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During the past several decades, desertification and land degradation have become more and more serious in Mongolia. The drivers of land use/cover change (LUCC), such as population dynamics and climate change, are increasingly important to local sustainability studies. They can only be properly analyzed at small scales that capture the socio-economic conditions. Several studies have been carried out to examine the pattern of LUCC in Mongolia, but they have been focused on changes in single land types at a local scale. Although some of them were carried out at the national scale, the data interval is more than 10 years. A small-scale and year-by-year dataset of LUCC in Mongolia is thus needed for comprehensive analyses. We obtained year-by-year land use/cover changes in Mongolia from 1990 to 2021 using Landsat TM/OLI data. First, we established a random forest (RF) model. Then, in order to improve the classification accuracy of the misclassification of cropland, grassland, and built and barren areas, the classification and regression trees model (CART) was introduced for post-processing. The results show that 17.6% of the land surface has changed at least once among the six land categories from 1990 to 2021. While the area of barren land has significantly increased, the grassland and forest areas have exhibited a decreasing trend in the past 32 years. The other land types do not show promising changes. To determine the driving factors of LUCC, we applied an RF feature importance ranking to environmental factors, physical factors, socioeconomic factors, and accessibility factors. The mean annual precipitation (MAP), evapotranspiration (ET), mean annual air temperature (MAAT), DEM, GDP, and distance to railway are the main driving factors that have determined the distribution and changes in land types. Interestingly, unlike the global anti-V-shaped pattern, we found that the land use/cover changes show an N-shaped trend in Mongolia. These characteristics of land use/cover change in Mongolia are primarily due to the agricultural policies and rapid urbanization. The results present comprehensive land use/cover change information for Mongolia, and they are of great significance for policy-makers to formulate a scientific sustainable development strategy and to alleviate the desertification of Mongolia. [ABSTRACT FROM AUTHOR]

Relict permafrost presents an ideal opportunity to understand the impacts of climatic warming on the ground thermal regime since it is characterized by a mean annual ground temperature close to 0 ∘ C and relatively thin permafrost. The long-term and continuous observations of permafrost thermal state and climate background are of great importance to reveal the links between the energy balance on hourly to annual timescales, to evaluate the variations in permafrost thermal state over multiannual periods and to validate the remote sensing dataset. We present 11 years of meteorological and soil data from the Mahan Mountain relict permafrost site of northeastern Qinghai–Tibet Plateau. The meteorological data comprise air and land surface temperature, relative humidity, wind speed and direction, shortwave and longwave downwards and upwards radiation, water vapor pressure, and precipitation on a half-hour timescale. The active layer data include daily soil temperature and soil volumetric water content at five different depths. The permafrost data consist of the ground temperature at 20 different depths up to 28.4 m. The high-quality and long-term datasets are expected to serve as accurate forcing data in land surface models and evaluate remote-sensing products for a broader geoscientific community. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Cryos.tpdc.271838, Wu and Xie, 2021). [ABSTRACT FROM AUTHOR]

Relict permafrost presents an ideal opportunity to understand the impacts of climatic warming on the ground thermal regime since it is characterized by mean annual ground temperature close to 0 °C and relatively thin permafrost. The long-term and continuous observations of permafrost thermal state and climate background are of great importance to reveal the links between the energy balance on hourly to annual timescales, to evaluate the variations of permafrost thermal state over multi-annual periods and to validate the remote sensing dataset. Until now there are few data available in relict permafrost regions although those data are important to understand the impacts of climate changes on permafrost especially in the boundary regions between permafrost and seasonally frozen ground regions. In this study, we present 11 years of meteorological and soil data in a relict permafrost site of the Mahan Mountain on the northeast of the Qinghai-Tibet Plateau. The meteorological data are comprised of air and ground surface temperature, relative humidity, wind speed and direction, shortwave and longwave downward and upward radiation, water vapor pressure, and precipitation on half-an-hour timescale. The active layer data include daily soil temperature and soil moisture at five different depths. The permafrost data consist of ground temperature at twenty different depths up to 28.4 m. The high-quality and long-term datasets are expected to serve as accurate forcing data in land surface models and evaluate remote-sensing products for a broader geoscientific community. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.271838, Wu and Xie, 2021). [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is very important in the vulnerable ecological environment of the Third Pole; however, data regarding the spatial distribution of SOC are still scarce and uncertain. Based on multiple environmental variables and soil profile data from 458 pits (depth of 0–1 m) and 114 cores (depth of 0–3 m), this study uses a machine-learning approach to evaluate the SOC storage and spatial distribution at a depth interval of 0–3 m in the frozen ground area of the Third Pole region. Our results showed that SOC stocks (SOCSs) exhibited a decreasing spatial pattern from the southeast towards the northwest. The estimated SOC storage in the upper 3 m of the soil profile was 46.18 Pg for an area of 3.27×106 km 2 , which included 21.69 and 24.49 Pg for areas of permafrost and seasonally frozen ground, respectively. Our results provide information on the storage and patterns of SOCSs at a 1 km resolution for areas of frozen ground in the Third Pole region, thus providing a scientific basis for future studies pertaining to Earth system models. The dataset is open-access and available at 10.5281/zenodo.4293454 (Wang et al., 2020). [ABSTRACT FROM AUTHOR]

• Greening of the QTP is mainly a transition from bareland to alpine grassland. • Precipitation and temperature are the dominant factors in the greening of the QTP. • GEE combined with deep learning for high-precision LC mapping in alpine regions. • The LC maps we generate for the QTP are more realistic than existing LC products. Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988–2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 × 104 km2) and from alpine grassland to alpine meadow (17.43 × 104 km2) during 1988–2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influencing factors affecting vegetation greening on the QTP are precipitation (q- statistic = 23.87 %) and temperature (q -statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human development strategies. [ABSTRACT FROM AUTHOR]

Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate. [ABSTRACT FROM AUTHOR]