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The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle. [ABSTRACT FROM AUTHOR]

In China, irrigation plays a fundamental role in food production, which is hampered by water security, rising population and climate change. To ensure food security and formulate agricultural and irrigation policies, decision-makers need detailed grid-level information on the location of irrigated areas. Unfortunately, this information is not easily available as national irrigation maps are frequently outdated and often have a coarse spatial resolution. In this paper, we present new irrigation maps for China at a spatial resolution of 30 arc seconds (~1 × 1 km) that cover the period between 2005 and 2015. The maps were created using a synergy approach, which combines and integrates regional and global irrigation maps, cropland maps and subnational statistics. The maps were calibrated to subnational irrigation statistics and validated using an external dataset with geocoded information on the location of irrigated areas. The maps show, at the detailed spatial level, how much irrigation expanded over the period of 2005–2015. The proposed synergy approach is flexible and can easily be applied to create irrigation maps for other regions. [ABSTRACT FROM AUTHOR]

In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. [ABSTRACT FROM AUTHOR]

This paper presents an integrated study on the effects of human activity, climate change and crustal movement on the changes of the middle reaches of the Tarim River in China. Remote sensing images and rainfall data from 38 stations from 1970 to 2021 were used for the research. The precise point positioning (PPP) approach was applied to ascertain the crustal movement of the study area using 256 Global Positioning System (GPS) station data of the recent 20 years. The results show that in the past 50 years the studied reaches of the river has moved north at maximum 2940 m or 57.9 m on average, and human activity may have played a dominant role in this change though extreme events of climate may have also taken a part in it. However, before the Anthropocene, its northeast migration is likely associated with crustal movement as the basin bottom inclines to northeast. [ABSTRACT FROM AUTHOR]

The Loess Plateau (LP) of China, which is the pilot region of the "Grain to Green Project" (GGP), has received worldwide attention due to its significant changes in the natural and social environment. Investigation of vegetation variations in response to climate change and human activities is vital for providing support for further ecological restoration planning. This paper aimed to monitor vegetation dynamics of the LP with trend comparisons of various vegetation types, disentangle the effects of climate variations and ecological programs on vegetation variations, and detect the consistency of vegetation variations. More specifically, vegetation dynamics during 1982–2015 were analyzed using the Global Inventory Modelling and Mapping System third-generation Normalized Difference Vegetation Index (GIMMS NDVI3g) data with the application of Breaks for Additive Season and Trend (BFAST) and Hurst Exponent. The results showed that: (1) Vegetation manifested a significant greening trend (0.013 decade−1p < 0.01) in the LP during 1982–2015, and a breakpoint (BP) was detected in 1999, which was the beginning of the GGP. Interannual NDVI after the BP (ABP) showed more than 3.5 times greening rates compared to the NDVI before the BP (BBP). (2) Human activities dominated the vegetation variation (accounted for 59.46% of vegetation variation), among which reforestation and land-use change with steep slopes (i.e., ≥15°) lead to the greening after the GGP implementation. (3) Future trends should be noticed in the Forest Zone and Forest-Grass Zone, where the greening trends tend to slow down or even reverse in the southern LP. The long-term GIMMS NDVI3g time series and multiple geospatial analyses of this study might facilitate a better understanding of the mechanisms of vegetation variations for the assessment of the large restoration programs in fragile ecosystems. [ABSTRACT FROM AUTHOR]

As an important part of the Earth's environmental system, sandy soils are particularly sensitive to changes in the climatic environment. As one of the four major desert regions in China, the MuUs desert has transformed from a desert to an oasis after more than half a century of ecological management. In this paper, we analyzed the spatial and temporal patterns of surface albedo, evapotranspiration, and fraction vegetation cover in the MuUs desert based on the Global Land Surface Satellite (GLASS) product with high spatial and temporal resolution and assessed the relationships between their variability and snow cover, air temperature, and precipitation. It is of great significance to understand the effect of desertification control and climate change after the conversion of land surface types in the MuUs region. The results show that the desertification control in the MuUs area has achieved remarkable results since 1982. The fraction vegetation coverage of the MuUs desert showed a significant increasing trend, with an interannual change rate of 1.32% each decade−1. The surface albedo of MuUs desert decreased significantly. Affected by vegetation and snow cover, it was lower in summer and higher in winter. The evapotranspiration showed a significant upward trend, higher in summer and lower in winter, which is significantly correlated with the changes in surface albedo, air temperature, and vegetation. In addition, the local-scale biophysical effects caused by vegetation change have influenced the climate of the MuUs region, manifested as the increase in precipitation and air temperature. In general, with the support of relevant policies and human construction projects, the overall ecological environment in the MuUs desert is developing in a good way. [ABSTRACT FROM AUTHOR]

Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China's surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration. [ABSTRACT FROM AUTHOR]

Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn't vary in adjacent space and earth surface property doesn't vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the 'Beijing', 'Dalanzadgad', 'NCU_Taiwan' and 'Kanpur' stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR 'Deep Blue' aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively. [ABSTRACT FROM AUTHOR]

Since its official establishment in 2010, Google Earth Engine (GEE) has developed rapidly and has played a significant role in the global remote sensing community. A bibliometric analysis was conducted on 1995 peer-reviewed articles related to GEE, indexed in the Scopus database up to December 2022 to investigate its trends and main applications. Our main findings are as follows: (1) The number of GEE-related articles has increased rapidly, with nearly 85% of them published in the last three years; (2) The top three domains where GEE has been extensively applied are earth and planetary sciences, environmental science, and agricultural and biological sciences. The majority of GEE-related articles were authored by scholars from China and the US, accounting for 58% of the total, with US scholars having the largest impact on the community, contributing to over 50% of the total citations; (3) Remote Sensing published the highest number of articles (26.82%), whereas Remote Sensing of Environment received the highest number of citations (30.40%); (4) The applications of GEE covered a broad range of topics, with a focus on land applications, water resource applications, climate change, and crop mapping; (5) Landsat imagery were the most popular and widely used dataset; and (6) Random forest, decision trees, support vector machines were the most commonly used machine learning algorithms in GEE. Although having a few limitations, this type of analysis should be conducted regularly to observe the development of this field on a regular basis, as the number of publications related to GEE is expected to continue to increase strongly in the coming years. [ABSTRACT FROM AUTHOR]

Lidar is an active remote sensing instrument, and it has been widely used in the detection of atmospheric environmental parameters and meteorological parameters. In recent years, climate change has been widely concerned by the public, and the corresponding atmospheric detection technology has also been developed rapidly. Atmospheric Lidar has been developed rapidly in China, and has achieved good research achievements. The research progress and development status of Lidar for atmospheric detection in recent years are introduced and summarized in this invited paper. The advantages and disadvantages of all kinds of atmospheric detection Lidars and their applications in different detection objects are comprehensively introduced in this article. Finally, the bottlenecks of Lidar technology are summarized, and the development trend of Lidar is also prospected. [ABSTRACT FROM AUTHOR]

It is well known that soil erosion at the watershed scale is the result of interactions between various factors. Among these environmental factors, vegetation is the most important and plays a major role in the soil erosion process. The impact of fractional vegetation cover change (FVCC) on soil erosion in non-contributing areas is a heavily discussed topic. In this paper, the fractional vegetation cover (FVC) in 2002 and 2005 was calculated by using a backpropagation neural network based on remote sensing (RS) data. Then the impacts of FVCC on sediment loads at the outlets of two Miyun reservoir sub-basins were evaluated by integrating RS and geographic information system with statistical analysis. The Miyun reservoir basin (MRB) is characterized by hilly and mountainous topographies and seasonal rainy weather. The primary goal of this paper is to gain a better understanding of FVCC, its driving forces, and its impact on regional soil erosion. We discuss spatiotemporal variations in precipitation and soil erosion, identify which factors contribute to those variations, analyze the influences of FVCC on climate change and human activities and, finally, conclude that changes in FVC and climate regimes are primary factors for soil erosion in MRB. We also discuss how sediment loads may be used to quantitatively separate biophysical and anthropogenic influences and to identify critical thresholds that might have dramatic consequences for the watershed ecosystem. These findings should be quite helpful for sensible watershed development and management planning. [ABSTRACT FROM AUTHOR]

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening (TsHARP) method and a binary linear model are compared to downscale the original daytime FengYun 2F (FY-2F) land surface temperature (LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land (SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error (RMSE) of the LST downscaled from the binary linear model is 1.30°C compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78°C. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37°C in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37°C and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events. [ABSTRACT FROM AUTHOR]

Amidst global climate change and unsustainable human exploitation of water resources, water has emerged as a critical factor constraining global agricultural food production and ecological environments. Particularly in agricultural powerhouses like China, irrigation water accounts for a significant portion of freshwater resource utilization. However, the inefficiency of irrigation water usage has become a weak link in water resource management. To better assess irrigation water efficiency, an accurate estimation of regional irrigated areas is urgently needed. This study proposes a method for identifying actual irrigated areas based on remote sensing-derived evapotranspiration (ET) to address the challenge of accurately interpreting irrigated areas in tropical regions. Using Yunnan Province's Yuanmou irrigation district as a case study, this research combined ground monitoring data and remote sensing data to identify actual irrigated areas through ET inversion and downscaling methods using the Penman–Monteith–Leuning (PML) model. In 2023, the total irrigated area interpreted from remote sensing in the study area was approximately 15,000 hm2, with a comparison against validation points revealing an extraction error of 16%. The small error indicates that this method can effectively enhance the reliability of monitoring actual irrigated areas, thus providing valuable data support for agricultural irrigation water management. [ABSTRACT FROM AUTHOR]

The Keriya River Basin was chosen as the study site in this paper in order to investigate the suitable scales of natural and artificial oases with a certain water resource quantity. A calculation model was constructed and the suitable scales of natural and artificial oases were analyzed using remote sensing images, water resources, weather and socioeconomic data. The results showed that (1) high- and low-flow variations were apparent for the Keriya River. The mean values for the high-flow period, normal flow period and low-flow period were 9.23 × 10 m, 7.177 × 10 m and 5.741 × 10 m, respectively. (2) The ecological water demand of natural oasis in the lower reaches of the Keriya River Basin was 2.561 × 10 m. (3) In the Tarim Basin, the proportion of natural and artificial oases was 6:4. (4) In the middle reaches, after guaranteeing the unchangeable scale of artificial oases, the most suitable size of a natural oasis was 798-1,157 km. Under the reasonable proportion (i.e. proportion of 6:4 for the natural and artificial oases), the respective suitable scales of natural and artificial oases in the middle reaches were 831-1,003 km and 554-669 km. This study constructed a model for calculating the suitable scale of an oasis, verified the suitable proportions of natural and artificial oases and discussed the reasonable scales of oases in order to provide a new theoretical basis for determining reasonable development planning in similar river basins located in extremely arid regions. [ABSTRACT FROM AUTHOR]

The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO2), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO2 changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO2) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO2 and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R2 = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))2/(exp(b3) + exp(b5))2, R2 = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R2 = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO2 in Chagan Lake during the ice-free period. The pCO2 in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO2 also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO2 values in the southeast of Chagan Lake were higher than those in the northwest. The CO2 fluxes were calculated based on the pCO2 and ranged from −3.69 to −2.42 mmol/m2/d, indicating that Chagan Lake was absorbing CO2 (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO2, while the Secchi disk depth was negatively correlated with pCO2. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change. [ABSTRACT FROM AUTHOR]

Three-River-Source (TRS) National Park stands as one of China's earliest established national parks, dedicated to significant ecological responsibilities that include conserving soil and water resources in the Tibetan Plateau region. Research on climate change's influence on the TRS region's grasslands is of great significance in our efforts to comprehend and conserve the grassland ecosystem. The most effective random forest (RF) model was chosen to invert the aboveground biomass (AGB) of grassland in the previous 6 years (2015-2020) and predict the grassland AGB in the following 20 years (2021-2040) by comparing linear regression and multivariate nonlinear regression models such as RF, support vector machine, decision tree, and artificial neural network. A Theil-Sen median trend analysis and a Mann-Kendal test were then used to examine the trends of grassland AGB. The results showed that (1) RF outperformed other models in estimating grassland AGB, with a test set decision coefficient of multiple determination (R²) of 0.722, a root mean square error of 42.596 g/m², and a mean absolute error of 35.619 g/m²; (2) over 6 years, the grassland AGB in TRS National Park had a spatial trend of a steady rise from the northwest to the southeast. The average annual grassland AGB was 247.333 g/m², with averages of 44.836 g/m² , 92.601 g/m² , and 120.217 g/m² in the Yangtze River, Yellow River, and Lancang River source parks respectively. The trend of the grassland AGB was primarily stabilized and slightly recovered, with a small portion of the slightly deteriorated areas; (3) climate change significantly affected grassland AGB, and when temperature and precipitation conditions were adequate, grassland AGB values increased with temperature and precipitation. In the scenarios of ssp119, ssp245, and ssp585, grassland AGB is projected to exhibit a dynamic upward trend over the next 20 years. Global warming is expected to boost grassland AGB. Comprehensive measures are essential to maintain grassland health and ensure a positive impact on global carbon and ecological balance. The study's findings hold great importance for the ecological security of the TRS region and contribute to our global understanding of sustainable grassland development. [ABSTRACT FROM AUTHOR]

The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be helpful for ecosystem management. Based on the NDVI dataset, the study analyzed the trend of NDVI change from 2000 to 2019 using the Theil–Sen trend analysis and the Mann–Kendal significance test, detected the driving forces of the spatial heterogeneity of NDVI by the means of the geographical detector, and analyzed the relative contribution of climate change and human activities to interannual NDVI changes using residual analysis model. The results showed that, in terms of the spatial distribution, the pattern of NDVI showed that it is higher in the southeast and lower in the northwest region of the SACA. Elevation was the dominant factor influencing the spatial heterogeneity of NDVI, with the explanatory power of 64%, much larger than other factors, and vegetation type, temperature, precipitation, land use type, and soil type were the main factors. In addition, the explanatory power of the dual factor interaction was higher than that of the single factor effect, which showed two kinds of interaction relationships: bivariate enhancement and nonlinear enhancement. In terms of the temporal variation, 85.59% of the study area showed an increasing trend, and only 14.41% of the area showed a decreasing trend. The main factor affecting NDVI changes was human activities, and climate change was the secondary factor, with relative contributions of 71.35% and 28.65%, respectively. The study will promote a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention of vegetation degradation and vegetation restoration in the SACA. [ABSTRACT FROM AUTHOR]

This paper examines the topographic and geometric controls on glacier changes in area and equilibrium-line altitude (ELA) in the central Tien Shan, China, since the Little Ice Age (LIA). We delineate the extents of 487 modern glaciers and their corresponding maximum LIA glacial advances using satellite imagery in Google Earth, and analyze the relationships between the magnitude of glacier changes and a set of local topographic/geometric factors including glacier area, slope, aspect, shape, hypsometry and mean elevation. Our results show that: (1) glacier area decreased from 460.2km² during the LIA to 265.6km² in the 2000s (a loss of 42.3%), with an average ELA increase of ~100 m; (2) relative area changes of glaciers are strongly affected by two of these local factors (glacier area and mean elevation); and (3) ELA change does not show a strong relationship with local factors, suggesting that it may be controlled mainly by climatic factors. This study provides important insights into the local controls on glacier changes at the centennial timescale, which are of critical importance to assess future glacier changes in this arid and semi-arid region. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm is used to build a comprehensive drought monitoring model. • Multi-source remote sensing data is processed as input data. • The comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. • The model successfully monitored a drought process in Henan Province, China. • This study provides a new method for the comprehensive assessment of regional drought. Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought. [ABSTRACT FROM AUTHOR]

Abstract: Remote sensing has become an important method for monitoring urban thermal environment, but, it is still difficult to analysis the spatio-temporal change of urban thermal environment quantitatively only by ground surface temperature (or brightness temperature). Three indexes on urban thermal environment monitoring are proposed in this paper: heat island intensity, heat field intensity index and heat island proportion index. Based on MODIS surface temperature products and FY3A/MERSI data of two years, urban thermal environment is monitored by remote sensing method in Beijing. The empirical results verify that three indexes proposed in this paper are meaningful in monitoring of urban heat island. It can not only monitor the intensity and changes of the urban heat island in Beijing effectively, but also, it is positive to carrying out meteorological operations in city thermal environment monitoring. [Copyright &y& Elsevier]

This study aims to assess CO2 levels in China, based on the dry air column-averaged mixing ratios of CO2 measured by the Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) instrument on board ENVISAT and emission inventories. A land cover and statistical data set, the Regional emission inventory in Asia (REAS), was also used in this study. The results show that the spatial variations of the CO2 concentration in the whole of China are obvious, and with clear seasonal fluctuation during 2003-2005. The monthly averaged variability of CO2 over China fluctuates, with the peak occurring in April to May and the trough occurring in August to October. The largest peak-to-trough amplitude was about 16 ppm in this period. The spatial and temporal variations of CO2 had a strong correlation with anthropogenic CO2 emissions and regional terrestrial ecosystem, as well as local meteorological conditions. This paper illustrates that satellite data are promising for obtaining a comprehensive understanding of the variations of atmospheric CO2 concentrations and of CO2 emissions to the atmosphere. [ABSTRACT FROM AUTHOR]

Recent climatic changes have affected terrestrial net primary productivity (NPP). This paper presents an investigation of the impact of climatic changes on Chinese terrestrial vegetation NPP by analyzing 18 years' (1982 to 1999) climatic data and satellite observations of vegetation activity. Results indicate that climatic changes in China have eased some critical climatic constraint on plant growth. (1) From 1982 to 1999, modeled NPP increased by 1.42% · a-1 in water-limited regions of Northwest China, 1.46% · a-1 in temperature-limited regions of Northeast China and Tibet Plateau, and 0.99% · a-1 in radiation-limited regions of South China and East China. (2) NPP increased by 24.2%, i.e. 0.76 petagram of carbon (Pg C) over 18 years in China. Changes in climate (with constant vegetation) directly contributed nearly 11.5% (0.36 Pg C). Changes in vegetation (with constant climate) contributed 12.4% (0.40 Pg C), possibly as a result of climate-vegetation feedbacks, changes in land use, and growth stimulation from other mechanisms. (3) Globally, NPP declined during all three major El Niño events (1982 to 1983, 1987 to 1988, and 1997 to 1998) between 1982 and 2000, but Chinese vegetation productivity responded differently to them because of the monsoon dynamics. In the first three events (1982 to 1983, 1987 to 1988, and 1992), Chinese vegetation NPP declined, while in the later two events (1993, 1997 to 1998) increasing obviously. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an important component of the ecosystem carbon pool, and fire is one of the important disturbances in forest ecosystems. With global warming, there has been a gradual increase in boreal forest fires, which has a nonnegligible impact on the SOC dynamics in forests. The CENTURY model was employed in our study to simulate the changes in SOC stocks in boreal forests of the Great Xing'an Mountains, China under different fire severity conditions. Fire severity was represented by the metric of difference normalized burn ratio (dNBR) derived from 30-m Landsat-8 imageries. Changes in forest SOC stocks following fire disturbance were predicted under four future Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). We found that the CENTURY model had good results in simulating the SOC stocks in the postfire of China's boreal forests. Forest SOC dynamics responded differently to fire severities and the larger SOC loss was associated with increasing fire severity. Importantly, a feedback mechanism was found between climate change and SOC stocks, which reduces SOC stocks with increasing temperatures. High-severity forest fires tended to cause serious damage to the SOC pool and delay forest SOC recovery time; after such events, forest SOC stocks cannot be fully recovered to the prefire levels (6.74% loss). In addition, higher CO2 emissions and warmer temperatures significantly affected the recovery of SOC stocks after fire disturbance, resulting in larger SOC losses. Overall, we projected losses of 10.14%, 12.06%, 12.41%, and 15.70% of SOC stocks after high-severity fires in four RCP scenarios, respectively. Our findings emphasize the importance of fire disturbance and climate change on future dynamics of SOC stocks in China's boreal forests, providing a scientific basis for future boreal forest management and fire management. [ABSTRACT FROM AUTHOR]

Global drylands are experiencing booming development of centralized photovoltaics (PV), which aims to address the dual challenges posed by climate change and energy transformation. In dryland areas with large‐scale deployment of solar PV infrastructure, vegetation was reported to experience drastic changes. However, the long‐term dynamic changes and driving mechanisms have not been thoroughly studied yet. Quantitatively distinguishing the disturbances of climate change and PV plant deployment on vegetation change is the key to understanding the environmental impact of clean energy development and formulating adaptive ecological recovery measures. To understand this, we selected the Gonghe solar power project in northern China, one of the largest dryland PV plants in the world, as a case study. Specifically, satellite‐derived Normalized Difference Vegetation Index (NDVI) and meteorological data from ground stations were used to analyze the changing patterns of vegetation growth and climatic factors. The relative contributions of climatic factors and PV plant deployment to NDVI change were quantified by multiple regression analysis. The results indicated that vegetation has increased gradually since 2000, with the rate of vegetation recovery doubled during the PV expansion period. From 2013 to 2020, climate change was the main driver of increased vegetation (56%), followed by the expansion of solar PV infrastructures (44%). Vegetation inside PV arrays increased 1.4 times faster than outside, mainly because the PV panels improve the efficiency of rainwater utilization in summer and reduce the negative impact of excessive sunlight in the growing season. In addition, vegetation management practices like grazing can further enhance carbon sequestration and create sustainable livelihood opportunities, achieving sustainable economic, social, and ecological development. The novelty of the study lies in the proposed framework to quantify the impact of solar PV programs on vegetation in dryland allowing easy interpretations of vegetation dynamics under clean energy development and climate change, which provide scientific references for clean energy planning and ecological recovery in arid areas. [ABSTRACT FROM AUTHOR]

The glaciers in northwest China are mainly distributed in the northern part of the Himalayas, the Kunlun Mountains, and the Tianshan Mountains. Glaciers are an important freshwater resource in the northern part of the Kunlun Mountains, and the melting of glaciers and snow provides an assured source of water for rivers on the southern edge of the Tarim Basin. Based on the first glacier inventory dataset on China (1968), the second glacier inventory dataset on China (2009), and the glacier inventory dataset on Western China in 2018, this study used DEM data, Landsat remote sensing images, and ERA5 atmospheric reanalysis data to investigate glacier change and its influencing factors with respect to the northern part of the Kunlun Mountains. The results showed that there were 9273 glaciers in the northern part of the Kunlun Mountains in 2018, with an area of about 11,762.72 km2, an ice inventory of about 1168.53 km3, and an average length per glacier of about 1.68 km. The glaciers were mainly distributed at altitudes of 5300–6100 m (7574.66 km2). From 1968 to 2018, the number of glaciers in the northern stretch of the Kunlun Mountains increased by 343, while the glacier area decreased by 2452.80 km2 (−0.14%/a). From 2009 to 2018, the glacier area at the altitude of 4900 m to 6100 m decreased in the northern section of the Kunlun Mountains, and the glacier area at the remaining altitude increased slightly (10.67 km2). From 1968 to 2018, the glacier area and glacier length in all river basins decreased. The relative rate of glacier area change in the Qarqan River basin from 2009 to 2018 was five times that of 1968–2009, and this needs significant attention. From 1968 to 2018, both temperature and precipitation increased to varying degrees, and the increase in precipitation was beneficial to the accumulation of glaciers. Therefore, the increase in temperature was the main cause of glacier change in the northern section of the Kunlun Mountains. [ABSTRACT FROM AUTHOR]

Irrigated agriculture is indispensable to the Lower Mekong River Basin (LMB), which ensures food security and provides livelihoods for tens of millions of people. Irrigation, agricultural production, hydropower and aquatic ecosystem health are intertwined in LMB, so it is necessary to adopt a holistic approach to analyze irrigation problems. Here, we discuss the challenges and opportunities of LMB irrigation. Bibliometric analysis is carried out to determine the characteristics and patterns of watershed irrigation literature, such as the importance of authors, affiliated institutions, and their distribution in China. Based on bibliometric analysis, research topics are determined for thematic review. Firstly, we investigated the factors that directly affect the demand and supply of irrigation water and associated crop yield impacts. Secondly, we analyzed the influence of water availability, land use and climate change on agricultural irrigation. Thirdly, we analyzed the adverse effects of improper irrigation management on the environment, such as flow pattern change, ecosystem deterioration and land subsidence caused by groundwater overexploitation. Fourthly, the time–space mismatch between water supply and demand has brought serious challenges to the comprehensive water resources management in cross-border river basins. In each specific application area, we sorted out the technologies in which remote sensing technology is used. We hope that this review will contribute to in-depth research and decision analysis of remote sensing technology in agricultural irrigation. [ABSTRACT FROM AUTHOR]

Dynamic monitoring and evaluation of vegetation ecological quality (VEQ) is indispensable for ecological environment management and sustainable development. Single-indicator methods that have been widely used may cause biased results due to neglect of the variety of vegetation ecological elements. We developed the vegetation ecological quality index (VEQI) by coupling vegetation structure (vegetation cover) and function (carbon sequestration, water conservation, soil retention, and biodiversity maintenance) indicators. The changing characteristics of VEQ and the relative contribution of driving factors in the ecological protection redline areas in Sichuan Province (EPRA), China, from 2000 to 2021 were explored using VEQI, Sen's slope, Mann-Kendall test, Hurst index, and residual analysis based on the XGBoost (Extreme gradient boosting regressor). The results showed that the VEQ in the EPRA has improved over the 22-year study period, but this trend may be unsustainable in the future. Temperature was the most influential climate factor. And human activities were the dominant factor with a relative contribution of 78.57% to VEQ changes. This study provides ideas for assessing ecological restoration in other regions, and can provide guidance for ecosystem management and conservation. [ABSTRACT FROM AUTHOR]

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region. [ABSTRACT FROM AUTHOR]

Climatic variability and the quantification of climate change impacts on hydrological parameters are persistently uncertain. Remote sensing aids valuable information to streamflow estimations and hydrological parameter projections. However, few studies have been implemented using remote sensing and CMIP6 data embedded with hydrological modeling. This research studied how changing climate influences the hydro-climatic parameters based on the earth system models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). GRACE evapotranspiration data were forced into the Soil and Water Assessment Tool (SWAT) to project hydrologic responses to future climatic conditions in the Hongshui River basin (HRB) model. A novel approach based on climate elasticity was utilized to determine the extent to which climate variability affects stream flow. CMIP6 SSPs (shared socio-economic pathways) for the second half of the 20th century (1960–2020) and 21st century (2021–2100) projected precipitation (5–16%) for the whole Hongshui River basin (HRB). The ensemble of GCMs projected an increase of 2 °C in mean temperature. The stream flow is projected to increase by 4.2% under SSP-1.26, 6.2% under SSP-2.45, 8.45% under SSP-3.70, and 9.5% under SSP-5.85, based on the average changes throughout the various long-term future scenarios. We used the climate elasticity method and found that climate change contributes 11% to streamflow variability in the Hongshui River basin (HRB). Despite the uncertainty in projected hydrological variables, most members of the modeling ensemble present encouraging findings for future methods of water resource management. [ABSTRACT FROM AUTHOR]

Desertification is one of the most severe environmental and socioeconomic issues facing the world today. Gonghe Basin is located in the monsoon marginal zone of China, is a sensitive area of climate change in the northeastern of the Qinghai-Tibet Plateau in China, desertification issue has become very severe. Remote sensing monitoring provides an effective technical means for desertification control. In this study, we used Landsat images in 2010 and 2020 to extract desertification information to constructed the Albedo-NDVI feature space in the Gonghe Basin. And then analyzed temporal and spatial evolution of desertification and its driving factors using Geodetector in the Gonghe Basin from 2010 to 2020. The main conclusions are as follows: (1) Albedo-NDVI feature space method can accurately classify desertification information with accuracy of more than 90%, which was benefit to quantitative analysis of desertification. (2) The desertification situation in the Gonghe Basin had improved from 2010 to 2020, especially in the west of the basin, desertification land area decreased by 827.46 km2, and desertification intensity had been obviously reversed. (3) The changes of the desertification in the Gonghe Basin from 2010 to 2020 was affected by both natural and human factors, and the influence of human activities on desertification reversal had increased gradually. The results indicate that the desertification status in the Gonghe Basin had been effectively controlled, and can provide useful basis for the desertification combat in the Gonghe Basin. [ABSTRACT FROM AUTHOR]

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites. [ABSTRACT FROM AUTHOR]

Despite the fact that gross primary productivity (GPP) and water use efficiency (WUE) have been widely used as indicators to evaluate the water-carbon cycle, uncertainties exist in the patterns of GPP and WUE responses to climate variability along different aridity gradients. In this study, the aridity index was used to divide China into four arid-humid zones. The spatiotemporal variability of multiple vegetation types GPP and WUE in response to climate change under different arid-humid zones were investigated based on remote sensing data. The results indicated that the increasing trend of WUE in the four arid-humid zones of China was less pronounced than GPP from 2001 to 2021. The GPP value decreased gradually from the humid to the arid zone, and the WUE value in the arid zone was slightly higher than in the semi-arid zone. The GPP of all vegetation types in China showed a tendency to increase, while shrubland and wetland WUE tended to decrease. The major vegetation types (e.g., forest, cropland and grassland) in each aridity gradient contributed to the changes in local GPP and WUE. However, in individual arid-humid zones, wetland and shrubland also exhibited high GPP and WUE values that were not inferior to forest and cropland. Temperature and precipitation were the main climatic factors responsible for the increase in vegetation GPP in different aridity gradients, with a higher positive correlation for temperature than precipitation. WUE showed a distinct positive and negative correlation with the thermal factors (temperature and net radiation) and the moisture factors (precipitation and relative humidity); this pattern was more pronounced in the humid and semi-humid zones. Net radiation and precipitation may be the main climatic factors causing a slight upward trend in WUE across the arid-humid zones, while the decrease in shrubland and wetland WUE may be related to relative humidity and precipitation. [ABSTRACT FROM AUTHOR]

Grassland ecosystems are an important part of terrestrial ecosystems and are important for building ecological barriers, promoting the pastoral economy, and maintaining social stability. In recent decades, grasslands in northern China have undergone extensive degradation due to the combined effects of global climate change and the anthropogenic overuse of grasslands. An understanding of the spatial distribution of grassland degradation species is helpful for evaluating the process of grassland degradation and formulating appropriate protective measures. This is important for grassland degradation monitoring. To address the limitations of traditional ground surveys and realize intelligent remote sensing grassland degradation monitoring tasks, we use unmanned aerial vehicle (UAV) hyperspectral remote sensing technology to collect data on vegetation species in desert grasslands. In this paper, we propose a local-global feature enhancement network (LGFEN) for the classification of desert grassland species. The method uses the local feature enhancement (LFE) module and global feature enhancement (GFE) module to extract local and spatial information from hyperspectral images (HSIs), respectively. In addition, the introduction of the convolutional block attention module (CBAM) refines the features of HSIs, improving the stability of the classification performance. The results show that the proposed method has superior classification performance compared with existing HSI classification methods. With only 10 training samples per class, the overall accuracy, average accuracy, and kappa coefficient of the proposed method were 98.61%, 97.61%, and 0.9815, respectively. The proposed method provides a new approach for high-precision and high-efficiency dynamic monitoring of grassland ecosystems. • A local-global feature enhancement network (LGFEN) is proposed. • Improved efficiency of grassland degradation monitoring. • The LFE module is proposed to implement global feature extraction. • Compared with existing methods, LGFEN has better performance. [ABSTRACT FROM AUTHOR]

The Asian elephant (Elephas maximus Linnaeus) is a globally endangered species, an internationally protected species, and a first-class protected animal in China. However, future climate change and human activities exacerbate the instability of its habitat range, leading to a possible reduction in the range. By using multi-source remote sensing data and products, as well as climate change models, including ASTER GDEM v3, Landsat8 OLI image and ClimateAP, we examined the effects of ecological factors related to climate and natural and anthropogenic influences on the distribution of Asian elephants in Sipsongpanna. Multiyear elephant field tracking data were used with a MaxEnt species distribution model and the climate model. First, the distribution of Asian elephants in potentially suitable areas in Sipsongpanna was simulated under current climatic conditions without considering human activities. The predicted distribution was verified by existing Asian elephant migration trajectories. Subsequently, the distribution of potentially suitable areas for Asian elephants in Sipsongpanna was simulated under two climate change scenarios (RCP4.5, RCP8.5) in three periods (2025, 2055, and 2085). The changes in potentially suitable areas for Asian elephants in Sipsongpanna were analyzed under multiple climate change scenarios for the current (2017) and different future periods by considering the effects of human activities. The results show the following: (1) under anthropogenic interference (AI), the optimal MaxEnt model has a high prediction accuracy with the area under the curve (AUC) of 0.913. The feature combination (FC) includes linear, quadratic, and threshold features, and the regularization multiplier (RM) is 2.1. (2) Jackknife analyses of the non-anthropogenic interference (NAI) and anthropogenic interference (AI) scenarios indicate that topography (altitude (Alt)), temperature (mean warmest month temperature (MWMT)), and precipitation (mean annual precipitation (MAP)) are the top three factors influencing the distribution of Asian elephants. (3) The total area suitable for Asian elephants under current climate conditions and AI accounts for 46.35% of the total area. Areas of high suitability (occurrence probability >0.5) are located in Jinghong City in central Sipsongpanna and Mengla County in southeastern Sipsongpanna. Among them, the minimum habitat range and ecological corridors are mainly located in Mengman Town, Mohan Town, Mengla Town, Mengban Township, Dadugang Township, and Mengwang Township. (4) The change in potentially suitable areas for Asian elephants between current and future conditions is small under AI and large under undisturbed conditions. [ABSTRACT FROM AUTHOR]

It is critical to understand the mechanisms and impacts of global climate change in cryosphere and polar environment. Here we describe the design and progress of the remote sensing products we have developed to monitor these changes. We obtain datasets by combining multi-sensor remote sensing systems with field validation systems, and then develop a methodological system that combines multiple sources and calibration technologies to address the challenges of data heterogeneity in dynamically monitoring key parameters. This global change and response special project supported by the National Key Research and Development Program of China, named 'observation and inversion of critical parameters of changes in the cryosphere and polar environment', providing comprehensive records of essential elements of the cryosphere through long-term satellite records. The multi-team is completing the project and collecting data from multi-sensor to create remote sensing data products that include records of changes in the cryosphere, such as permafrost, glaciers, ice sheet mass balance, seasonal snow accumulation, sea ice, and albedo changes. Finally, the existing ground- and space-based observing system can be improved to provide dynamic monitoring of key parameters in the cryosphere and changes in the polar environment. [ABSTRACT FROM AUTHOR]

The green tide caused by Ulva prolifera (U. prolifera) is becoming more severe as climate change and human activity accelerate, endangering tourism, aquaculture, and urban landscapes in coastal cities. In order to understand the spatio-temporal distribution of U. prolifera in response to the green tide disaster, this study used the Haiyang-1C (HY-1C) satellite accompanied by the Sentinel-2 and GaoFen-1 (GF-1) satellites to systematically monitor U. prolifera between 2020 and 2022. The consistency of U. prolifera distribution between the HY-1C and Sentinel-2 satellites, as well as the HY-1C and GF-1 satellites, was first investigated and the determination coefficients (R2) were 0.966 and 0.991, respectively, which supports the feasibility of China's first ocean water color operational satellite, HY-1C, for U. prolifera monitoring. Therefore, the spatio-temporal distribution of U. prolifera is studied herein, along with the influence range, influence area, and drift paths. From 2020 to 2022, U. prolifera appeared in late May and lasted for 61, 88, and 73 days. Additionally, the in influence area continuously decreased in 2020 and 2022, while it generally increased and then decreased in 2021. It is an interesting phenomenon that when the maximum influence area occurred at the early stage of U. prolifera in both 2020 and 2022, the drift paths tended to move southward after traveling northward. The overall trend of the drift path in 2021 was to head northward. Thus, the study of the dynamic evolution, influence range, influence area, and drift paths of U. prolifera is helpful to promote the systematic development of emergency response mechanisms for U. prolifera. [ABSTRACT FROM AUTHOR]

Due to the combined effects of the natural environment, climate change and human activities, profound changes have occurred in terms of the eco-environmental effects of land use/cover change (LUCC) in the Baiyangdian basin. Therefore, based on land remote sensing monitoring data from 2000 to 2020, the Eco-environmental Quality Index (EQI) was introduced in this study to measure the eco-environmental effects of land use change in the Baiyangdian basin. Subsequently, the GeoDetector model was applied to detect the formation mechanism of the eco-environmental effects in the Baiyangdian basin from 2000 to 2020. The results of the study showed that cropland, woodland and grassland were the most widely distributed land use types in the Baiyangdian basin. The area of cropland declined the most and was mostly converted to construction land. The EQI increased slightly during the study period. The eco-environment of the mountainous areas in the western part of the basin and in Baiyangdian Lake was better than that of other areas. Land use intensity had a significantly stronger influence on the quality of the eco-environment than other factors. The interaction between the influencing factors was mainly a non-linear enhancement and a two-factor enhancement, with non-linear enhancement dominating. [ABSTRACT FROM AUTHOR]

The formation and development of blowouts is an important surface indication of sand drift activity in the Hulun Buir sandy grassland and the main cause of landscape pattern heterogeneity. In this study, dynamic changes and determinants of blowouts in this region were studied via analysis of multi‐period high‐resolution remote sensing data from 1959 to 2018. The different stages of activity and dominant factors of the fixation and activation of blowouts were analysed with Lancaster's M‐index. The results show that the activity of blowouts in Hulun Buir sandy grassland can be divided into three stages from 1959 to 2018: (I) growth and expansion stage, from 1959 to 2004; (II) fixation and grassland recovery stage, from 2005 to 2010; (III) redevelopment stage, from 2011 to 2018. The two geomorphic units of depressions and depositional lobes exhibit different responses to changes in climatic factors. The depositional lobes are more sensitive to the change in external factors with better feedback and their areas and numbers change with the variation of stage, while depressions exhibit relatively slow changes. The wind regime is the dominant factor explaining the variation of activity of blowouts, with precipitation and potential evapotranspiration as secondary factors for this region. Roads have little effect on the development and deformation of blowouts, but the movement of cattle and sheep across the region has a big influence. Therefore, the desertification process of Hulun Buir grassland is driven by both natural and human factors. [ABSTRACT FROM AUTHOR]

Aim: Mangrove forests are important coastal wetlands for the blue carbon budget and play a significant role in mitigating global climate change. However, spatial patterns of carbon stocks in mangrove forests on an island scale have not been quantified owing to methodological limitations and lack of understanding of controlling factors. We took the entire Hainan Island as a case study and aimed to carry out a comprehensive investigation of the spatial patterns and driving factors of carbon stocks in mangrove forests. Location: Southern China. Time period: 2017–2020. Major taxa studied: Mangrove forest. Methods: The upscaling method combined with field surveys and Sentinel‐2 imagery analysis were used to compare different models for optimization of mangrove ecosystem carbon stock estimations. We also used structural equation modelling (SEM) to evaluate the factors driving the distributional patterns of mangrove carbon stocks on an island scale. Results: The current total mangrove carbon stock of the entire Hainan Island was estimated to be 703,181 Mg C (with a mean density of 192 Mg C/ha), with the above‐ and below‐ground carbon stocks averaging at 44.7 and 147.3 Mg C/ha, respectively. The mangrove carbon storage in the north‐eastern region of the island was the highest and in the west region the lowest. Sediment nitrogen content and plant species diversity had the most positive driving effects on the distribution of total carbon stock for Hainan Island mangroves. Main conclusions: The combination of field surveys and Sentinel‐2 imagery analysis can be applied to regional‐scale estimations of carbon stocks for mangrove forests. Spatial pattens of mangrove carbon stocks vary among locations on Hainan Island, and soil nutrient (especially nitrogen) availability is the dominant factor regulating carbon stock variations along the land‐to‐sea gradient. Our findings have significant implications for better understanding the distribution of mangrove carbon stocks and quantification of the global blue carbon budget. [ABSTRACT FROM AUTHOR]

Vegetation canopy interception (Ic) of precipitation is a considerable component of the global hydrological cycles. Although the measurement and modeling of canopy interception have been explored worldwide at the individual, stand or ecosystem scale, it is still unclear how to recognize this process at the regional or global scales within the context of global climate change. In this study, a revised Gash model was employed to estimate canopy interception based on remote sensing and meteorological data. The spatial and temporal variations in Ic were investigated and the main environmental factors were explored in China for the 2000–2018 period. The results showed that the revised Gash model performed well in modeling canopy interception at the regional scale compared with the PML_V2 dataset product and the in-situ measurements. The average annual Ic in China from 2000 to 2018 was 166.55 mm, with a significant decreasing spatial pattern from the Southeastern to the Northwestern regions. The ratio of canopy interception to precipitation (Ir) displayed a similar spatial pattern, with an average value of 22.30%. At the temporal scale, the mean annual Ic significantly increased at a rate of 1.79 mm yr−1 (p < 0.01) during the study period, and the increasing trend was more pronounced during the 2000–2009 period, at a rate of 3.34 mm yr−1 (p < 0.01). In most vegetation types, except for the deciduous broad-leaved forest and temperate desert, canopy interception showed a significant increasing trend (p < 0.01). Precipitation, temperature, and the normalized differential vegetation index (NDVI) were considered to be the main factors affecting the variations of Ic in China during the last two decades, with specific dominant factors varying in different areas. Specifically, precipitation was considered to control the variations of Ic in the Northwestern regions, temperature mainly influenced the Southern regions, and the NDVI was identified as the main factor in regions where significant ecological conservation projects are established, such as the Loess Plateau. Our findings are expected to not only contribute to the understanding of regional ecohydrological cycle but also provide valuable insights into the methodology of interception modeling at the regional and global scales. [ABSTRACT FROM AUTHOR]

As impacted by climate change and further global warming, drought turns out to be the most frequent meteorological extreme event worldwide, which severely affects agriculture, ecosystem, water management, and even human survival. In this study, the global pattern and development trends and directions on drought monitoring were presented based on Web of Science database by conducting a bibliometric analysis from 1983 to 2020. The following conclusions were drawn. 1) The United States and China were found as the most productive and influential nations, accounting for 24.63% and 14.30% in publication outputs and taking up 5,023 and 2,040 in local citations, respectively. 2) Chinese Academy of Science was reported as the core institution with 5.73% publication outputs and 829 local citations. 3) Remote Sensing of Environment and Remote Sensing were found as the most influential journals and the most productive journals with 1,045 local citations and 210 publication outputs, respectively. 4) Agricultural drought profoundly affecting food security was found as the most concerning drought type in the world. The drought monitoring research mainly focus on the research and development of drought index, the response of terrestrial ecosystems to drought, and the trends and dynamics of drought in context of climate change. This study explored key findings, contradictions, and limitations of drought monitoring studies were summarized and explored. In addition, the development trend and research direction of drought monitoring research in the future were also explored. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of the onset and evolution of drought in China are important to reduce losses from drought. The Integrated Surface Drought Index (ISDI) which originally established in mideast China shows a large potential for real-time regional drought monitoring. However, ISDI is still at the developmental stage, and the applicability of the index requires further examination especially for China with vast area, climatic conditions, complex topography, and land cover. Furthermore, ISDI model depends on the historical training data corresponding to the study area. ISDI application in China must be remodeled using the historical training data over China. In this paper, we remodeled ISDI over China based on previous work and evaluated its capability for near real-time drought monitoring. Using the Palmer Drought Severity Index (PDSI) as a dependent variable, ISDI integrates climate-based drought indices, satellite-based Vegetation Index (VI) and land surface temperature (LST) with other biophysical and elevation data to produce a 1-km regional drought condition map at 16-day intervals. Strong relationships were determined between the calculated ISDI and PDSI for spring, summer and autumn, and all of the correlation coefficients exceeded 0.8. The initial ISDI results demonstrated a good performance for monitoring droughts in southwestern China from 2009 to 2010, high temperatures and droughts in southern China in 2013, and floods in northeastern China in 2013. The higher spatial resolution and near real-time capability of ISDI can provide important inputs for drought management and mitigation in China. [ABSTRACT FROM AUTHOR]

Surface water provides a basic resource for life and is vital for maintaining climate stability, hydrological cycles, and natural ecosystems. Lakes, which are an important part of surface water, play a crucial role in sustaining the hydrological balance and maintaining a healthy environment. The onset of climate change and anthropogenic disturbance on lakes and the environment, however, have drastically changed the morphological characteristics of lakes, leading to undesirable negative effects on the ecological environment. In recent years, many lakes around the world are undergoing phenomenal changes due to climate change and human activities. These changes have greatly affected the availability of freshwater resources, leading to a series of regional ecological and environmental problems, which constrain the regional sustainable development. Changes in lake parameters often indicate environmental change. Therefore, it is of great practical significance to extract lake water information to reflect the water quantity changes in the lake, understand its implications, and come up with timely solutions. This paper summarizes the existing research methods of lake water estimation in China and abroad and draws the following conclusions: (1) In the study of lake water volume estimation, it can be roughly divided into with topographic data and without topographic data. The most representative methods are the estimation methods based on topographic parameters and the Triangle Irregular Network (TIN) model method. In the actual research process, most of the scarce data that are difficult to obtain are lake topographic data, prompting researchers to constantly put forward new methods; (2) From the perspective of research methods, estimation of lake water volume can be divided into remote sensing-based and non-remote sensing-based research. With the continuous improvement of scientific and technological levels, current geographical researchers tend to use remote sensing to monitor lake water volume; (3) Although researchers have proposed a series of models for lake water volume estimation for the long-time process. However, many models are based on the ideal static estimation process, which poses challenges for researchers to make a long-term dynamic estimation of lake storage capacity in the future. At the same time, the accuracy of the model can be a problem that researchers need to work out solutions in the future. Finally, combined with the current development of remote sensing technology, the application potential and the future prospect of remote sensing in lake monitoring are briefly discussed. • Lake water volume estimation, it can roughly be divided into with topographic data and without topographic data. • Estimation of lake water volume can be divided into remote sensing-based and non-remote sensing research. • The application potential and the future prospect of remote sensing in lake monitoring are briefly discussed. [ABSTRACT FROM AUTHOR]

Soil moisture (SM), an important variable in water conversion between the atmosphere and terrestrial ecosystems, plays a crucial role in ecological processes and the evolution of terrestrial ecosystems. Analyzing and exploring SM's processes and influencing factors in different permafrost regions of the Qinghai-Tibet Plateau (QTP) can better serve the regional ecological security, disaster warning, water management, etc. However, the changes and future trends of SM on the QTP in recent decades are uncertain, and the main factors affecting SM are not fully understood. The study used SM observations, the Global Land Evapotranspiration Amsterdam Model (GLEAM) SM products, meteorological and vegetation data, Mann–Kendall test, Theil–Sen estimation, Ensemble Empirical Mode Decomposition (EEMD), and correlation methods to analyze and explore the characteristics and influencing factors of SM change in different permafrost regions of the QTP. The results show that: (1) At the pixel scale, GLEAM SM products can better reflect SM changes in the QTP in the warm season. The seasonal permafrost region is closer to the real SM than the permanent region, with a median correlation coefficient (R) of 0.738, median bias of 0.043 m3 m−3, and median unbiased root mean square errors (ubRMSE) of 0.031 m3 m−3. (2) The average SM in the QTP warm season increased at a rate of 0.573 × 10−3 m3 m−3 yr−1 over the recent 40 years, and the trend accelerated from 2005–2020. In 64.31% of the region, the soil was significantly wetted, mainly distributed in the permafrost region, which showed that the wetting rate in the dry region was faster than in the wet region. However, the wetting trend does not have a long-term continuity and has a pattern of "wetting–drying-wetting" on interannual and decadal levels, especially in the seasonal permafrost region. (3) More than 65% of the SM wetting trend on the QTP is caused by temperature, precipitation, and vegetation. However, there is apparent spatial heterogeneity in the different permafrost regions and vegetation cover conditions, and the three factors have a more substantial explanatory power for SM changes in the seasonal permafrost region. With the global climate change, the synergistic SM–Climate–Vegetation effect on the QTP tends to be more evident in the seasonal permafrost region. [ABSTRACT FROM AUTHOR]

High-resolution gridded 2 m air temperature datasets are important input data for global and regional climate change studies, agrohydrologic model simulations and numerical weather predictions, etc. In this study, the digital elevation model (DEM) is used to correct temperature forecasts produced by ECMWF. The multi-grid variation formulation method is then used to fuse the data from corrected temperature forecasts and ground automatic station observations. The fused dataset covers the area over (0–60°N, 70–140°S), where different underlying surfaces exist, such as plains, basins, plateaus, and mountains. The spatial and temporal resolutions are 1 km and 1 h, respectively. The comparison of the fusion data with the verification observations, including 2400 weather stations, indicates that the accuracy of the gridded temperature is superior to European Centre for Medium-Range Weather Forecasts (ECMWF) data. This is because a more significant number of stations and high-resolution terrain data are used to generate the fusion data than are utilized in the ECMWF. The obtained dataset can describe the temperature feature of peaks and valleys more precisely. Due to its continuous temporal coverage and consistent quality, the fusion dataset is one of China's most widely used temperature datasets. However, data uncertainty will increase for areas with sparse observations and high mountains, and we must be cautious when using data from these areas. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a key component of the hydrological cycle, but traditional monitoring approaches are always based on measurements, which cannot satisfy the requirements of research on a regional scale. Hence, ET estimation by remote sensing is essential. MOD16 is a remote-sensing model based on the P-M equation and has good applicability. However, it describes soil moisture indirectly by RH, etc., which may cause uncertainties in ET estimating, so this study attempts to utilize the NDWI as a supplement to soil moisture information and makes improvements on the MOD16 model (with the resultant new model being named MOD16-sm). Specific work includes two aspects: one is model verification through making comparisons between ET estimates and measurements, and the other is a model application effect test analyzing the spatiotemporal characteristics of ET and exploring how ET responds to climate and land-use changes. Model verification indicated that the accuracy of the improved MOD16-sm model increased, with a higher R2 of 0.71, a lower RMSE 0.9 mm, and a lower MAE 0.91 mm, and that the improved MOD16-sm model was convincing. The application effect test of the MOD16-sm model showed that the average relative change rate of annual ET was 1.7%, showing an upward trend, and areas with growth trends of ET also had high vegetation coverage. As for the impacts of climate and land-use changes on ET, ET was positively correlated with precipitation, whereas it had no relevant correlation with air temperature in most areas, and the ET of all land-use types displayed significant increasing trends resulting from climate change. The application effect test demonstrated that ET estimates by the improved MOD16-sm model were reasonable. [ABSTRACT FROM AUTHOR]

Over the past 60 years, China has made fruitful achievements in the field of ocean microwave remote sensing satellite technology. A long-term plan has now been formulated for the development of Chinese ocean satellites, as well as the construction of a constellation of ocean dynamic environmental and ocean surveillance satellites. These will gradually form China's ocean monitoring network from space, thereby playing important roles in future ocean resource and environmental monitoring, marine disaster prevention and reduction, and global climate change. In this review manuscript, the developmental history of ocean microwave satellites and the development status of oceanic microwave remote sensing satellites in China are reviewed. In addition, China's achievements in the field of oceanic microwave remote sensing satellite technology are summarized, and the future development of China's ocean microwave remote sensing satellite program is analysed. [ABSTRACT FROM AUTHOR]

Abstract: Previous studies show that the area of a moraine-dammed lake can provide a good indicator of the significance of its outburst. For a glacier-dammed lake however, because its area and depth fluctuates with the melting of its ice dam, it is difficult to predict the outburst of the glacier‐dammed lake by using its area alone. A characteristic of the surface of Lake Merzbacher is a large amount of floating ice therefore, a method is proposed in this article to extract the area of floating ice on the lake and the area of ice free water in the lake by using Environment and Disaster Monitoring Small Satellite images respectively. Furthermore, based on the area of floating ice extracted through the image information of Lake Merzbacher in 2009 and 2010, we determined the relationship between the ice area and the outburst of the lake, then formulated the Index for hazard of Glacier Lake Outburst Flood (IGLOF) of Lake Merzbacher, which cannot only predict the flood outburst, but also determine the specific outburst period after the lake drainage had occurred. This can be shown in a recalculation of the lake drainages in the years 2009 and 2010. Research results indicate that when IGLOF is less than 0.5 and the lake area is larger than 3km2, the outburst process is in early-warning period and GLOF will occur in the next 5–8days. Also, the successful outburst prediction of Lake Merzbacher in 2011 showed that the index described in this paper provides a quick methodology for forecasting and warning against Lake Merzbacher outburst floods. However，as our research was based on a short observation period (2009-2011) and also cannot be supplemented by other images, it will still be needed to be checked and validated by continuous observation and improvement in future. [Copyright &y& Elsevier]