a large geographic area to produce regional maps of EBF in southwest China. Experimental results showed good performance on root mean square error (5.59%), mean absolute error (4.63%), root mean absolute percentage error (13.59%), and coefficient of determination (0.72), respectively. The advantages of the proposed method are reflected in its simplicity and minimal requirements for auxiliary data while still achieving comparatively better accuracy than existing related indices. Thus, the KBRI has the great potential for the application in other regions around the world with the similar geological backgrounds, thereby helping to address the similar or other related environmental issues. Results of this study provide baseline data for the KRD assessment and karst-ecosystem management in southwest China.