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Winter warming is fast than summer warming on the Qinghai-Tibet Plateau (QTP). However, no assessment of winter warming effects on permafrost has been attempted. Here we conducted hypothetical control experiments and used the Noah land surface model to evaluate the impacts of winter warming on the QTP permafrost. The results show that air temperature in winter (November-April) was increasing at a rate of 0.66°C/decade during 1980s−2000s, over double that in summer (May-October). The mean annual ground temperature of permafrost increased by 0.13°C/decade. The summer warming dominated the variations in thermal regime of permafrost before 2000. After that, the influence of winter warming on permafrost thermal regime has gradually grown and exceeded that of summer warming. Winter warming has amplified the thermal degradation of permafrost. Our findings reveal that alpine continuous permafrost on the northern QTP has experienced a prominent regional warming due to rapid winter warming since 2000. Plain Language Summary As the highest and largest permafrost region in mid-latitudes,Qinghai-Tibet Plateau (QTP) has experienced prominent winter warming in the past three decades. To date, no study has been made to assess the impacts of winter warming on the QTP permafrost. We used the Noah land surface model to investigate this effect, based on controlled experiments including a baseline representing historical climate conditions and two intentionally constituted hypothetical experiments that remove the winter (November-April) or summer (May-October) warming from the historical records. We analyzed the seasonal changes in air temperature, evaluated the effects of winter and summer warming on the changes of frozen ground in terms of several key indicators (including active layer thickness, permafrost area, mean annual ground temperature of permafrost, and maximum freezing depth of seasonally frozen ground) and investigated the possible underlying mechanism of winter warming on permafrost. The results indicate that winter warming accelerates permafrost thermal degradation. Especially since 2000, alpine continuous permafrost on the Qiangtang High Plain, Northern QTP, has undergone an obvious regional warming induced by rapid winter warming.

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Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining multi-dimensional features derived from Sentinel-1/2 images, Visible Infrared Imaging Radiometer Suite supporting a Day-Night Band (VIIRS-DNB) dataset, and Digital Elevation Model (DEM) data using the Google Earth Engine (GEE) platform, we proposed an efficient framework with good transferability for mapping rural settlements in the Yangtze River Delta. To avoid the time-consuming selection of a large number of training samples in the whole study area, we employed four random forest models obtained from the training samples in respective training municipal districts in four different regions to classify other municipal districts in their corresponding region. We found that different features play diverse vital roles in the extraction of rural settlements in various regions. Compared to results only using optical data, accuracies obtained by the proposed method were significantly improved. The average user’s accuracy, producer’s accuracy, overall accuracy, and Kappa coefficient increased by 16.75%, 17.75%, 11.50%, and 14.50% in the four training municipal administrative areas, respectively. The overall accuracy and Kappa coefficient were 96% and 0.84, respectively. By contrast, our classification results are superior to other public datasets. The final mapping results provided a detailed spatial distribution of the rural settlements in the Yangtze River Delta and revealed that the total area of rural settlements is approximately 32,121.1 km2, accounting for 17.41% of the total area. The high-density rural settlements are mainly distributed in the Northern Plain and East Coast, while the low-density rural settlements are located in the Central Hills and Southern Mountain.