Search

Both human activities and climate change have posed great impacts on the vegetation changes in the context of global climate change. It is necessary to identify the areas of man-made vegetation degradation for better environmental protection. However, only limited effort has been focused on distinguishing between the influences of yearly climatic factors and human activities on vegetation growth. Particularly, it is still lacking in the vegetation dynamics under the seasonal climate changes and anthropogenic activities in China. In this study, the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) and climate change were investigated using the AVHRR NDVI3g data, land use data and gridded climate data, A multiple linear regression model was then used to determine the driving factors of vegetation change using residual analysis at the regional and national scales from 1982 to 2015. The results show that there was outstanding regional spatial characteristic of vegetation changes with a decreasing trend from the southeast to northwest China, where the annual rate of change ranged from -0.016/a to 0.029/a. An increasing trend was found in the temperature and solar radiation, whereas, the precipitation showed a downward trend in most regions. According to the Hurst index, 80.62% of the vegetation presented a continuous and stable change, while the grasslands and woodland tended to be unstable and vulnerable to climate change and human activities. The NDVIs of Junggar Basin, Qinghai-Tibet Plateau, and Xilin Gol League of Inner Mongolia were negatively correlated with the air temperature and solar radiation, whereas, the NDVIs of Yunnan-Guizhou Plateau, southern Loess Plateau, and Sichuan Basin were positively correlated. The residual analysis showed that the drought resulted in the vegetation degradation in the arid areas, such as Xinjiang Junggar Basin and central Inner Mongolia, while the increase of temperature and radiation was the main driver for the vegetation improvement in the eastern coastal plain, the southern Loess Plateau, Sichuan Basin, and the southwestern Yunnan-Guizhou Plateau. Since the beginning of the 21st century, human activities were gradually strengthened, including soil and water conservation, the conversion of farmland to forest on the Loess Plateau, the desert control in Alxa, as well as the construction of coastal city clusters and northeast industrial bases. Thus, the vegetation improvement was identified in the Alxa Plateau of Inner Mongolia, the central and northern Loess Plateau, and most parts of South China, whereas, the vegetation degradation was detected in the Greater and Lesser Khingan Mountains, the eastern regions of the Changbai Mountains and the Yangtze River Delta. Among them, the national projects of ecological protection were attributed to the significant improvement of vegetation conditions. Consequently, the monitoring of vegetation degradation can provide a theoretical basis for the implementation of ecological restoration and the construction of ecological civilization in the future. The finding can greatly contribute to regional green and sustainable development. [ABSTRACT FROM AUTHOR]

The detection and attribution of vegetation dynamics in drylands is an important step for the development of effective adaptation and mitigation strategies to combat the challenges posed by human activities and climate change. However, due to the spatial heterogeneity and interactive influences of various factors, quantifying the contributions of driving forces on vegetation change remains challenging. In this study, using the normalized difference vegetation index (NDVI) as a proxy of vegetation growth status and coverage, we analyzed the temporal and spatial characteristics of the NDVI in China's Inner Mongolian grasslands using Theil–Sen slope statistics and Mann–Kendall trend test methods. In addition, using the GeoDetector method, a spatially-based statistical technique, we assessed the individual and interactive influences of natural factors and human activities on vegetation-NDVI change. The results show that the growing season average NDVI exhibited a fluctuating upward trend of 0.003 per year from 2000 to 2018. The areas with significant increases in NDVI (p < 0.05) accounted for 45.63% of the entire region, and they were mainly distributed in the eastern part of the Mu Us sandy land and the eastern areas of the Greater Khingan Range. The regions with a decline in the NDVI were mainly distributed in the central and western regions of the study area. The GeoDetector results revealed that both natural and human factors had significant impacts on changes in the NDVI (p < 0.001). Precipitation, livestock density, wind speed, and population density were the dominant factors affecting NDVI changes in the Inner Mongolian grasslands, explaining more than 15% of the variability, while the contributions of the two topography factors (terrain slope and slope aspect) were relatively low (less than 2%). Furthermore, NDVI changes responded to the changes in the level of specific influencing factors in a nonlinear way, and the interaction of two factors enhanced the effect of each singular factor. The interaction between precipitation and temperature was the highest among all factors, accounting for 39.3% of NDVI variations. Findings from our study may aid policymakers in better understanding the relative importance of various factors and the impacts of the interactions between factors on vegetation change, which has important implications for preventing and mitigating land degradation and achieving sustainable pasture use in dryland ecosystems. [ABSTRACT FROM AUTHOR]

In recent decades, frequent compound dry and hot events have posed a great threat to humans and the ecological environment, especially in Inner Mongolia, which has arid and semi-arid characteristics. In this study, monthly temperature and precipitation data from 115 meteorological stations in Inner Mongolia from 1982 to 2020 were used to establish a standardized dry and hot index (SDHI). Theil–Sen median trend analysis, Mann–Kendall test, partial correlation analysis, and stepwise multiple regression models were used to characterize the changes in compound dry and hot events and the normalized difference vegetation index (NDVI) from 1982 to 2020, and the relationship between the SDHI and NDVI was quantitatively evaluated. The results showed that the overall SDHI values in Inner Mongolia showed a significant decrease at a rate of 0.03/year from 1982 to 2020, indicating an increase in the severity of compound dry and hot events. NDVI values showed a significant increasing trend and NDVI showed mutated 2001. Among the grassland vegetation types, SDHI and NDVI trends were more significant in forests, and meadow steppe, desert steppe, and desert were more susceptible to compound dry and hot events, and forests had the greatest severity of compound dry and hot events. The results of the partial correlation analysis showed that the average value of the partial correlation coefficient between the SDHI and NDVI was 0.68, and the area of positive correlation was 84.13%. Spatially, it showed strong response characteristics in the middle and gradual weakening towards the east and west sides. The correlation between NDVI and climatic conditions varied greatly in different vegetation areas. The forest area is most sensitive to the influence of temperature, and the desert steppe area is most affected by compound dry and hot events. The overall vegetation growth in Inner Mongolia was most affected by temperature conditions, followed by compound dry and hot conditions, and the influence of drought conditions was the least significant. The results of the relative importance analysis confirmed this. The research results provide a more detailed understanding of compound dry and hot events in arid and semi-arid regions and useful insights and support for ecological protection. [ABSTRACT FROM AUTHOR]

The frequency of extreme climate events has increased resulting in major changes to vegetation in arid and semi-arid areas. We selected 12 extreme climate indices and used trend analysis and multiple linear regression models to analyze extreme climate trends in steppe areas of Inner Mongolia and their impact on the normalized difference vegetation index (NDVI). From 1998 to 2017, the NDVI of the Inner Mongolia steppe increased overall; however, there was a small area of decrease. Extreme climate indices related to warming exhibited increasing trends, particularly in the desert steppe. Although the extreme precipitation index did not change significantly overall, it increased in the northeastern and southwestern regions of the study area and decreased in the central region. The established model showed that the extreme climate explained the highest NDVI variation in desert steppe (R2 = 0.413), followed by typical steppe (R2 = 0.229), and meadow steppe (R2 = 0.109). In desert steppe, TX90P (warm days index) had the greatest impact; in typical steppe, R10 (number of heavy precipitation days index) had the greatest impact; in meadow steppe, R95P (very wet days index) had the greatest impact. This study offered new insights into dynamic vegetation changes in steppe areas of Inner Mongolia and provided a scientific basis for implementing environmental protection strategies. [ABSTRACT FROM AUTHOR]

Dust events in Northeast Asia have several adverse effects on human health, agricultural land, infrastructure, and transport. Wind speed is the most important factor in determining the total dust emission at the land surface; however, various land-surface conditions must be considered as well. Recently, the Korea Meteorological Administration updated the dust emission reduction factor (RF) in the Asian Dust Aerosol Model 3 (ADAM3) using data from the normalized difference vegetation index (NDVI) of the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the improvements of ADAM3 according to soil types. We incorporated new RF formulations in the evaluation based on real-time MODIS NDVI data obtained over the Asian dust source regions in northern China during spring 2017. This incorporation improved the simulation performance of ADAM3 for the PM10 mass concentration in Inner Mongolia and Manchuria for all soil types, except Gobi. The ADAM3 skill scores for sand, loess, and mixed types in a 24 h forecast increased by 6.6%, 20.4%, and 13.3%, respectively, compared with those in forecasts employing the monthly RF based on the NDVI data. As surface conditions in the dust source regions continually change, incorporating real-time vegetation data is critical to improving performance of dust forecast models such as ADAM3. [ABSTRACT FROM AUTHOR]

Drought events cause ecological problems, including reduced water resources and degraded vegetation. Quantifying vegetation responses to drought is essential for ecological management. However, in existing research, the response relationships (correlations and lags) were typically determined based on Pearson correlation coefficient and the resultant lag times were constrained by the spatial and temporal resolutions of the analyzed data. Inner Mongolia is an important ecological barrier in northern China. Ecological security is one of the most concerned issues of the region's sustainable development. Herein, we combined Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI3g) with Systeme Probatoire d'Observation de la Terra-vegetation (SPOT-VGT) NDVI data through spatial downscaling. The obtained 1 km-resolution NDVI dataset spanning Inner Mongolia from 1982 to 2019 was used to represent the refined vegetation distribution. The standardized precipitation evapotranspiration index (SPEI) derived from gridded meteorological data was used to measure drought over the same period. We investigated the spatiotemporal characteristics of vegetation and drought in the region in the past 38 years. We then discussed changes in different vegetation responses to drought across eastern Inner Mongolia using cross wavelet transform (XWT) and wavelet coherence (WTC). The results reveal that in 82.4% of the study area, NDVI exhibited rising trends, and the SPEI values exhibited declining trends in 78.5% of the area. In eastern Inner Mongolia, the grassland NDVI was positively correlated with SPEI and significantly affected by drought events, while NDVI in forestlands, including shrubs, broad-leaved forests, and coniferous forests, was negatively correlated with SPEI in the short term and weakly affected by drought. The NDVI lag times behind SPEI in grasslands, coniferous forests, and broad-leaved forests were 1–1.5, 4.5, and 7–7.5 months, respectively. These findings provide a scientific foundation for environmental preservation in the region. [ABSTRACT FROM AUTHOR]

The typical steppe in Inner Mongolia is an important component of the Eurasian steppes. It plays a dominant role in preventing desertification and against sandstorms, but highly sensitive and vulnerable to climate change. Based on long-term observed precipitation data and remotely sensed Normalized Difference Vegetation Index (NDVI) images, the non-stationary behavior of summer precipitation and its linkage with vegetation change were investigated, by means of incorporating time-varying and physical based explanatory covariates in non-stationary modeling. Results indicated that time-dependent models exhibited good performance to reproduce the temporal variations of eco-hydrological variables. The non-stationarity of summer precipitation was prominently visible for the majority of sites during the period from 1957 to 2017, with the mean behavior described as a linear or nonlinear time-varying pattern. In general, the steppe has experienced a decreasing trend in summer precipitation, but whether the decline tends to maintain or weaken or strengthen depends on the spatial location of the site studied. Differences appeared in the changes of vegetation in summer from 1998 to 2017 in different sub-regions. Evidences for the presence of stationary evolution was found in most sub-regions in the middle part, together with a linear increase in the westernmost sub-regions while a non-linear decrease in the easternmost sub-regions. Covariate analyses further highlighted the role of precipitation variabilities in the modeling of the NDVI-related vegetation dynamics over the steppe. The potential relations of summer precipitation to vegetation growth were characterized as both linear and non-linear positive forms. In particular, precipitation extremes could be responsible for the occurrences of exceptional cases in vegetation condition. The fluctuations in summer precipitation have crucial significance for future predictions of vegetation succession. Findings from this study would lead to additional insights to understanding the effect of climate change on grassland ecosystem processes. [ABSTRACT FROM AUTHOR]

• The suitability of VMD method to extract long time-series NDVI periodic characteristics and trend information was discussed. • The specific periodic relationship between NDVI and climate change in the growing season were analyzed. • The interannual periodic variation of the NDVI from 1982 to 2015 was complex, with multiple time scales. • The periodic relationship between NDVI and climate factors has regional differences, and the periodic consistency of precipitation and NDVI is more obvious. Inner Mongolia is one of the most sensitive regions to global change with vegetation significantly affected by global warming and human activities. Therefore, exploring the dynamic change of vegetation and its impact mechanism is of great significance for guiding the protection ecosystem and policy formulation. Based on the 1982–2015 growing season and the interannual normalized difference vegetation index (NDVI), simultaneous temperature and precipitation data sets, we extracted the characteristic components of the NDVI and climatic factors by the Variational Mode Decomposition (VMD) methods. And the periodic characteristics of the NDVI in Inner Mongolia and its relationship with climate over a period of 34 years were analyzed. The results show that: 1) The VMD algorithm overcomes the "mode aliasing" problem of Empirical Mode Decomposition (EMD) and can more accurately extract the periodic information of long time series vegetation NDVI dataset containing complex frequencies.; 2) the NDVI in the growing season of Inner Mongolia has main change cycles of one growing season (7 months) and half a growing season, and temperature and NDVI have the same double-cycle relationship, whereas precipitation has a periodic change of one growing season; 3) the NDVI cycle change of Inner Mongolia vegetation from 1982 to 2015 shows multiple time scales, showing 16.95a, 6.8a, 4.85a, 4.25a, 3.4a and 2.83a change cycles; 4) vegetation NDVI in Inner Mongolia from 1982 to 2015 has a good correlation with climate factors, and the partial correlation coefficients are different in Inner Mongolia and three sampling areas. NDVI and precipitation have relatively good periodic consistency, and there is a significant positive correlation between vegetation NDVI and temperature and precipitation before and after removing the interference of periodic variables. In general, temperature and precipitation jointly affect the NDVI cyclical changes of Inner Mongolia. The VMD algorithm has significant effects on determining the cyclic dynamic changes of NDVI and climatic factors. [ABSTRACT FROM AUTHOR]

Drought poses a significant environmental risk and can deeply affect the growth of grasslands. However, there is still uncertainty regarding the precise impact of varying levels of drought on grassland growth. To address this gap, we utilized several key indicators, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Global Orbiting Carbon Observatory-2-based Solar-induced Chlorophyll Fluorescence (GOSIF), and Gross Primary Productivity (GPP), in conjunction with drought indices (the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture (SM). Our study aimed to comprehensively assess the consistency of spatiotemporal patterns in grassland vegetation and its responsiveness to different drought levels in the Inner Mongolia region from 2002 to 2020. The results indicated that NDVI, EVI, GOSIF, and GPP in grassland vegetation across Inner Mongolia exhibited significant increasing trends from 2002 to 2020. Specifically, NDVI, EVI, GOSIF, and GPP all displayed consistent spatial patterns, with 25.83%, 21.18%, 22.65%, and 48.13% of the grassland area showing significant increases, respectively. Drought events, as described through SPEI and SM, from June 2007 to September 2007 and June 2017 to July 2017 were selected to evaluate the response of grassland vegetation to drought. The drought events of 2007 and 2017 resulted in reductions in NDVI, EVI, GOSIF, and GPP relative to the multi-year average (2002–2020). GOSIF exhibited a more intense response to drought, suggesting that GOSIF may reflect the inhibition of water stress on grassland photosynthesis better than NDVI and EVI for the drought in 2007 and 2017. The reductions in NDVI, EVI, GOSIF, and GPP in grassland increased significantly across different drought levels, with the sharpest reductions observed during extreme drought. Under the severe and extreme drought events, the most substantial reductions in NDVI, EVI, GOSIF, and GPP were observed in the temperate steppe (TS). Moreover, the effects of different drought severity levels within the same grassland type varied, with the most significant reductions in NDVI, EVI, GOSIF, and GPP observed during extreme drought. Our results provide new perspectives for developing and implementing effective strategies to address grassland carbon cycling management and climate change in Inner Mongolia. [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]

Greening is recognized to occur in China; however, it is still unclear about the change of vegetation and its attributions from climate change and human activities in Inner Mongolia, located in a typical transition climate zone from humid to arid. This study has comprehensively deliberated the variations of the Normalized Difference Vegetation Index (NDVI) in 2001–2018 and quantified its controls resulting from climate change and human activities. The results showed that NDVI in Inner Mongolia experienced a significant increase with a rate of 0.0018/year (P < 0.05) during 2001–2018. Climate change dominated the NDVI variations area accounting for 55.43%, while human activities 44.57%. The effects of climate change increased from south to north. The increase in precipitation dominated the augment of NDVI in the east and middle of Inner Mongolia. Climate change had slight effects on vegetation in the drier regions due to the counterbalance effect of precipitation and vapor pressure deficit. Moreover, human activities are the chief reasons for the NDVI increase in western Inner Mongolia, especially in the arid zone. In general, ecological projects, cropland expansion, urbanization, and human management measures made positive contributions to vegetation growth. Ecological projects among all human activities were the most effective way to affect vegetation growth. [ABSTRACT FROM AUTHOR]

To reveal the characteristics of climate change and the controlling factors for vegetation dynamics in the Ordos, Inner Mongolia, China, 34 years (1982–2015) of regional climate variables and vegetation dynamics were investigated. The results show that: Annual mean air temperature (TMP) significantly increased with a linear slope of 0.473°C/10yr. Annual precipitation (PRE) had a non-significant positive trend nearly 5 times lower than the trend of potential evapotranspiration (PET). The average Normalized Difference Vegetation Index (NDVI) computed for the region was found to show a significant positive trend (6.131×10−4/yr). However, all climate variables displayed non-significant correlations with NDVI at annual scale. The reduction of desert and the increase of grassland over the past decades were accountable for the increased NDVI. Principal components analysis revealed that the regional climate change can be characterized as changes in temperature, humidity and the availability of radiant energy. Based on principal components regression coefficients, NDVI was mostly sensitive to humidity component, followed by growing season warmth (WMI). Spatially, 93.1% of the pixels displayed positive trend and 61.8% of the pixels displayed significant change over the past decades. Both principal regression analysis and partial correlation analysis revealed that NDVI in eastern part of Ordos was sensitive to TMP, whereas, NDVI in southern and western areas of Ordos displayed the high sensitivity to combined effects of PRE and cloud coverage (CLD). Partial correlation analyses also revealed that TMX was a surrogate for aridity, TMN was a representative of humidity, and temperature variations below the threshold of 5°C (CDI) were less important than WMI. We conclude that regional climate change can be characterized by warming and increased aridity. The significant positive trend of regional NDVI and the non-significant correlations between NDVI and climate variables at annual scale suggests the hidden role of the human activities. [ABSTRACT FROM AUTHOR]

Dynamic change of Soil Salinity Content (SSC) is widely used to control soil salinization for high production efficiency in modern agriculture. This study aimed to enhance the correlation between SSC and spectral reflectance under vegetation coverage using Sentinel-2 multispectral remote sensing. Soil samples of salinity and moisture were collected at 100 sampling sites with different depth ranges, including <20, 20-40, and >40-60 cm, in Shahaoqu Irrigation Area, Inner Mongolia, China, from June to August in 2019. The multispectral data of Sentinel-2 satellite was acquired synchronously according to the sample time and location. The specific procedure was as follows. Firstly, a depth decision tree was constructed with the normalized difference vegetation index as a branching criterion, where the best one was then selected to determine the optimal depth range for the SSC retrieval of soil samples. Secondly, a classification decision tree was used to divide the soil samples into different categories, according to the normalized vegetation index and Soil Moisture Content (SMC). As such, the category of each soil sample was determined using the classification decision tree. Thirdly, the optimal spectral combination for each category was calculated to serve as an input variable into the SSC inversion model. Several machine learning models were adopted for the SSC inversion models to monitor the SSC at the optimal depth range from the salinity depth decision tree, including Adaptive boosting algorithm (Adaboost), Partial Least Squares Regression (PLSR), Support Vector Machines (SVM), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). The results showed that the correlation coefficient between SSC and spectral reflectance was above 0.66, considering the decision tree. In terms of soil depth range, the optimal inversion depth for the SSC under vegetation cover was >40-60 cm, followed by <20 cm, but the SSC inversion model presented some limitations in the middle layer (20-40 cm). Furthermore, the inversion accuracy was ranked in the descending order of RF, GBDT, Adaboost, PLSR, and SVM, where the RF, GBDT, and Adaboost presented similar inversion. Correspondingly, the SSC inversion model using ensemble learning demonstrated a strong generalization ability to achieve the ideal and stable inversion under different application scenarios, compared with the other machine learning. Specifically, the SSC inversion model performed the best using RF, where the coefficient of determination, the root mean square error, the residual predictive interquartile range, and the residual predictive deviations were 0.77, 0.27%, 2.65, and 8.99, respectively. The correlation between SSC and spectral reflectance was 0.38 without considering the decision tree, indicating there was no significance in the SSC inversion model. Considering the decision tree, the correlation coefficient between soil salinity and spectral reflectance was above 0.66, and the coefficient of determination of the SSC inversion model was 0.72, indicating that the decision tree effectively enhanced the sensitivity of spectral reflectance to SSC for the high accuracy, particularly when the vegetation on the surface of the soil. Consequently, this finding can provide a promising potential way to monitor the soil salinization in the irrigated areas during crop growth using multi-spectral satellites in modern agriculture. [ABSTRACT FROM AUTHOR]

Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC2021) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R2 = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations. [ABSTRACT FROM AUTHOR]

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) serves as a pivotal metric for studying ecohydrological processes. Its dynamics are intricately linked to the interplay among water balance, energy balance, and land use. However, the precise estimation of ET at a regional scale, coupled with a comprehensive understanding of its response to changes in water, heat, and vegetation factors, presents an ongoing challenge. In this study, we assessed the necessity to refine soil evaporation simulations in arid and semiarid regions. Subsequently, we introduced the random forest algorithm to optimize the soil evaporation module of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model to enable a more accurate prediction of ET. Then, we quantitatively explored the correlation between ET and pertinent factors, such as soil moisture, solar radiation, and the normalized difference vegetation index (NDVI) in the Inner Mongolia section of the Yellow River Basin (IMSYRB), which is a typical arid and semiarid region. The findings underscore that soil evaporation is the predominant constituent of ET in the IMSYRB. Moreover, soil moisture, as opposed to relative humidity, better represent the near-surface moisture conditions within the study area. The refined model (PT-JPL-RF) exhibited enhanced simulation performance at both individual stations and in the overall region for ET. Furthermore, the application of the PT-JPL-RF model for scrutinizing the spatiotemporal ET variations in the study area revealed an average ET of 250 mm over the past four decades. Since 1982, a fluctuating upward trend of 0.99 mm/year has been observed. Spatially, ET exhibits an eastward distribution, with elevated values in the eastern sectors compared to those in the western regions, particularly in the Big Black River Basin (296 mm). Vegetation was the primary factor influencing ET variations in the IMSYRB, contributing to an annual increase by 1.58 mm. These findings demonstrate the benefits of integrating machine learning algorithms with physical models and provide a practical example for accurately simulating and predicting ET across diverse regions. • Examined the need to enhance PT-JPL model within arid and semiarid regions. • Soil moisture better reflected regional humidity than relative humidity. • Improved evapotranspiration simulations by merging PT-JPL and Random Forest models. • Boosted evapotranspiration simulation accuracy by 7.9% with the hybrid model. • Vegetation index contributed most to evapotranspiration, reaching 1.79 mm/year. [ABSTRACT FROM AUTHOR]

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas. • A novel framework for quantifying the impacts of mining activities on vegetation. • The sensitivity of 8 VIs to mining activities is assessed. • The area of vegetation by mining activities is 3 times larger than the actual mining site. • GCI, GNDVI, RDVI and SAVI are more sensitive to mining activities in arid/semi-arid areas. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of crop evapotranspiration (ET c) is essential for efficient irrigation management at the farmland scale. However, effective decision-making for irrigation scheduling requires high spatiotemporal-resolution data to provide within-field heterogeneity information. The objective of this study was to evaluate the use of optical and thermal information obtained from an unmanned aerial vehicle (UAV) to quantify maize (Zea mays L.) ET c within the framework of the FAO-56 dual crop coefficient approach. Canopy temperature-based crop water stress index (CWSI) and number of degrees above canopy threshold (DACT) were used to determine the stress coefficient (K s). Three types of normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) were adopted to determine basal crop coefficient (K cb). Then the two forms of stress coefficient in combination with three vegetation indices (VIs) were evaluated to estimate daily crop ET c under different irrigation treatments over two years in the southwest region of Inner Mongolia, China. The results demonstrated that the combination of NDVI and CWSI produced the best estimates of maize ET c , with R2 of 0.84 and root mean square error (RMSE) of 0.50 mm/day. The DACT-based model also performed well, with R2 of 0.77–0.80 and RMSE of 0.53 mm/day. Although the results varied with irrigation levels, the daily mean bias error of ET c predictions over different years indicated acceptable accuracy, with a mean bias error (MBE) of 0.48 mm/day. Sensitivity analyses indicated that ET c models were most sensitive to the slope of the linear regression between K cb and VIs, followed by soil evaporation constant and influential factor in K s_DACT. From this study, the combinations of vegetation index and CWSI, as well as DACT, were recommended as alternative approaches for estimating ET c due to intrinsic simplicity and easy interpretation. These ET c models relying on UAV-based multi-sensor data thus show promising potential in farmland-scale applications. • The potential of low-cost UAV-based image data for maize ET c prediction was evaluated. • Three vegetation indices NDVI, SAVI, and EVI were used to determine K cb. • Two different forms of K s methods (CWSI and DACT) were compared. • The robustness and adaptability of UAV-based ET c estimation model across different irrigation treatments was discussed. [ABSTRACT FROM AUTHOR]

• NDVI changes will be influenced by drought with segmental trends. • Land use and soil texture affect the relationship of SPEI-NDVI. • The degree of drought affects the probability of NDVI loss. Climate change is causing severe changes in the ecosystem, thus understanding how plants respond to drought is essential to protecting ecosystems in Inner Mongolia. Multi-temporal variations of drought in Inner Mongolia were studied using the Standardized Precipitation Evapotranspiration Index (SPEI), and the vegetation growth was analyzed using the Normalized Difference Vegetation Index (NDVI). Moreover, the correlation between droughts of different lengths and NDVI was studied. Subsequently, we developed an optimal binary probability model to calculate the probability of vegetation productivity loss at different drought levels using the copula method. The results showed: (1) a weak aridification in the western parts and significant wetness in the central and eastern parts of Inner Mongolia; (2) a significant decreasing trend of NDVI from 1982 to 2009 and a significant increasing trend from 2010 to 2015; (3) a strong positive correlation between NDVI and SPEI and a difference between their related time scales based on soil textures; and (4) a higher probability of vegetation productivity loss with increasing drought at different drought levels. [ABSTRACT FROM AUTHOR]

• Spatiotemporal distribution of vegetation productivity and climate drivers are identified. • Water availability dominated the variability of vegetation productivity for grasslands in Mongolia. • Drought contributed to more vegetation decline than hot. • Vegetation productivity declined more in the northern typical steppe under more severe hot and drought events. Vegetation productivity is controlled by multiple climatic variables such as temperature, precipitation, and radiation, while the driving mechanism remains unclear in Inner Mongolia grasslands. Since climatic factors change geographically and seasonally, we need to know their spatiotemporal patterns and how they impact plant growth. Here, we investigated vegetation responses to climatic changes by analyzing both climatic data and NDVI (Normalized Difference Vegetation Index) for 34 years (1982 to 2015). Our results indicate that water availability dominated the variability of vegetation productivity, regardless of grassland types. The vegetation productivity was more sensitive to variations in water conditions than to variations in temperature, especially in the northern region. The results are of great significance for formulating adaptation and mitigation strategies against climate impacts on grassland productivity in Inner Mongolia and provide an invaluable comparison for grassland studies in other regions. [ABSTRACT FROM AUTHOR]

The warming climate has rapidly altered vegetation growth in drylands, and consequently, has put great pressure on sustainable livelihoods. Various datasets have been applied from local to global scale to study vegetation dynamics and there is a lack of solid comparison among multiple datasets. Note that vegetation growth might shift over time and the greening and browning components over a long-time span might be masked by a linear trend. Here, we aim to monitor the long-term and nonlinear dynamics in vegetation greenness for Inner Mongolia (an important part of dryland Asia). As a useful tool that indicates vegetation greenness, NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) integrals derived from the GIMMS (Global Inventory Modelling and Mapping Studies) NDVI3g and the GIMMS LAI3g products are applied. During the period of 1982-2016, NDVI/LAI integrals have an overall acceptable consistency in characterizing the trends of vegetation greenness, with NDVI large/small integrals and LAI large/small integrals increase at a rate of 0.96, 1.72, 2.23, and 3.13 per decade, respectively. Inner Mongolia experienced a noticeable greening process (71% and 82% greening area in NDVI large/small integrals, 67% and 73% greening area in LAI large/small integrals), despite the fragmentally distributed browning trends in eastern and partial northern Inner Mongolia. As inferred from nonlinear trend analysis, we found the greening process is still prevalent. The browning of eastern Inner Mongolia under the linear analysis was actually transferring from browning to greening, while the greening trend in northern Inner Mongolia was changing to browning. Increased occurrences in the frequency of breakpoints after 1999 suggest that previously stable vegetation ecology is more sensitive to external disturbances such as altered climatic impact and anthropogenic intervention. [ABSTRACT FROM AUTHOR]

Vegetation in arid and semi-arid regions is extremely sensitive to climate change and human activities, and when disturbed, it is prone to irreversible degradation. However, the mechanisms of how natural and anthropogenic factors drive vegetation variations remain unclear for environmentally sensitive and vulnerable areas. To understand the relationships between vegetation variation, climate change, human activities, relief degree of the land surface, and El Niño-Southern Oscillation (ENSO), trend analysis, partial correlation analysis, residual trend (RESTREND), and wavelet coherence approach were utilized. Normalized difference vegetation index (NDVI), meteorological data, and ENSO were used as data sources to determine the spatial response of vegetation growth to climate change and human activity from 1982 to 2018. This study defined the 400 mm annual precipitation fluctuation zone in China (APFZC) for the first time, involving 15 provinces, accounting for 27.37% of China. Vegetation activity was mainly driven by precipitation, temperature, sunshine duration, and wind speed. Precipitation and temperature promoted vegetation activity, while the wind speed inhibited it. Moreover, vegetation variation exerts an obvious topographic effect and is deeply affected by ENSO. Under the influence of climate change from 1982 to 2018, the vegetation became greener in the Loess Plateau, North China Plain, Qinghai, and the border regions of Inner Mongolia and Liaoning; however, in the northeastern part of Inner Mongolia and the western part of Heilongjiang, vegetation was browning. In 1982–1998 and 1999–2018, the contribution rates of climate change to vegetation variation were 50.36% and 42.89%, respectively, and human activities were 49.64% and 57.11%, respectively. This information is crucial for alleviating climate change and for the ecological restoration of vegetation, especially in environmentally sensitive and vulnerable areas. [ABSTRACT FROM AUTHOR]