Despite the mounting attention being paid to vegetation growth and their driving forces for water-limited ecosystems, the relative contributions of atmospheric and soil moisture dryness stress on vegetation growth are an ongoing debate. Here we comprehensively compare the impacts of high vapor pressure deficit (VPD) and low soil water content (SWC) on vegetation growth in Eurasian drylands during 1982–2014. The analysis indicates a gradual decoupling between atmospheric dryness and soil dryness over this period, as the former has expanded faster than the latter. Moreover, the VPD–SWC relation and VPD–greenness relation are both non-linear, while the SWC–greenness relation is near-linear. The loosened coupling between VPD and SWC, the non-linear correlations among VPD–SWC-greenness and the expanded area extent in which SWC acts as the dominant stress factor all provide compelling evidence that SWC is a more influential stressor than VPD on vegetation growth in Eurasian drylands. In addition, a set of 11 Earth system models projected a continuously growing constraint of SWC stress on vegetation growth towards 2100. Our results are vital to dryland ecosystems management and drought mitigation in Eurasia.

As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in the extraction of water bodies. However, there is a great deal of speckle noise in SAR images, which seriously affect the extraction accuracy of water. At present, most of the processing methods are filtering methods, which will cause the loss of detailed information. Based on the characteristic of side-looking SAR, this paper proposed a self-adaptive thresholding approach for automatic water extraction based on an OTSU algorithm and distance block. In this method, the whole images were firstly divided into uniform image blocks through a distance layer which was produced by the distance to the orbit. Then, a self-adaptive processing was conducted for merging blocks. The OTSU algorithm was used to obtain a threshold for classification and the Jeffries–Matusita (JM) distance was calculated with the classification result. The merge processing continued until the separability of image blocks reached the maximum. Subsequently, we started from the next block to repeat the merger, and so on until all blocks were processed. Ten study areas around the world and the local Dongting Lake area were applied to test the feasibility of the proposed method. In comparison with five other global threshold segmentation algorithms such as the traditional OTSU, MOMENTS, MEAN, ISODATA and MINERROR, the proposed method obtains the highest overall accuracy (OA) and kappa coefficient (KC), while this approach also demonstrates greater robustness in the analysis of time series. The findings of this study offer an effective method to improve water detection accuracy as well as reducing the influence of speckle noise and retaining details in the image.

Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables.