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[Display omitted] • SIF and NDVI datasets had large differences on Yun-Gui Plateau and northwestern China. • Drought propagation time based on NDVI is larger than that based on SIF. • Drought had a similar impact degree in SIF and NDVI. • Both SIF and NDVI showed similar performance when monitoring crop yield. An increase in global warming has resulted in a global increase in meteorological droughts. As droughts play a key factor regulating vegetation growth, they inevitably have social and economic effects. Understanding vegetation dynamics and its response to climate change is therefor highly important to reveal the behavior mechanism of terrestrial ecosystems and to guide environmental management. In this investigation, Solar-induced Chlorophyll Fluorescence (SIF) and the Normalized Difference Vegetation Index (NDVI) were applied as vegetation indicators to describe vegetation dynamics and meteorological drought was described using the standardized precipitation evapotranspiration index (SPEI). Bias correction of precipitation and crop yield data of major crop producing areas was also used. By employing correlation coefficient of the maximum absolute value, a drought impact degree and propagation time to vegetation were defined. Dimensions of SIF and NDVI were unified by normalization. Results indicated that: (1) SIF and NDVI generally had a high level of consistency over China, and NDVI had a higher normalized value distribution level than SIF for different vegetation types. (2) Drought generally had a negative impact on vegetation in northern China and a positive impact in southern China. (3) Drought propagation time based on NDVI was larger than that based on SIF. (4) SIF had some advantages when monitoring crop yields among different crop producing areas. Results from this investigation will enable a greater understanding of SIF and NDVI for their application to monitor droughts and crop yield. [ABSTRACT FROM AUTHOR]

• Satellite data were combined with transfer learning method to identify cotton area. • Two frameworks were formed for cotton mapping by integrating LSTM and RF with TrAdaBoost. • The improved approaches performed well in cotton detection with few target samples. • The cotton distribution map in Uzbekistan was generated. Cotton is an important cash crop and strategic material in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely cotton distribution maps are crucial for monitoring and managing cotton cultivation system. Remotely sensed data have been widely used in cropland mapping, whereas relatively less attention has been paid specifically to cotton mapping partly due to the difficulty in obtaining the training samples over large regions. To resolve this issue, this study introduced the instance-based transfer learning to identify the cotton cultivated areas using the remotely sensed images. The annual time series of normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images was adopted as input features. The random forest (RF) and long short-term memory (LSTM) algorithms integrated with the Transfer AdaBoost (TrAdaBoost) algorithm were adopted to generate the RF-based and LSTM-based TrAdaBoost approaches, respectively. The experiments were conducted in Arkansas State of the United States, where the cropland data layer (CDL) was available and utilized as a source of auxiliary data. The results showed that both the RF-based and LSTM-based TrAdaBoost performed better than the original RF and LSTM under the condition that the training samples in the target domain were limited. The advantages of transfer learning were much greater when the percentage of training samples from the source domain equals 80%. The two transfer learning approaches were then applied to identify the cotton cultivated areas in Uzbekistan. The cotton areas detected by MODIS images in 2018 were agreed well with the statistics at the sub-national level, with the R2 values of 0.57 and 0.64, respectively. These results demonstrate the potential of the RF-based and LSTM-based TrAdaBoost approaches in generating the cotton distribution maps when there are few samples in the target domain. [ABSTRACT FROM AUTHOR]

• Four critical degradation regions were found. • Effects of environment on NDVI were different under different land uses. • Lag effects between NDVI and climate were studied. The response between NDVI and climate has been a hot topic in recent research. Based on the data of GIMMS NDVI 3g and five climatic factors (precipitation, humidity, atmospheric pressure, air temperature, and sunshine hours), we evaluate spatial-temporal variation characteristics of the Normalized Difference Vegetation Index (NDVI) on the Loess Plateau and its response to climate by Sen+Mann-Kendall, redundancy analysis (RDA) variance decomposition and correlation analysis methods. The results showed that: (1) At the annual scale, the change of vegetation manifested an overall upward trend. However, there was severe degradation (2.49%) in the region, which was mainly distributed in Hohhot City, eastern Yan'an City, eastern Qinghai Province, and along Baoji-Xianyang and Xi'an-Weinan linear district. (2) The correlation between NDVI and atmosphere pressure was negative, while the correlation between NDVI and other four climatic factors was positive. (3) The independent impact and the combined impact of climatic elements, topographical elements and geographical elements were different under different land uses. The uneven spatial distribution of NDVI under different land uses was driven by climatic elements groups. (4) The bi-direction lag effects between NDVI and climatic factors showed the characteristics of short (1–3 months) or long (3–6 months) term under different land uses. Except for atmospheric pressure, the effects were positive in the short-term and negative in the long-term, while atmospheric pressure exhibited the opposite. This study can be conducive to forecast and evaluate the vegetation dynamics under the background of global climate change and provide a theoretical basis for the protection of the regional ecological environment. [ABSTRACT FROM AUTHOR]

[Display omitted] • NDVI shows an overall increasing trend from 2000 to 2020. • The Hexi region shows a warming and wetting trend. • Extreme precipitation is more important for NDVI. • The lag effect of NDVI on climate extremes varies by indicators and spaces. Climate extremes significantly affects vegetation dynamics, and understanding the relationship between dryland vegetation and climate extremes is crucial for regional ecological security and disaster warning. According to meteorological and satellite remote sensing data, the paper analyses the dynamics of normalized difference vegetation index (NDVI) and climate extremes in the Hexi region from 2000 to 2020 using Theil-Sen Median, Mann-Kendall and linear regression methods; the relationship between climate extremes and vegetation dynamics is revealed using Pearson correlation and boosted regression trees (BRT) models. On this basis, the time-lag effect of NDVI in response to climate extremes was further investigated. The results show that the NDVI in the Hexi region increased from 2000 to 2020, with significant greening (67.72 %) and a small amount of degradation concentrated in the desert in the northwest and the oasis in the southeast. The correlation between climate extremes indicators and NDVI shows a large spatial heterogeneity, with extreme precipitation showing a high importance for all regions except oasis. NDVI has no lag or 1 month lag for extreme temperature, no lag for max 1-day precipitation amount (Rx1day) for areas other than mountainous areas, and a 3-month lag for max 5-day precipitation amount (Rx5day) for extensive areas. The results of this study could serve as a reference for the study of the relationship between vegetation dynamics and climate extremes in arid and semi-arid regions, and provide a theoretical basis for the establishment of scientific and reasonable strategies for regional vegetation restoration and conservation. [ABSTRACT FROM AUTHOR]

• Soil salinity classification for semi arid areas. • Challenge similiar reflections for classification. • Salinity indices and vegetetion index added as a spectral channel to image. • Comparison deep learning (semantic segmentation) and machine learning classification. Soil salinity may occur naturally through pedogenetic processes or may result from abiotic factors resulting from human activities such as irrigation with poor quality water, lack of drainage and land development. The infertility of agricultural lands due to soil salinity brings many economic and social problems on a local and global scale. In this study, the salinity problem in the Harran Plain, one of the largest agricultural areas in Turkey, was investigated using deep learning-based U-NET algorithm. Different combinations of Normalized Difference Salinity Index (NDSI), Salinity Index I (SI), Salinity Index II (SII), and Normalized Difference Vegetation Index (NDVI) indices integrated into the RapidEye multispectral image to increase the segmentation accuracy. The most successful result (93.78% overall accuracy) was achieved when the algorithm was trained with 300 iterations and only the SII index was added to the original image. The same images were also segmented by the SVM method, and the U-NET deep learning architecture was able to detect soil salinity about 20–32% more accurately than SVM for three different 5-band test images. This study demonstrates that the delineation and mapping of soil salinity can be done automatically thanks to a deep learning (DL) network with greater accuracy, less time and effort, and without requiring continuous in-situ Electrical Conductivity (EC) measurements and repetitive supervised learning for each new satellite image. [ABSTRACT FROM AUTHOR]

• Correlation coefficient between Arctic NDVI and active layer thickness is higher than that between NDVI and soil temperature at 0–28 cm depth. • When precipitation and snow water equivalent remained unchanged, the partial correlation between NDVI and active layer thickness was 0.711. • Permafrost degradation is a dominant factor to control NDVI increase, while precipitation and snow water equivalent had little effect. • Ecological services of permafrost should be included in global ecosystem service value accounting system. As an important component of the climate system, permafrost responds significantly to climate change, and its impact on the ecosystem cannot be ignored. In this study, we analyzed the temporal and spatial variation trends of the normalized difference vegetation index (NDVI) in Arctic permafrost regions and revealed the correlation between the active-layer thickness (ALT), soil temperature, and NDVI change. Using the partial correlation method, we assessed the ecological regulation service of permafrost to the ecosystem. The results showed that both the average annual maximum and summer NDVI values in the Arctic region followed a significant increasing trend from 1982 to 2015. The average correlation coefficient (ACC) between Arctic NDVI and ALT was 0.35, followed by the ACC (0.33) between NDVI and soil temperature at 7–28 cm depth, and had a lower ACC (0.31) at 0–7 cm ALT. When the precipitation and snow water equivalent (SWE) remained unchanged, the partial correlation between NDVI and ALT was 0.711, which was a significant positive correlation. It also showed that permafrost degradation was the dominant factor controlling Arctic NDVI increase, whereas precipitation and SWE had little effect. The study revealed the impact of permafrost on NDVI change, deepened our understanding of the importance of permafrost degradation for ecosystem services, and effectively filled the gap that tundra ecosystem services value has been ignored in the global ecological service value assessment. [ABSTRACT FROM AUTHOR]

• SOM was measured in eight particle size for 84 samples in northern China. • The distribution of SOM in each size class basically decreased from east to west of the study region. • Though SOM mostly fell with size class, 25 % of the samples showed an increase or no monotonic trend. • Predictive models were built for the relationships between SOM and particle size. Soil wind erosion is one of the most important surface processes in the arid and semi-arid regions of northern China, which serves as an important driver for the migration and turnover of soil organic matter (SOM) and should be given full attention in regional as well as in global carbon cycles. Wind tends to transport fine soil particles typically enriched with SOM, which, however, has been frequently ignored and the mean SOM of the bulk soil is instead used in the carbon budgets at various scales. The objective was then to characterize the variations of SOM with soil particle size and to establish the predictive models for SOM in the wind erosion region of northern China. A total of 84 soil samples were collected in the wind erosion region of northern China, covering 3 climatic regions, 17 erodible soil types, and 5 land uses. Each sample was sieved into 8 size classes of 0–63, 63–100, 100–150, 150–200, 200–250, 250–300, 300–400 and 400–880 μm using the classical dry sieving method, and the potassium dichromate volumetric method was applied to determine SOM for each class. No matter for which size class, SOM basically decreased from east to west, which was consistent with the spatial distribution trends of precipitation and the normalized difference vegetation index (NDVI). The magnitudes and variations of SOM with particle size, moreover, differed among soil types and land uses. For most of the soil samples, i.e., 63 out of the total 84 (75 %), SOM generally decreased with particle size. Whereas for the remaining samples, SOM tended to increase or remained rather consistent as the particle size increased. The relationships between SOM and particle size highly depended on the climatic factors of precipitation and temperature, as well as the vegetation type and health as indicated in the magnitudes of NDVI. The corresponding models developed exhibited high accuracies for SOM prediction, which were capable of explaining 70–90 % of the total SOM variations. These findings hold important implications for understanding the mechanisms of carbon loss in the wind erosion region of northern China. [ABSTRACT FROM AUTHOR]

• Limited phosphorus availability hampers grassland productivity in Banni; PAC and PSM play key roles. • Livestock movement introduces organic matter and PSM, enhancing phosphorus accessibility for plants. • Effective phosphorus management considering livestock and organic contributions can boost grassland productivity. The Banni grasslands, located in the Kachchh district of India, have experienced significant degradation and a decrease in grassland productivity. Phosphorus is a crucial nutrient for grassland productivity, and its availability is often limited in these ecosystems. The study is a maiden attempt to understand the link between phosphorus availability, measured by the phosphorus activation coefficient (PAC), phosphate solubilizing microorganisms (PSM), vegetation patterns, indicated by normalized difference vegetation index (NDVI) and its relationship with livestock movement. Soil samples were collected from 45 locations spread across the three divisions of Banni grassland, and various soil parameters including phosphorus content, organic carbon, and microbial activity were analyzed. The results showed that the concentration of available phosphorus (AP) varied significantly among different divisions of the grassland, with division 1 having the highest AP concentration. The phosphorus activation coefficient (PAC) was also highest in division 2. Additionally, PSM diversity was significantly different among divisions, with division 2 exhibiting the highest PSM values. The NDVI values, representing vegetation density, varied across divisions, with division 1 having the highest overall NDVI. Statistical analysis revealed significant positive correlations between PAC, PSM, and NDVI. The research emphasizes the significance of PSM driven phosphorus availability in influencing vegetation dynamics within the Banni grassland ecosystem. The movement of livestock emerges as a key factor in introducing organic matter and phosphate solubilizing microorganisms, thereby facilitating the conversion of total phosphorus (TP) into forms accessible to plants. The study indicates that implementing effective phosphorus management practices, taking into account livestock movement and organic contributions, could enhance grassland productivity and ensure the long-term sustainability of the ecosystem. These findings offer valuable insights for more holistic and nuanced understanding of the complex relationships between phosphorus availability, phosphate solubilizing microorganisms, vegetation patterns, and sustainable management in the Banni grasslands and potentially in other ecosystems facing similar challenges. [ABSTRACT FROM AUTHOR]

• Plant diversity is higher in pastures with grazing donkeys than in abandoned pastures. • Catalan donkey grazing reduces phytovolume and biomass in pastures. • Sentinel-2 images can help monitoring donkey grazing activities. • In Puy de Cinca, northeast Spain, forest expansion in mountain landscapes has increased 8-fold in 6 decades. • Current and historic aerial imagery contributes to assessing mountain landscapes changes. Traditional Mediterranean Mountain landscapes in Spain have suffered dramatic environmental and social changes over the last seven decades. The loss of these landscapes has had consequences for biodiversity, soil erosion, landscape quality and ecosystem services as croplands and pastures were mainly converted into forests and scrublands. Many animal breeds present in traditional land uses such as the Catalan donkey (Equus asinus var. catalana), are also at risk of extinction, but can provide environmental services while recovering traditional landscapes. In Puy de Cinca, a village in mountainous northeast Spain, we studied how grazing by Catalan donkeys reduces pasture biomass and the effects on plant diversity in pastures. We used Sentinel-2 satellite imagery to calculate the Normalized Difference Vegetation Index (NDVI), a biomass sensitive spectral index, throughout the grazing period to monitor pasture biomass and compared it to pastures without grazing. We also calculated several plant diversity indicators in pastures with and without grazing donkeys. Furthermore, we studied land use changes over the last seven decades using old (1956) and current (2018) aerial images, with forest, agricultural lands, and trails interconnecting the village being mapped to understand landscape changes. The results indicated a great increase in the forested area (348.3 ± 17.0 ha). Meanwhile, a severe decrease in cropland area (73 %) and trail length (62.5 %) was also observed. Concerning the effect of donkey grazing, biomass was lower in pastures with grazing donkeys, with NDVI values decreasing once donkeys started grazing. Nevertheless, plant diversity was higher in pastures with grazing donkeys than in abandoned pastures. This study demonstrated the capacity of low-to-moderate-intensity donkey grazing to improve plant diversity and reduce biomass in pastures. Furthermore, the study of land use changes allowed an understanding of landscape dynamics, which can help address the social and environmental recovery of the village. [ABSTRACT FROM AUTHOR]

• Landscape position–silt–NDVI interactions determine surficial SOC. • Landscape position affects silt and NDVI, in turn collectively driving SOC. • Silt, NDVI, and altitude exhibit low, high, and low states respectively at high SOC. • BN effectively assesses the cause-and-effect relationship between SOC and factors. Understanding the cause-and-effect relationship and the factor interactions between soil organic carbon content (SOC) and environmental properties remain challenging at a catchment scale. Using an extensive dataset (n = 1126), we aimed to develop a Bayesian network (BN) to simulate the response of surficial SOC (0–5 cm) to multiple interacting environmental factors across a semi-humid catchment (1.1 km2) on the Chinese Loess Plateau. We found that landscape position, silt content, and Normalized Difference Vegetation Index (NDVI) controlled SOC, with the functional pathways being that landscape position affected silt content and NDVI, then silt content and NDVI collectively affected SOC. Low landscape position, low silt content and high NDVI were associated with high SOC. The interactive effect of silt content and NDVI on SOC was antagonistic whereas the strongest contribution of silt content could be partly eclipsed by NDVI. Moreover, when SOC was in high state, low state silt content, high state NDVI, and low state altitude were convincingly inferred with probability in 49%, 65%, and 63%, respectively. These findings highlight the role of the landscape position–vegetation–soil texture interactions in accounting for SOC variation at the catchment scale. We also emphasize the potential of the BN as an effective tool for detecting cause-and-effect relations in terrestrial ecosystem. [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 soil δ13C showed a moderate spatial heterogeneity in an alpine-gorge region. • The GWR model was a useful tool for mapping the isoscapes of soil δ13C. • Soil C/N and NDVI were the main factors influencing the spatial variation of soil δ13C. Soil stable carbon isotope (soil δ13C) can reflect soil carbon metabolic processes and record environmental and vegetation change information, so mapping the spatial distribution of soil δ13C (isoscapes of soil δ13C) can help us better understand the spatial variability of ecosystem carbon cycles. However, efficient approaches for obtaining the isoscapes of soil δ13C remain challenging, especially in mountainous areas with complex terrain. A total of 150 soil samples were collected and their soil δ13C was measured to investigate the spatial variation of soil δ13C in an alpine-gorge region on the eastern Qinghai-Tibetan Plateau which is characterized by complex terrain. Four prediction methods included ordinary kriging interpolation, multiple linear regression, random forest regression, and geographically weighted regression (GWR) were selected and compared to find the best prediction model for mapping the isoscapes of soil δ13C. The pathways of vegetation, topography, soil, and spatial factors influencing the spatial variability of soil δ13C were explored using variance partitioning analysis and structural equation model. The soil δ13C was significantly different among vegetation types, and ranged from −27.155‰ to −9.647‰. The spatial heterogeneity of soil δ13C showed moderate variation, and was dominated by spatial structural factors. The GWR model had higher prediction accuracy in the modeling soil δ13C in comparison to other models. Soil carbon to nitrogen ratio and normalized difference vegetation index were the main factors determining the spatial variability of soil δ13C, and other topographic and soil factors indirectly regulated the spatial distribution of soil δ13C by influencing these two factors. Our results provided a useful tool (GWR model) for mapping the isoscapes of soil δ13C and explored the factors controlling the spatial variability of soil δ13C in the alpine-gorge region on the eastern Qinghai-Tibetan Plateau, which are important for an in-depth understanding of soil carbon cycling in mountain ecosystems. [ABSTRACT FROM AUTHOR]

• Drivers of vegetation greening were examined at the inter-annual and spatial scales. • Human activities were the dominant driver of variation in vegetation during 2001–2020. • Forests following row-belt distribution with low coverage is more suitable for application in drylands. Vegetation growth of the Chinese Loess Plateau (CLP) has improved since the launch of the Grain to Green Programme. This study examined the mechanism of spatial differentiation of the Normalized Difference Vegetation Index (NDVI) and the relative contributions of climate variations (CV) and human activities (HA) to variations in inter-annual NDVI in the CLP for 2001–2020. The response of NDVI to afforestation was examined and the trend of projected future NDVI was identified using the Moderate Resolution Imaging Spectroradiometer and other correlative data. The results showed that: (1) precipitation, sunshine duration, relative humidity, solar radiations, and vegetation type were the dominant factors affecting the spatial distribution of NDVI. All interactive explanatory powers of two factors exceeded those of any individual factor, and the interactions between natural and anthropogenic factors had significant effects on NDVI. (2) Variation in NDVI in 88.77% of the total area was influenced by the combined effect of HA and CV, and HA was the dominant factor with relative a contribution of 68.44%. (3) There was an increasing (2001–2005) followed by a decreasing (2006–2019) pattern of annual area of afforestation in each province. The seven provinces showed extremely significant correlations (p < 0.01) between the cumulative area of afforestation and NDVI. (4) The trend in the change in NDVI exhibited the unsustainability of development of the CLP, with 63.20% of regions at risk of vegetation degradation. Finally, the present study suggests that forests in arid and semi-arid areas following a row-belt distribution with low coverage composed of narrow forest belts (covering an area of 15–25%) and wide natural vegetation restoration (covering an area of 75–85%) may be more appropriate for restoration of the regional ecology. [ABSTRACT FROM AUTHOR]

[Display omitted] • Vegetation changes in Gannan Prefecture were in a stable increasing trend during 2000–2018. • The LISA cluster maps of NDVI were mainly characterized by "High-High" and "Low-Low" types. • Annual mean temperature, soil type and elevation were the dominant factors affecting NDVI changes. • The interaction between any two factors was higher than that of any single factor. Vegetation is essential in maintaining terrestrial ecosystem functions, and understanding why and how vegetation evolves is necessary for regional ecological management. On the Tibetan Plateau, the effects of various factors and their interactions in directing vegetation change remain unclear. Supported by the Google Earth Engine (GEE) platform, we quantified spatiotemporal vegetation variation in Gannan Prefecture on the northeastern Tibetan Plateau for 2000–2018 using trend analysis and spatial autocorrelation and explored its driving factors with a geographic detector method. The normalized difference vegetation index (NDVI) illustrated fluctuating growth from 2000 to 2018, and the overall growth rate was 0.002/year. The spatial distributions of NDVI were variable, and areas with NDVI greater than 0.8 accounted for more than 69% of Gannan Prefecture area. From 2000 to 2018, the global Moran's index of vegetation NDVI was greater than 0.52 and showed a fluctuating upward trend, indicating that the vegetation NDVI tended to be distributed with a high spatial concentration. The local Moran's index of NDVI was mainly characterized by "High-High" and "Low-Low" clustering types, and the former increased significantly, but the latter decreased. The annual mean temperature, soil type, and elevation were the dominant factors driving vegetation NDVI change in Gannan Prefecture, explaining more than 15% of the variability. Furthermore, the influence of the interaction between any two factors on NDVI was an almost nonlinear enhancement. These results could help us improve our understanding of the underlying mechanism of NDVI changes and provide a scientific basis for ecological protection in alpine regions. [ABSTRACT FROM AUTHOR]

• Precipitation drove f PV variation, temperature dominated f NPV variation. • The time-lag of temperature and precipitation effect on f NPV were 1.28 ± 1.2 and 1.19 ± 1.24 months. • Accumulated precipitation had positive effect on f PV and negative effect on f NPV. • Accumulated temperature and accumulated precipitation have opposite effects on f PV , f NPV. Efficient prediction of the response of vegetation to future climate change requires an in depth understanding of the effect of climate change on the land cover by photosynthetic (f PV) and non-photosynthetic vegetation (f NPV) and its driving mechanism. Here, we aimed to estimate f PV and f NPV , and analyze the impacts of climate factors on their seasonal variability in the arid desert of northwestern China. First, optimal vegetation indices (VIs) were selected to estimate f PV and f NPV based on measured spectral reflectance, and the accuracy of estimated f PV and f NPV using Sentinel-2 images was evaluated with data from an unmanned aerial vehicle (UAV). Then, partial correlation, multi-correlation and time‐lag effect analyses were used to obtain the correlation coefficients and lag times between f PV , f NPV and climate change. The results showed that Normalized Difference Vegetation Index (NDVI) and Dead Fuel Index (DFI) were the optimal VIs to estimate f PV and f NPV in the desert, with the accuracy of 67 and 52 % for f PV and f NPV , respectively. The average f PV and f NPV were 8.15 and 9.26 % from March to November, their seasonal variability was consistent with plant phenology, and there was a decrease in f PV and f NPV from east to west with a decrease in precipitation. The variability in f PV was driven by precipitation while that of f NPV was dominated by temperature. The time-lag of temperature and precipitation effect on f PV was 1.28 ± 1.28 and 1.61 ± 1.16 months, respectively, and on f NPV was 1.28 ± 1.2 and 1.19 ± 1.24 months, respectively. The time-lag of temperature-precipitation interaction on f PV and f NPV were 1.4 ± 1.22 and 1.2 ± 1.28 months, respectively. Accumulated precipitation had a positive effect on f PV and a negative effect on f NPV , while the impact of accumulated temperature was the opposite. We conclude that there were time-lag and cumulative effects of climate change on f PV and f NPV variability in desert areas. This study helps us better understand the climate-vegetation interactions and provides a scientific basis for desert vegetation management under climate change. [ABSTRACT FROM AUTHOR]

• A system for vegetation change mechanisms in cold-arid mountainous was developed. • Climate factors dominate vegetation greening and exist nonlinear thresholds. • Topographic factors coupled with multi-factors influence vegetation degradation. • Reasonable sustainable planning can avoid further degradation of vegetation. Understanding vegetation evolution trends and their driving mechanisms are essential to uncover changes in ecosystem structure and function. Under the interaction of natural and human factors in cold-arid mountainous region, the main controlling factors and degree of nonlinear influence are not yet clear. This study utilized trend analysis, along with modeling techniques such as boosted regression trees (BRT) and structural equation model (SEM), to identify and quantify the contributions and nonlinear response thresholds of natural and human factors to normalized difference vegetation index (NDVI) in the Qilian Mountains from 2000 to 2020. The results showed that the implementation of ecological projects made the overall vegetation into greening trend (91.06%), while irrational human activities led to degradation in the central-eastern regions. The BRT showed that climatic factors dominated for overall vegetation changes, greening and significant greening. Thresholds exist for different drivers to influence both greening and degradation of vegetation. Regarding degradation/significant vegetation degradation, the importance of climate factors decreased, and topography ranked first, followed by population density and LUCC. The expansion of building and unused land will inhibit vegetation greening. SEM revealed that climate factors continued to maintain positive effect with the warm-humid change. The destruction of forestlands and grasslands led to significant degradation of vegetation in the central-eastern regions with the largest total negative effect. Topographic factors coupled with multi-factors inhibited vegetation growth in the central-eastern regions. This study provided a new framework for accurately assessing the dynamic evolution of vegetation, and better reveal the multi-factor driving mechanism vegetation changes in cold-arid mountainous regions. Future research should consider combining more accurate AI algorithms with field surveys to quantify the coupling mechanisms between factors and vegetation dynamic. It is expected to provide scientific guidance for vegetation ecosystem planning in other mountainous regions globally and support the achievement of sustainable development. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new normalized differential soil organic carbon spectral index was proposed. • Some measured environmental auxiliary data can be replaced by spectral indices. • NDVI, LST, SI, and CI contribute the most to the prediction of SOC. • The PSO-RFR model can better predict the SOC of coastal wetlands. To study the dynamic changes of soil organic carbon (SOC) content in coastal wetlands over the past 23 years, we use the third core area of the Dafeng Elk Nature Reserve in Jiangsu Province, China as the study area. A normalized difference soil spectral index (NDSOC), which is directly related to SOC, was constructed by using the spectral reflectance of Landsat images, and six auxiliary environmental variables related to SOC were extracted: surface temperature, surface moisture, soil salinity, the normalized difference vegetation index (NDVI), surface brightness, and soil texture information. The coastal wetland soil surface SOC inversion model was constructed by multiple linear regression, support vector machine, and particle swarm optimization-based random forest regression (PSO-RFR). By using the best inversion model, we obtain the dynamic distribution of soil surface SOC in the third core area of the Dafeng Elk Reserve for the past 23 years. The results indicate (1) that the NDSOC index constructed based on the short-wave infrared (SWIR) bands 1 and 2 of the Landsat images has a strong indication of SOC. Among the extracted auxiliary environmental variables, the NDVI, soil clay content, surface temperature, and soil salinity contribute more strongly to the SOC content of coastal wetlands, which averaged 27.9 %, 24.4 %, 22.1 %, and 18 %, respectively. (2) The PSO-RFR algorithm provided the best simulation of the surface SOC content of coastal wetlands: the model determination coefficient R 2 is 0.731, the average fitting root mean square error RMSE is 0.917 g/kg, and the fitting uncertainty standard deviation was 0.362 g/kg. (3) From 2000 to 2022, the organic carbon storage and organic carbon density in the third core area of the Dafeng Elk Reserve gradually increased, the highest value was reached in 2016, they were 4.017 (104 t) and 1.718 kg/cm2, respectively. [ABSTRACT FROM AUTHOR]

• Holocene vegetation and climate were reconstructed in the Mu Us Sandy Land. • Steppe dominated vegetation during a climate optimum at 8.3–2.6 ka. • Ice cover and solar radiation drove Holocene millennial-scale climate change. Knowledge of changes in vegetation and climate during the Holocene is important for improved assessments of future climate change and for mitigating climate warming. Owing to the relative difficulty in obtaining high-resolution sedimentary records and identifying pollen, few paleovegetation reconstructions are available from the Mu Us Sandy Land, China. This study analyzes a naturally-exposed fluvio-lacustrine section in the hinterland of the Mu Us Sandy Land. Based on a chronological framework constructed by AMS 14C, a pollen-based random forest model was used to quantitatively reconstruct changes in Holocene climate and normalized difference vegetation index (NDVI) changes. The relationships between NDVI, pollen and precipitation, as well as pollen assemblage characteristics were combined to reconstruct the evolution of the Holocene vegetation in the Mu Us Sandy Land. Our results show that the climate was cold and humid, and the dominant vegetation type was Ephedra shrub-encroached steppe between 11.2 and 8.3 ka BP; the climate reached an optimum, and vegetation evolved into Artemisia – Taraxacum -type– Thalictrum steppe from 8.3 to 2.6 ka BP; and finally, the climate gradually deteriorated and vegetation became an Artemisia –Chenopodiaceae steppe after 2.6 ka BP. In comparisons with other climate records, we found that the Mu Us Sandy Land climate optimum occurred synchronously at 8.3–2.6 ka BP, and the pattern of climate change is closely consistent with the East Asian summer monsoon. We suggest that millennial-scale Holocene climatic change in this region was mainly driven by the influences of high-latitude ice volume and summer solar radiation on the East Asian summer monsoon. [ABSTRACT FROM AUTHOR]

• Desertification vulnerability was assessed from exposure, sensitivity, adaptivity. • Natural and human influences upon desertification vulnerability were investigated. • Vegetation dominated desertification vulnerability changes of northern China. • Factors interaction enhance their contribution to vulnerability changes. As desertification is a major ecological and environmental problem affecting global sustainable development, it is imperative to assess the vulnerability of desertified land systems in order to formulate desertification control policies and improve livelihoods in arid lands. Using the vulnerability scoping diagram (VSD) and Geodetector method, we explored trends in desertification vulnerability by considering the exposure, sensitivity, and adaptability of the sandy region of northern China from 1985 to 2010, and identified the influencing mechanisms in terms of natural and human factors. According to the results, desertification vulnerability was higher in the central and western regions and lower in the eastern regions, and decreased significantly in most areas from 1985 to 2010. This could mainly be attributed to their reduced sensitivity, while the contributions of exposure and adaptability were relatively small in comparison to sensitivity. The spatial and temporal variations in the Normalized Difference Vegetation Index (NDVI) were the primary drivers of significant changes in desertification vulnerability in most areas, while the Net Primary Productivity (NPP), percentage of unused land, and grazing density were deemed secondary principal factors. Interactions between factors can reinforce the explanatory power of individual factors for changes in vulnerability, especially for wind erosion and GDP. The findings of this study could inspire governors to implement integrated measures to mitigate vulnerability and control desertification, including the restoration of vegetation on degraded land and enhancing the adaptability of the sandy region from both economical and ecological perspectives. [ABSTRACT FROM AUTHOR]

• Unmanned aerial vehicle data can replace field measurements to optimize the line-point intercept method. • Key factors of sample plot design affect shrub coverage's measurement accuracy. • An efficient measurement scheme based on coverage stratification and the minimum number of sample lines. Shrub encroachment has become a global concern. The fraction of shrub coverage (FSC) is an important indicator that reflects the distribution of shrubs and the degree of shrub encroachment in grasslands. The line-point intercept (LPI) method is commonly used for FSC measurement in field surveys, but it's often associated with issues of poor accuracy and low measurement efficiency. Here, we focus on Caragana microphylla shrub-encroached grassland as a case study. We used normalized difference vegetation index (NDVI) data obtained from unmanned aerial vehicle (UAV) to derive the actual values of FSC (FSC T) and to simulate measurements using the LPI method. We compared the results for measurements based on different sample plot designs, including variation in plot shape and line distribution, the number of sample lines, and the sample point spacings. The aim was to investigate the influence of these main parameters of sample plot design upon measurement results, in order to provide a reference for FSC measurements in field experiments. We first calculated FSC T and stratified the plots according to coverage levels. Based on this, we further evaluated the measurement accuracy of the LPI method under different parameter settings. The results revealed significant systematic errors in the measurement from circular plots. For square plots, the minimum number of lines required for the "high," "medium," and "low" coverage groups are 48, 16, and 8 (for 80% accuracy), and likewise, 108, 24, and 16 (for 90% accuracy), respectively. Futuremore, point spacings of 0.1 m and 0.5 m can achieve the same accuracy as the original spacing (0.02 m). We conclude that the systematic errors in circular plots are caused by the radial distribution of lines, whereas in square plots, achieving an efficient measurement requires considering different coverage levels, ensuring an adequate number of lines, and setting a relatively smaller point spacing. [ABSTRACT FROM AUTHOR]

• Vegetation coverage based on NDVI seriously overestimates soil wind erosion. • The area proportion decreases with vegetation coverage as an exponential function. • Different vegetation coverages in the Tarim River Basin have obvious region. • Vegetation coverage changed as a linear function of climate factors. Vegetation plays an important role in soil erosion, and its coverage as an important parameter appears in almost all soil erosion models, such as USLE (Universal Soil Loss Equation), RWEQ (Revised Wind Erosion Equation) and WEPS (Wind Erosion Prediction System) et al. In the past several decades, scientist have paid attention to photosynthetic vegetation coverage (PVC) based on Normalized Difference Vegetation Index (NDVI). However, in soil wind-eroded region, there is almost no PVC in the nongrowing season and is a large amount of nonphotosynthetic vegetation (NPVC) in the growing season. Because the effect of NPVC on soil erosion is the same as that of PVC, vegetation coverage (FVC) in soil erosion field would include PVC and NPVC. Based on field observation, NDVI and meteorological data et al., this paper studied temporal and spatial distribution of vegetation coverage in the Tarim River Basin, and its response to surface temperature (T) and rain (R). The main conclusions are as follows. (1) PVC based on NDVI, would result in an underestimation of FVC in the growing season with about 30%, which led to an overestimation of soil wind erosion with 12%, indicating great errors in the nongrowing season. (2) FVC had obvious region distributions. FVC with 10%∼40%, 40%∼70%, and>70% were distributed in the mountain of middle elevation and the edge of oasis, the cultivated of the oasis, and the alpine grass of the Kongque River Subbasin, respectively; FVC<10% was widely distributed. With increasing of FVC, its area proportion (y) decreased as an exponential function (y = 73.19e-0.038FVC). FVC in the Kongque River, Aksu River, and Weigan-Kuqa River subbasins was better than that in the Hotan River, Keriya River, Cheerchen River, and mainstream Tarim River subbasins. Since the restoration of the ecological environment in the Tarim River Basin in 2000, FVC of 0 ∼ 10% decreased, while that of 40%∼70% increased, and the other fluctuated. (3) There was a linear relationship (FVC = a 1 T + a 2 R + b, where a 1 , a 2 , and b were the fitting coefficients) among FVC , T , and R , although the coefficients would change in different subbasins or half-months. [ABSTRACT FROM AUTHOR]

[Display omitted] • Runoff changes in the Yarlung Zangbo River Basin were attributed based on the Budyko framework. • 78.81% of runoff changes is attributed to climate change and 21.19% to human activities. • Increasing precipitation and accelerating snowmelt may not offset water dissipation induced by rising temperature. • Combined effects of multiple factors on runoff were explored. • Precipitation is the best predictor of runoff variations in the region. The runoff in Tibetan Plateau (TP) is sensitive to environmental changes, and it is thus challenging to assess the variations and influencing factors of runoff in the context of global warming and increased human activities. We addressed this challenge by using the most comprehensive dataset, Budyko hypothesis, and wavelet theory to attribute runoff variations in the Yarlung Zangbo River Basin (YZRB), and its interaction with regional environmental variables (e.g., precipitation PCP, relative humidity RH, average temperature TMPave, maximum temperature TMPmax, minimum temperature TMPmin, wind speed WS, snow depth SD, potential evapotranspiration ET0, and normalized difference vegetation index NDVI). The results indicated that, 1) the warming of the YZRB was 0.47, 0.36, and 0.65 °C/decade in TMPave, TMPmax, and TMPmin, two to three times the global mean rate, and the increasing trend of precipitation was nonsignificant; 2) ET0 increased significantly by 2.25 mm/decade, and SD decreased rapidly at a rate of 2.54 mm/decade. 3) The streamflow (lower Nuxia) showed a decreasing trend from 1960 to 2015, primarily caused by climate change, which accounted for 78.81%. Human activities contributed 21.19%; 4) Seasonal streamflow increased in Spring and Winter and declined in Summer and Autumn. 5) PCP was the single variable mostly associated with runoff. When PCP-SD and PCP-RH-ET0 were combined, they demonstrated the most robust relationships with runoff, respectively; 6) A modest increase in PCP and accelerating snowmelt may not offset water dissipation due to severe warming. Further research is necessary to improve understanding of the driving mechanisms behind seasonal and inter-annual variability in the runoff and to provide strategies for mitigation and adaptation to environmental change. [ABSTRACT FROM AUTHOR]

• A new index was proposed to monitor surface mining disturbance. • The new index enhance the sensitivity to improve the detection accuracy. • Compared with five products, the detection accuracy is superior. • Mine Landscape Damage/Reclamation Index can be widely used in the detection of open-pit mining. The exploitation and utilization of mineral resources have significantly contributed to economic and social development; however, the procession has also negatively impacted the ecological environment. Thus, it is necessary to effectively monitor coalfields, an important basis for reasonable development and utilization of coalfields and following ecological reclamation. Based on MODIS Global Damage Index, We couple the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), and proposal the Mine Landscape Damage Index (MLDI) and Mine Landscape Reclamation Index (MLRI) to supervise the disturbed vegetation of mines. Huolinhe coalfield, one of the largest mines in China, is monitored in this research. 3675 Landsat imageries from 1990 to 2020 are analyzed using the LandTrendr algorithm with MLDI and MLRI time series data on Google Earth Engine. The results demonstrate that this method could distinguish the region and disturbing process of damage and reclamation due to its effectiveness proved by visual interpretation of high-resolution imageries and verification of field investigation. The total detected area of vegetation damage was 16505.10 ha, and the vegetation damage in the mining was active from 2006 to 2011, with average annual damage of more than 1000 ha. The total area of vegetation restoration in the mine is 8640.81 ha, accounting for 52.35% of the vegetation damage. Vegetation restoration has increased gradually since 2010. In the past three years, vegetation restoration accounted for 47.61% of the total reclamation area. A novel index for monitoring the effects of surface damage in multi-open-pit mining areas is provided to us, and the detection is reliable. It can give the government an effective practical data reference in the sustainable development of utilization management and ecological reclamation in coalfield. [ABSTRACT FROM AUTHOR]

[Display omitted] • The soil quality changed as a result of the landscape pattern alteration. • The land use patch density was among the most important landscape properties affected the soil quality. • The soil quality had significant spatial relationships to elevation, slope, and NDVI. • The soil quality level was higher in the forest land use compared to farmland and rangeland. The soil quality is the inherent capacity of soils to function in an ecosystem, which depend on soil properties and change under the influence of landscape patterns. This study aimed to evaluate the soil quality in the Shoor River basin, southwestern Iran, by using the soil quality indicators based on two approaches, i.e. , minimum data set and total data set. The spatial relationships of the soil quality to landscape pattern properties (e.g. , density, dominance, shape, aggregation, and diversity of patches) were also analyzed through the exploratory regression in the Arc GIS 10.5 software in addition to the effects of land use, elevation, slope, and normalized difference vegetation index (NDVI) on the soil quality. The results indicated that the soils of northern and eastern regions of the study area had higher levels of quality than those of southern and western regions. Very low and very high of soil quality covered<15% (43413 ha) and 20% (56759 ha) of the basin, respectively. The hot spots of soil quality were observed in the eastern and northern regions of the study area whereas the cold spots of soil quality were seen in the southern and western regions. According to the findings, the soil quality level was higher in the forest land use compared to farmland and rangeland. The results confirmed that the soil quality had significant spatial relationships with elevation, slope, and NDVI (0.12 < r < 0.35, p-value < 0.1). Moreover, the soil quality had significant positive spatial relationships with aggregation and dominance of patches (0.40 < r < 0.62, p-value < 0.05), whereas it had significant negative spatial relationships with the density and diversity of patches (0.35 < r < 0.63, p-value < 0.1). However, there were no significant relationships between the soil quality and the shapes of patches. This study confirmed that the soil quality would vary in different landscape patterns and that it might change as a result of development and anthropogenic activities. [ABSTRACT FROM AUTHOR]

Rock fragments (RFs, material particles with diameter > 2 mm) are widely distributed in soils over globe. However, spatial pattern of RF content (RFC) and its roles on hydrological and ecological patterns have remained unclear at the global scale. In this study, based on the collected global datasets, we investigated the relationships between mean annual temperature (MAT), mean annual precipitation (MAP), elevation and RFC at 0–5 cm soil depth. In addition, we also examined the links of RFC to representative hydrological and ecological indicators (soil water content or SWC, and normalized difference vegetation index or NDVI, respectively). Results showed that through affecting the pedogenic and morphogenic processes, temperature, precipitation and elevation presented significant relationships with RFC over the globe. When MAT < 20 ℃, RFC decreased with MAT increasing, due to that high temperature could promote RF weathering and thus soil production; while when MAT > 20 ℃, RFC slightly increased with MAT increasing, due to that extreme high temperature induced arid condition would surpass soil production. RFC decreased with MAP increasing, because precipitation increased SWC and hydrologic flow and thus enhanced soil production. In addition, higher elevation zones usually had colder condition and greater loss of fine-textured soils due to erosion, and thus RFC increased with elevation increasing. Furthermore, a RFC threshold of about 0.2 m3 m−3 was identified. When RFC < 0.2 m3 m−3, existence of RFs may improve the soil pore structure and thus water and nutrient retention capacity. Therefore, higher SWC and NDVI were observed. However, when RFC > 0.2 m3 m−3, due to poor soil water and nutrient retention capacity, as well as lower MAT with higher elevation, or lower MAP, lower SWC and NDVI were observed. These findings can supplement the lack of knowledge on RFs and its effects from the global perspective. [ABSTRACT FROM AUTHOR]

• Annual mean R s and Q 10 values in HD were significantly higher than those in LD. • R s was mainly controlled by θ in the summer and T s in the other seasons. • Double variable equation was better than single to predict R s at the annual scale. • Annual R s in HD was positively related with θ , NDVI and SOM, negatively with T s. • Annual θ was the main factor controlling annual Q 10 at both sites. Although correlations between soil respiration (R s) and environmental factors in different ecosystems have been extensively investigated, results based on long-term experiments are limited. In this study, we analyzed the correlations between R s and soil temperature (T s), moisture (θ), the normalized difference vegetation index (NDVI), and soil organic matter (SOM) based on an 11-year field campaign in abandoned farmland (HD) and bare land (LD) areas with a Cambisol soil type in the eastern Loess Plateau of China. The results showed that the mean annual R s and annual temperature sensitivity (Q 10) values in HD were significantly larger than those in LD during the measurement period. R s and the annual contribution ratio (R ratio) of root respiration (R r) to R s in HD increased significantly with vegetation recovery years. On the annual scale, R s was controlled primarily by T s at both sites. However, in the summer months, R s was controlled by θ rather than T s. A two-variable model, including T s and θ , could more accurately predict the temporal variations in R s at the annual and seasonal time scales than a single-variable model, and the explanatory power of the equations in HD was significantly larger than that in LD. On the interannual scale, variations in the annual R s and R r showed significant positive correlations with the annual θ and NDVI and SOM, and negative correlations with T s in HD. Moreover, the annual Q 10 was significantly positively related to θ in HD. In LD, the only significant relationship was between the annual Q 10 and the annual θ. All differences between HD and LD in R s , T s , and θ and their correlations were attributed to differences in vegetation. Our findings highlight the importance of long-term observations for the accurate estimation of CO 2 efflux in semiarid regions and the impact of vegetation changes on soil respiration. [ABSTRACT FROM AUTHOR]

• Land use and land cover classes homogenize within-class differences in vegetation density, type and other characteristics that influence surface erosion. • When these classes are used to estimate current and projected future RUSLE C-factor for native vegetation, this introduces significant error. • Remote sensing methods can be used to assess intra-class variation in C-factor, improving the soil loss estimate. • A strong, predictive relationship exists between remotely-sensed C-factor values and climate. • Current and projected changes in climate can be used to directly estimate C-factor, improving the estimate of current and future soil loss rates. This new method provides a low-cost, efficient and reliable estimate of future C. Understanding how reservoir sedimentation rates may evolve due to climate change is essential for projecting future changes in reservoir water storage capacity. The Revised Universal Soil Loss Equation (RUSLE) is commonly used to assess regional soil loss rates because of its suitability for working with the coarse temporal and spatial scale of climate model outputs. Application of the RUSLE for projecting future erosion rates is constrained by the relatively limited number of classes used in projected changes of native vegetation: this typically reduces a wide range of variation in RUSLE cover (C) factors to a single value per class, and these classes may be insensitive to changes in species composition and canopy density with time and space. This paper develops a low-cost, efficient, and objective approach to projecting future C values directly based on widely available Landsat data, downscaled climate model data, and a limited number of terrain variables. Observed C is estimated from Landsat-derived Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) values using standard methods. A linear relationship is established between the observed C values and average annual antecedent temperature, average annual antecedent precipitation, latitude, and percent sand (adjusted R2 = 0.75604, 0.7543, and RMSE = 0.09448, 0.07043, respectively). A proof of principal study demonstrates that, unlike when land cover classes are used to estimates C, a RUSLE model driven by the regression-based C provides a correct order-of-magnitude estimate of observed long-term erosion and sediment yield rates (e.g., an estimate of 2.9–3.2 t ha−1 y-1 is obtained where observed rates range from 1.2 to 3.9 t ha−1 y-1 for the same time period, and up to 8 t ha−1 y-1 historically, while standard formulations of RUSLE yield 16.1 and 0.3 t ha−1 y-1). Using climate model data to estimate both precipitation intensity (R factor) and C in the RUSLE model results in basin wide projected end 21st century soil erosion rates for relative concentration pathway 8.5 of 5.5–7.5 t ha−1 y-1, consistent with expectations for accelerated soil loss in the study area as climate change reduces soil moisture while increasing precipitation intensity. The same model driven by land cover class estimates of C produces soil loss rates an order of magnitude smaller (0.3–0.4 t ha−1 y-1), consistent with a much more heavily vegetated landscape than could be supported under the increasingly arid projected climate. [ABSTRACT FROM AUTHOR]

• A change detection method was developed to identify abandoned cropland within-year. • The abandoned, fallow, and lost cropland were distinguished. • Spontaneously and induced abandoned cropland were further distinguished. • The average annual spontaneous abandonment is as high as 11.98% in Linxia, China. Against the background of the COVID-19 pandemic and various armed conflicts, the world is experiencing an unprecedented food crisis. The reclamation of abandoned cropland with food production potential may increase the global food supply in a short period of time, ensuring food security. At present, the extraction of abandoned cropland is mainly based on low- and medium-resolution remote sensing image data, making it difficult to extract fragmented areas in mountainous regions and to distinguish between abandoned cropland and transitional classes (such as fallow cropland). We developed a change-detection method based on within-year Sentinel-2 time series to extract cropland abandoned from 2018 to 2021 and defined four types of croplands, namely spontaneously abandoned, induced abandoned, fallow, and lost cropland, using Linxia County in mountainous China as the study region. First, cropland objects were generated from multi-temporal Sentinel-2 images using the multi-resolution segmentation method, and the land use map of Linxia County from 2017 to 2021 was drawn using random forest classifier. Second, through defining and identifying different cropland types, the interannual dynamic changes in cropland from 2018 to 2021 were extracted by analyzing the annual land use change trajectory. Third, by analyzing the normalized difference vegetation index (NDVI) time series of cropland within-year, the active and cultivated cropland sites within-year were extracted by threshold segmentation. Finally, the changes in the four cropland types were extracted by intersecting the two result types. Our method captured the object level changes well (overall mapping accuracy = 93 ± 5 %), and the extraction accuracy of abandoned cropland reached 81 ± 2 %. Abandoned cropland was mostly located in areas of medium quality and with a moderate distance from rural settlements. Reclamation can potentially increase the grain production in Linxia County by at least 3.6 % and needs to be combined with the local natural geography and human activities. Our method is a robust method for extracting abandoned cropland and may be applied to other research related to land use change. [ABSTRACT FROM AUTHOR]

[Display omitted] • The spectral indices of salinized wasteland (SW) are different from other lands. • The thresholds were determined based on multi-period and multi-year data. • Multiple validation approaches showed SW model was highly accurate and reliable. • The area of large-sized SWs increased with years, while others was the opposite. The salinized wasteland (SW) is a buffer zone to accumulate salt introduced from irrigation water in salinized irrigation districts. It is crucial to identify the locations and sizes of SW to achieve effective land management and reclamation, as well as agricultural sustainable development. However, the SW is less explored due to the lack of low-cost and effective techniques. In this study, a decision tree-based remote sensing model was proposed to distinguished SW from other land types using the high-resolution Gaofen-1 (GF-1) images in the Hetao Irrigation District of China during 2017–2020. Meanwhile, multiple validation approaches including points (field and random points) and area (small and medium-sized SWs, i.e., SW A , SW B) matching validations were adopted. To establish the model of SW, four spectral indices including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Salinity Index (SI), as well as a series of multi-period and multi-year remote sensing images were used. The threshold values of the four spectral indices were determined based on the probability density curve. The SW remote sensing model was highly accurate and reliable. The average producer accuracy (PA) and average user accuracy (UA) were respectively 88.0% and 81.6% for field investigation (field points matching validation), and 85.9% and 80.1% for Google Earth image investigation (random points matching validation). Besides, the average overlapping rate and misclassification rate were respectively 95.6% and 2.3% for small-sized typical SWs (area validation), and 92.9% and 4.7% for medium-sized typical SWs during 2017–2020. The model indicated that the SW area in the region was respectively 9969, 9694, 9660, and 9608 ha from 2017 to 2020. The area of small and medium-sized SWs decreased with time, while that of large-sized SWs was the opposite. [ABSTRACT FROM AUTHOR]

• Successful application of the ESTARFM to study the dynamics of playas surface. • NDVI and SI are sensitive to precipitation events. • Precipitation events affect the spatiotemporal distribution of surface salinity. • Salinity spectral indices decrease after precipitation events. Playas are desert landscapes unique to arid regions that respond quickly to climate change. Changes in surface salinity in Praia are directly linked to water security and regional ecological and economic health. This study aimed to explore the sensitivity of remotely sensed spectral indices to precipitation events. An ESTARFM was utilized to fuse daily (less than 10% cloudiness) MODIS and Landsat 8 imagery from April to September 2019 in the Ebinur Lake Wetland Reserve. Salinity was tested in the laboratory after soil collections in May (spring) and August (summer). The significantly correlated normalized difference vegetation and salinity indices were compared to the precipitation data and found to fluctuate in response to precipitation. The normalized difference vegetation and salinity indices decreased following precipitation events and increased during precipitation intervals, and the precipitation amount and evaporation intensities may have influenced the magnitudes of the decrease and increase. Following precipitation, the playa surface produces new puddles and a new spatial distribution pattern of soil salinity. This study determined that the spatiotemporal fusion technique is an effective method for observing the dynamics of playa surfaces, and precipitation and evaporation affect the spatial distribution of salt on the playa surface; however, the monitoring period should be short when utilizing remote sensing to monitor playa salinity. [ABSTRACT FROM AUTHOR]