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Vegetation, a key intermediary linking water, the atmosphere, and the ground, performs extremely important functions in nature and for our existence. Although satellite-based remote-sensing technologies have become important for monitoring vegetation dynamics, selecting the correct remote-sensing vegetation indicator has become paramount for such investigations. This study investigated the consistencies between a photosynthetic activity index (the solar-induced chlorophyll fluorescence (SIF) indicator) and the traditional vegetation index (the Normalized Difference Vegetation Index (NDVI)) among different land-cover types and in different seasons and explored the applicability of NDVI and SIF in different cases by comparing their performances in gross primary production (GPP) and grain-yield-monitoring applications. The vegetation cover and photosynthesis showed decreasing trends, which were mainly concentrated in northern Xinjiang and part of the Qinghai–Tibet Plateau; a decreasing trend was also identified in a small part of Northeast China. The correlations between NDVI and SIF were strong for all land-cover types except evergreen needleleaf forests and evergreen broadleaf forests. Compared with NDVI, SIF had some advantages when monitoring the GPP and grain yields among different land-cover types. For example, SIF could capture the effects of drought on GPP and grain yields better than NDVI. To summarize, as the temporal extent of the available SIF data is extended, SIF will certainly perform increasingly wide applications in agricultural-management research that is closely related to GPP and grain-yield monitoring. [ABSTRACT FROM AUTHOR]

Time-lag effect of vegetation refers to the growth and distribution patterns out of sync with the varying climate. Vegetation cannot adapt immediately the climate change, leading to adjustment time. Previous research has focused mainly on the overall growth of vegetation under climate monitor or changes in a single type of vegetation. It is still lacking in the timelag effect of vegetation types and their response to the climate. Therefore, this study aims to determine the vegetation activity and its response to climate change. The lag time of different vegetation types was also clarified in response to the hydrothermal changes. The Normalized Difference Vegetation Index (NDVI) and meteorological grid data were monthly collected from 1982 to 2015. The GIMMS NDVI dataset was also selected, together with the CRU precipitation and air temperature dataset. Sen + Mann-Kendall trend analysis was then conducted to obtain the interannual change rate of vegetation NDVI in over the past 34 years. At the same time, a significance test was carried out on the NDVI change trend, in order to distinguish the significant improvement and degradation areas. The growth and degradation of different vegetation types were statistically determined after the test. The lagged partial correlation analysis was used to calculate the partial correlation coefficients between vegetation NDVI and precipitation and air temperature in the current month, the previous month, the first previous two months, and the first previous three months. Then, the partial correlation coefficients were also synthesized to obtain the maximum partial correlation coefficient. The lag time was set as the maximum partial correlation coefficient. The dynamic characteristics of the vegetation pattern change over the past 34 years were presented to determine the relationship between vegetation NDVI and climate response. The lag effect was obtained in the different vegetation types on climate response. The results showed that: 1) A spatial distribution pattern of vegetation was achieved in the higher vegetation in northern Xinjiang than that in the southern, and higher in the west than that in the east. There was an overall "greening" trend in the study area. Specifically, the significantly improved and stable unchanged areas accounted for 20.6%, and 65.7%, respectively. The areas insignificant improvement and degradation accounted for 3.3%, and 10.4%, respectively. There was no area with significant degradation. In the spatial distribution, a significant NDVI increase was found in the oasis around the Tarim Basin and the northern section of the Tianshan Mountains, whereas, the Ili region showed a trend of degradation. In vegetation types, the cultivated vegetation shared the largest improvement area, accounting for 75.3%, while, the degraded area of coniferous forest was the largest, accounting for 24.2%; 2) There was a lag response of 72% of vegetation areas to the precipitation at the monthly time scale, with an average lag time of 1.1 months, while the lag response of 70% of vegetation areas to air temperature, with an average lag time of 1.4 months. The higher the lagged correlation coefficient between vegetation and climate elements was, the higher the response speed was. Overall, the vegetation was much more sensitive to the precipitation. 3) Different types of vegetation varied greatly in response to the precipitation and air temperature. Precipitation was the main promoting factor for the grassland, shrubland, and coniferous forest, while air temperature shared the strongest impact on the broad-leaved forest. The lagged partial correlation coefficients between different vegetation types and precipitation were higher than those of air temperature. The response time of different vegetation types to air temperature was longer than that of precipitation. There were lagged effects in the response of different vegetation to both precipitation and air temperature. The correlation between vegetation and precipitation was higher in Xinjiang, indicating a more rapid response of vegetation to precipitation than that to air temperature. [ABSTRACT FROM AUTHOR]

NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spectral response functions in the red and near-infrared channels, making MERSI/NDVI an effective replacement for MODIS/NDVI (MNDVI). Therefore, it is critical to conduct a thorough evaluation of the product's quality. In this study, the consistency characteristics of two normalized difference vegetation index (NDVI) products, FY-3D MERSI-II NDVI and MODIS NDVI, were compared and validated at national and regional scales in China from 2020 to 2021. To assess the consistency of these two NDVI datasets, the correlation coefficient, root-mean-square error, and mean bias error were used. The findings revealed that the spatial distribution patterns of FNDVI and MNDVI were highly consistent across the country at the monthly time scale. The correlation coefficients were greater than 0.9475 for the two years 2020–2021, while the average deviation was between 0.02 and 0.05, and the root-mean-square error was 0.11. Based on the difference in the time consistency between FNDVI and MNDVI, the changes in the monthly NDVI values of the two types of satellites are generally consistent across the country. Among the three typical experimental areas, the relative deviation of the regional time series for products was the highest in Xinjiang. The relative average deviation of FNDVI in other regions was low, and its change trend was consistent with that of MODIS. [ABSTRACT FROM AUTHOR]

Global warming and human activities have posed a great threat to the climatic elements in drylands. The very fragile ecosystem is also highly sensitive to natural and anthropogenic disturbances. It is a serious risk to the evolutionary direction and habitability of oases in recent years. Among them, the Land Surface Temperature (LST) is one of the most important parameters to evaluate the interaction and energy exchange between the earth and the atmosphere. The spatial pattern of the surface temperature and the thermal environment in a region can be attributed to the combined effect of the regional small terrain factors and the properties of underlying surfaces. It is a high demand to explore the spatial distribution of the LST and the "source-sink" effect in the dryland oases, particularly for the local climate change patterns and the influencing factors in the arid regions. However, the oases cold island has put a great challenge on the temperature inversion and high-temperature center using remote sensing in the large-scale basins or open terrain regions. This study aims to analyze the spatial distribution of LST and the influencing factors in the Yanqi Basin in Xinjiang of China. The Mono-window algorithm was also utilized in this case. A quantitative analysis was performed to determine the contribution of the “source-sink” effect in the different land use/cover types to the LST change. The results showed that: 1) The land use/cover type was determined by the spatial distribution pattern of surface temperature and the “source-sink” effect. There was the strongest heat source in the unused lands. The strongest heat sink was observed in the water bodies. There was little influence of grasslands and construction lands on the land surface thermal environment. The intensity of the heat sink effect in the croplands depended mainly on the area of agricultural irrigation and crop growth conditions. 2) The Normalized Difference Vegetation Index (NDVI) was negatively correlated with the LST. The surface temperature decreased by about 1.98 to 2.66 for every 0.1 increase in NDVI. ℃ However, more effects of the vegetation were found on the surface temperature in the cultivated, grassland, and unused land. Less effect of the vegetation was observed in the wetlands. There was also a smaller cooling effect on the vegetation in the construction lands, due to the relatively small proportion of green space in the towns and cities. 3) The slope presented the most significant impact on the gentle slope areas less than 13° at the lower elevation in the basin. The LST increased rapidly with the increase of the slope. There was a weaker effect of slope on the LST at altitudes higher than 1 252 m. The LST decreased gently with the increase of slope. 4) The cold island effect also created a unique thermal structure of the near-surface atmosphere over the Yanqi Basin Oasis. Specifically, the surface temperature rose rapidly at first, with a high-temperature center at the height of 100 to 300 m from the ground, and then gradually decreased, as the altitude rose. The atmospheric temperature inversion layer was generated on June 22. The thickness then exceeded 1 000 m on July 1. The redistribution of the slope direction on the solar radiation disappeared within the height range of 100-300 m above the Yanqi Basin Oasis surface. This absence was attributed to the high-temperature center. This finding can fill the gap in the observation data in the Yanqi Basin in Xinjiang of China. [ABSTRACT FROM AUTHOR]

Vegetation is an indicator of changes in the ecological environment and a key source of feedback on the impact of drought. In this work we selected northern China as the study area and used the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) to assess the relationship between drought and vegetation dynamics at annual and seasonal scales for the period 1982–2015. The results indicated that annual drought showed a slight increase in northern China during this time, and it was mainly attributed to the intensified summer drought; the percentages of areas where significant drying, drying, wetting, and significant wetting occurred accounted for 3.30%, 53.36%, 40.39%, and 2.95%, respectively. Annual NDVI showed a highly significant (p < 0.01) upward trend in northern China, suggesting that vegetation there is greening. However, there was still a significant decrease in the NDVI trend in 13.65% of the study area, and forest accounted for the greatest area among the three land cover types studied. A positive correlation between annual SPEI and annual NDVI was found in most of northern China, and 18.66% of the study area (p < 0.05) were mainly distributed in the central-eastern part of Inner Mongolia, the central part of the Loess Plateau, and northwestern part of Xinjiang Province. Residual analysis found that the percentages of vegetation dynamics affected by human-induced degradation, human-induced improvement, and drought accounted for 13.91%, 47.91%, and 38.18%, respectively; however, 54.15% of the areas affected by drought were located in arid and semi-arid regions and that about 54.61% of those were grassland. The work could provide guidance for vegetation construction and water resource management in northern China. [ABSTRACT FROM AUTHOR]

A planting area of fruit trees has accounted for up to 13% of the national planting area in Xinjiang, China. Local specialty fruits (e.g. walnut, jujube, apricot, fragrant pear, and apple) have been produced in four prefectures of Southern Xinjiang around the Tarim River Basin, accounting for more than 80% of the total fruit planting area in Xinjiang. It is very necessary to real-time and accurately acquire the fruit tree type and area information under the pattern of forest and crops interplanting, further to improve the quality and efficiency of the specialty fruit industry. Taking the continuous area of forest and crops interplanting in Hotan oasis of southern Xinjiang in China as an example, this study aims to extract the structure information of fruit trees from the satellite images. The high-resolution remote sensing image data with abundant texture and spectral characteristics was also integrated with the medium-resolution sensing image data with multi-temporal characteristics. Firstly, an object-oriented programming was used to extract the high-precision boundary of fruit trees parcel using GF-2 (PMS) image data. The GF-2 image data was divided into the winter (February) and summer-autumn (July-September). The Normalized Difference Vegetation Index (NDVI) was determined to identify the spectral and texture features between target objects and other ground objects. Four plots were obtained to gradually eliminate the non-target ground categories. Then, the NDVI time series products were constructed using multi-temporal Sentinel-2 image data. A decision tree model was established to extract the interplanting walnut, pure walnut, jujube, and grape orchard, according to the characteristics of phenology. There were many peaks and troughs in the NDVI time series curve of fruit trees in one year. The peak represented the flourishing period of fruit trees’ growth, and the trough reflected the orchard management (such as irrigation and pruning branches). There were some outstanding and different time windows for the NDVI timing series of pure walnut, interplanting walnut, jujubes, and grapes, particularly for the rarely staggered overlapping. These differences were greatly contributed to the classification of fruit trees. Finally, the multi-phase orchard classification was overlaid the high-resolution of fruit trees parcel, further to obtain the distribution of fruit crops in the study area. The research results show that the major area of fruit was 4.28×105 hm2 here, with 3.31×105 hm² of the walnut orchard (including interplanting and pure walnut), 8.29×104 hm² of jujube, and 1.40×104 hm² of grapes. The area of interplanting walnuts accounted for 63.8% of the total fruit area, The user accuracy and overall classification accuracy were both exceed 90%, and the Kappa coefficient was 0.95, fully meeting the accuracy requirements of agroforestry classification at the county and city level. The relative accuracies of walnuts, jujube, and grapes were 62.1%, 97.8%, and 85.2%, respectively, using remote sensing extraction, compared with the forestry survey in 2019. The areas of jujube and grape using remote sensing extraction were close to the forestry survey data. The walnut planting areas in the Hotan area were mainly distributed in the upper reaches of Yurunkax and Karakax River with suitable soil and water conditions. The jujube trees were mostly distributed in the downstream oasis-desert ecotone, and the grapes were mostly distributed in the sandy desert of the lower reaches of the Karakax River. This finding can provide a strong reference for the fruit tree extraction under the pattern of forest and crop intercropping. [ABSTRACT FROM AUTHOR]

Crop classification is a crucial task in agricultural remote sensing, with the accuracy of such classification heavily relies on field sampling. Reusing historical samples can minimize the reliance on annual field sampling for crop classification. Although previous research primarily focused on the classification accuracy based on the reused historical samples, the underlying factors that influence the accuracy have not been adequately investigated. In this study, we employed a two-dimensional convolutional neural network (2D-CNN) model for crop classification reusing historical samples and investigated the factors influencing the classification accuracy. First, we calculated a normalized difference vegetation index (NDVI) time series from historical data to characterize crop growth patterns. Secondly, we assessed three different time-series construction methods. Subsequently, we used the 2D-CNN model to automatically extract abstract features of crop growth patterns. Finally, we designed various strategies for reusing historical samples to explore the influencing factors. Experiments were conducted in Kuitun, Xinjiang Uygur Autonomous Region, China, from 2016 to 2020, employing a long time series of Sentinel-2 images as remote sensing data. Our results indicated that optimal 2D-CNN models using an irregular satellite image time series (irSITS) outperformed random forest models in inter-annual classifications (the overall accuracies for 2016–2020 were 0.78, 0.61, 0.89, 0.89, and 0.70, respectively). In addition, we identified that the primary factors affecting classification accuracy were 1) the time-series construction method used; 2) the crop growth patterns; and 3) the sample diversity. By reusing historical samples and considering the factors influencing classification accuracy, this study provides valuable insights into high-quality crop classification mapping under limited field sample conditions. [ABSTRACT FROM AUTHOR]

• Synergistic effects of surface and groundwater on desert riparian vegetation were analyzed. • The cautionary depth to groundwater for vegetation expansion in the lower Tarim River is 4.0–5.0 m. • Flooding by surface water (lateral water transfer coverage) acts as the main driver for the expansion of vegetation in arid areas. • A zonal ecological water transfer strategy helps to promote effective ecosystem restoration in the region. Restoring degraded ecosystems in drylands is challenging because of low precipitation and high evapotranspiration. Water availability determines the existence and spatiotemporal distribution patterns of key endangered species struggling to survive in the severely damaged desert riparian ecosystem of the lower Tarim River (LTR) in NW China. Ecological water flow restoration measures with high costs and uncertainties have been implemented since 2000. The key task for scientists is to assess the success and drawbacks of this restoration project and propose specific recommendations for stakeholders to optimize the ecological benefits of the project in a cost-effective manner. In this study, Landsat time-series data from 1990 to 2020 were used to extract vegetation index values from river corridors along the LTR and explore the spatiotemporal patterns of desert riparian forest (DRF) vegetation. Based on land use and surface water data, we extracted the interannual spatial distribution range of vegetation and surface water, and selected several sample plots to demonstrate the influence of surface water and groundwater on vegetation change trends. The study results showed the following. 1) From 1990 to 1999, the vegetation slowly degraded; from 2000 to 2012, the extent of vegetation restoration was approximately 1 km from the river; and from 2013 to 2020, the extent of vegetation restoration at the Yingsu and Karday sections increased to 4.5–6.0 km, while that at the Arghan and Yiganbujima sections remained within 1 km from the river. 2) The vegetation growth rate increased substantially and was spatially heterogeneous when the groundwater level reached 4.0–5.0 m and was influenced by surface water divergence; the average vegetation expansion rates per square km for P 1 –P 4 were 0.017 km2/a, 0.011 km2/a, 0.005 km2/a, and 0.002 km2/a, respectively. 3) Flooding by surface water was the main driver of vegetation expansion when groundwater was stable and elevations in surface vegetation area were often accompanied by the spreading of surface water. Surface water was significantly correlated with vegetation cover and normalized difference vegetation index (NDVI) (Water pixels–Vegetation cover [P 1 : R2 = 0.57, P < 0.001; P 2 : R2 = 0.45, P < 0.01]; Water pixels–NDVI [P 1 : R2 = 0.22, P < 0.05; P 2 : R2 = 0.40, P < 0.01]). The increase in surface vegetation has alleviated the desertification process in the LTR, and the survival of the poplar has been mitigated. The results of this paper can help decision makers to formulate reasonable ecological water conveyance strategies and realize sustainable management of DRF ecosystems in arid zones. [ABSTRACT FROM AUTHOR]

Four modes of NDVI changes, Mode α, Mode β, Mode γ and Mode δ can be classified due to three precipitation distribution types, A-, B- and C-type. In order to increase the probability of continuous growth of vegetation in a timely manner and to improve efficiency of precipitation, the probability of occurrence of α, β and δ Modes (these three changes are mainly for vegetation growth) should be increased as much as possible, and the vegetation grown of the β Mode is the best. In EGS, Type B of precipitation is not associated with δ Mode. In MGS, γ Mode increases rapidly while the proportion of the β Mode decreases, and the other modes disappeared. The reason that γ Mode increases rapidly in MGS may be excessive precipitation which keeps the soil moisture in a state of supersaturation, thus inhibiting the growth of desert vegetation. Type A most likely gives rise to γ Mode and β Mode, and Type C only cause δ Mode in EGS. In MGS, α and β Modes of NDVI are for Type A , while α, β and γ Modes are for Type C. Therefore, it can be investigated that Type B and Type A of precipitation are the optimum precipitation distribution type that can effectively maintain the trend of lush vegetation in EGS and MGS, respectively. [Display omitted] • Shortening time lags of NDVI responses to daily precipitation over time. • Classified three temporal distribution types of a precipitation process. • Different precipitation processes benefit availability of moisture for diverse stage. • Precipitation changes tend to impact NDVI changes given dry period of > 18 days. Vegetation in arid/semiarid areas in the northern hemisphere is highly sensitive to precipitation variability. Understanding the change of dryland vegetation and its response to spatiotemporal patterns of precipitation changes is critical to delve impacts of meteorological droughts on dryland vegetation. Based on the Normalized Difference Vegetation Index (NDVI) and precipitation data, we delineated the spatiotemporal pattern of trends of the arid/semiarid plant growth and its responses to daily precipitation changes in the northern Xinjiang. The results indicated several regular characteristics in the NDVI responses to precipitation at unequal time and spatial scales during the growing season, i.e. April-October. It identified shortening time lags of NDVI responses to precipitation over time. This study found for the first time that precipitation changes tend to impact NDVI changes given dry period length (DL) of > 18 days, and the effect of precipitation process was more significant. Three temporal distribution types of a precipitation process were classified, i.e. A-type or B-type precipitation process, which are characterized by two consecutive rainfall days with less or larger precipitation amount on the first day than the next day, and C-type precipitation process, which is characterized by three or more consecutive rainfall days. A- and B-type precipitation processes can benefit availability of moisture in the medium and early stage of the growing season respectively. Precipitation of 6.1–12.0 mm is the most favorable condition for the growth of desert vegetation. This study highlights multifarious vegetation responses to different precipitation processes and provides theoretical ground for ecological restoration and conservation in a changing environment across northern Xinjiang, China, and also in drylands in other regions of the globe. [ABSTRACT FROM AUTHOR]

The ecological evolution and relevant factors in semi-arid and arid regions is a research focus on climate changes. One such region, the mountain-oasis-desert coupling system in Xinjiang, China, is sensitive to global climate changes because it has a fragile ecological environment. This study attempted to investigate the temporal-spatial variations of surface soil moisture, vegetation cover and their influencing factors of mountain-oasis-desert coupling system in Xinjiang from 1986 to 2015. The results showed that: (1) The surface soil moisture increased in most of the ecoregions of Xinjiang from 1986 to 2015 whereas the vegetation cover decreased in the same period. (2) The comparison of the correlations between soil moisture content (SMC) and the variables of precipitation and temperature revealed that precipitation is an important supplement source of SMC. The variation characteristics of surface soil moisture are closely related to the dry and wet soil conditions. (3) The comparison of the correlations between the normalized difference vegetation index (NDVI) and the variables of precipitation and temperature revealed that the different responses of vegetation to precipitation were related to the gradually decreased precipitation pattern in the mountain-oasis-desert system. Meanwhile, vegetation was found to be more sensitive to precipitation in deserts and oases and more sensitive to temperature in mountainous regions. In each ecoregion, the variation cycles of SMC and vegetation cover were slightly different, but it was found that SMC is more likely to be affected by temperature and precipitation rather than vegetation cover. Overall, surface soil moisture and vegetation cover products have a great potential to be effectively used in investigating landscape pattern variations, which is very important theoretically and practically for the mountain-desert-oasis system in arid areas. [ABSTRACT FROM AUTHOR]

• Drought analysis with spatial–temporal variations was performed. • SIF contains information on SM, PPT, SPEI, and ET. • SPEI and ET have higher sensitivity in response to drought than SM and PPT. The effectiveness of satellite solar-induced chlorophyll fluorescence (SIF) for drought evaluation was measured in this study. Here we compare the association of SIF with soil moisture (SM), precipitation (PPT), standardized precipitation evapotranspiration index (SPEI), and ratio of evapotranspiration (ET) to monitor the drought stress on crop growth. A severe drought occurrence was observed in 2015 as compared to other years between 2007 and 2017 in Xinjiang of China. In the period of this drought, the changes of SIF and SIF normalized by absorbed photosynthetically active radiation (Φ F) were obstinate. SPEI and ET were observed with higher sensitivity and much more constant decline in response to drought than SM and PPT. Moreover, Φ F is highly sensitive to drought than SIF, SIF normalized by photosynthetically active radiation (SI F PAR ) , enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI). The results demonstrate that satellite SIF provides deep insight for drought detection. [ABSTRACT FROM AUTHOR]

• A VCCP model is developed for quantifying compound risks on vegetation in Xinjiang. • Spatial quantitative response of vegetation to compound dry-hot event is identified. • Vegetation loss reaches peak in August conditioned on compound extreme conditions. • Results are helpful for decision maker to make extreme mitigation recommendations. Extreme events (e.g., drought and heatwave) occur frequently and intensively with climate change, where the combination of dry and hot events has catastrophic impacts on terrestrial ecosystems. It is challenged to quantitatively understand the vegetation vulnerability under compound dry-hot extremes. In this study, a vine-copula conditional probability (VCCP) model is proposed to quantify the impacts of dry-hot events on vegetation dynamics, where the dependence patterns of the Normalized Difference Vegetation Index (NDVI), standardized precipitation evapotranspiration index (SPEI), and standardized temperature index (STI) are modelled through vine copula functions. The VCCP model can evaluate the conditional probability of vegetation loss under multiple dry-hot events and reveal the temporal and spatial patterns of vegetation vulnerability of different land-use types. Then, the VCCP model is applied to Xinjiang province, where the ecological environment is fragile and soil erosion is serious. The dependence patterns among NDVI, SPEI and STI in summer season (June-August) during 1983-2015 are identified. The main findings are: (i) spatial and temporal responses of vegetation to drought and hot events present distinctively; (ii) under the extreme scenario, the average probability of vegetation loss below the 50th percentile in August reach 58.2%, followed by July (with 44.0%) and June (with 33.1%); (iii) the northern and southwestern regions of Xinjiang (especially for the grassland in the mountain areas) have the worst resistance to extreme dry-hot events in summer season. The findings can provide insights into the impacts of compound extremes on vegetation conditions and help decision makers take effective and efficient ecosystem management to mitigate climatic disasters. [Display omitted] [ABSTRACT FROM AUTHOR]

The dynamics of the ecosystem represented by vegetation under the influence of human activities have become an important issue in the study of the regional ecological environment. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. However, the spatiotemporal patterns and evolutionary trends of anthropogenic impacts on vegetation dynamics in Xinjiang are still unclear. In this study, the anthropogenic impacts on vegetation dynamics were quantitatively assessed by combining the improved normalized difference vegetation index (NDVI) prediction model and the residual analysis method in Xinjiang, China. The human driving factors were analyzed with the support of a stepwise multiple regression model for vegetation changes at the county scale. Based on trend analysis and the Hurst exponent, the spatiotemporal characteristics and evolutionary trends of the impact of human activities on vegetation change were discussed. The results show that (1) the NDVI values in Xinjiang showed a gradually increasing trend at a rate of 0.005/10 years from 1982 to 2018, and the vegetation dynamics mainly showed significant improvements (57.09% of the vegetated areas), especially for crops. (2) The anthropogenic effects of vegetation changes in Xinjiang mainly included positive impact increases (43.22% of the vegetated areas) from 2000 to 2018. Human activities promoted the increase in the NDVI of various vegetation types. Both the positive and negative impacts of human activities increased over the study period, and the growth rate of the positive influence (0.08%/10 years) was higher than that of the negative influence (0.04%/10 years). (3) The cultivated area, GDP of primary industry, and population are the main anthropogenic factors causing the increase in NDVI, which dominate the vegetation greening in 30.34%, 29.22%, and 28.09% of the counties in Xinjiang, respectively. The animal husbandry population, agricultural population, and livestock number are the main anthropogenic factors causing the decrease in NDVI, which dominate the vegetation degradation in 23.60%, 21.35%, and 17.98% of the counties in Xinjiang, respectively. (4) The evolutionary trend of the anthropogenic impact on vegetation dynamics in Xinjiang will be dominated by anti-persistence (53.84% of the vegetated areas), thereby mainly showing that the positive impacts continued to increase (22.56% of the vegetated areas), especially for crops, shrubs, grasslands, and alpine vegetation. Our results are helpful in understanding the characteristics and evolutionary trends of vegetation changes in arid areas caused by human activities and are of significance as a reference for policymakers to appropriately adjust policy guidance in a timely manner to promote the protection and sustainable development of fragile ecosystems. [ABSTRACT FROM AUTHOR]

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change. [ABSTRACT FROM AUTHOR]

• A two-step CNN approach was proposed to downscale SIF from 0.05°, 0.005° to 0.0005°. • 0.0005° SIF had higher cotton yield prediction accuracy at field-scale than coarse SIF. • Using only 0.0005° SIF estimated cotton yield at field-scale with acceptable accuracy. As the largest cotton-growing region in China, Xinjiang has contributed more than 80% of the total national cotton production in recent years. Timely and accurate estimation of cotton yield in Xinjiang is important for sustainable agricultural development and food security. However, most current studies have been devoted to the linkage of crop yield with remotely sensed reflectance and climate parameters. This has caused numerous uncertainties due to that these explanatory variables are unable to quickly reflect the actual photosynthetic dynamics of crops. Solar-induced chlorophyll fluorescence (SIF), as a direct proxy of plant photosynthesis (gross primary productivity, GPP), has recently been suggested to be a promising method for crop yield estimation, but the spatial resolution of current SIF products derived from satellites is usually very low (such as Global OCO-2 SIF (GOSIF): 0.05°). This greatly limited the ability of SIF to accurately estimate field-scale cotton yield in Xinjiang. Here, we first proposed a two-step convolutional neural network (CNN) strategy to downscale the monthly GOSIF products sequentially from 0.05°, 0.005° to 0.0005° to match the size of cotton field parcels, and then linear regression and random forest (RF) regression were respectively conducted using the monthly downscaled SIF product (CNN-SIF) to assess its feasibility to estimate field-scale yield. Results showed that the proposed stepwise approach for downscaling GOSIF worked well, indicating a high goodness of fit (R2 > 0.85) with the referenced SIF as well as strong correlations to both GPP products and fraction of photosynthetically active radiation (FPAR) (the median r > 0.90). On this basis, preferable accuracies (the optimal R2 = 0.62 and the ratio of prediction to deviation = 1.64) were also achieved for our proposed cotton yield estimation models in the Mosuowan region, Xinjiang only by the 0.0005° SIF products. With the assistance of NDVI (normalized difference vegetation index), the higher performance was given (R2 = 0.67 and RPD = 1.72). This study reveals the importance of finer-resolution SIF products for accurate crop yield estimation and offers a promising and practical approach for estimating agricultural yield, especially for fragmented farmlands. [ABSTRACT FROM AUTHOR]

Living in greener areas may reduce adiposity, but epidemiological evidence on this topic is still inconsistence and limited, especially in rural areas. We performed a cross-sectional study among 4651 Uyghur adults in rural areas in Xinjiang province, northwestern China, from May to September 2016. We measured residential greenness levels using satellite-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) in 100 m, 300 m, 500 m, and 1000 m buffers around each home address. Body height, weight, and waist circumference were assessed according to recommended guidelines. Data on baseline characteristics and confounders were collected using a questionnaire. We used generalized linear mixed models to estimate the associations of residential greenness with overweight/obesity prevalence and obesity-related anthropometric indices. Higher residential greenness levels were associated with lower waist circumference and body mass index levels, as well as with a lower odds ratio of peripheral overweight/obesity prevalence. No significant association was found for greenness and central obesity prevalence. The associations persisted in magnitude and direction across several sensitivity analyses we performed. Stratified analysis suggested that the associations were generally stronger in older adults than those in younger adults. Additionally, neither air pollutants nor physical activity significantly mediated the associations between greenness and obesity. Our results suggest that higher residential greenness were associated with lower odds of overweight/obesity and lower obesity-related anthropometric indices among rural Uyghur adults in China, especially for older adults. • The first study to explore the effects of greenness on obesity in Uyghur adults. • Higher greenness exposures were associated with lower BMI and WC levels. • Higher greenness was associated with lower peripheral overweight/obesity prevalence. • The associations were stronger in older participants. • Neither air pollutants nor physical activity mediated the associations. [ABSTRACT FROM AUTHOR]