Search

In this study, a multilevel drought-induced resistance and resilience analysis (MDRRA) approach was developed to investigate the stability of vegetation in the Yellow River Basin (YRB). MDRRA was quantified by utilizing the Normalized Difference Vegetation Index (NDVI). It was applied to YRB to assess vegetation resistance and resilience to various levels of drought by utilizing precipitation and NDVI data from 2000 to 2019. The results reveal that vegetation resistance and resilience in YRB are affected by drought severity. Monthly and annual changes in SPI over the warm–temperate humid zone of the YRB show a decreasing trend, with rates of 0.001 per decade and 0.034 per decade, respectively; however, the other climatic subregions exhibit an increasing trend, with rates ranging from 0.002 per decade to 0.82 per decade. Over 77.56% of the downstream areas show increases in the annual SPI averages. Drought severity differs across subregions in the YRB. More severe drought events occur in its upper and middle reaches, while less severe ones happen in its lower reaches. As the drought severity increases, the arid and semiarid regions of the mesothermal zone exhibit a decrease in the resistance and resilience indices. MDRRA can help improve the stability and resilience of the ecosystem in the YRB. [ABSTRACT FROM AUTHOR]

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices. [ABSTRACT FROM AUTHOR]

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region. [ABSTRACT FROM AUTHOR]

PM2.5, a critical air pollutant, requires health-conscious management, with concentrations varying across regions due to diverse sources. This study, conducted in South Korea in 2021, employed the geographically weighted regression model to analyze the spatiotemporal correlations of PM2.5 with O3 and the normalized difference vegetation index (NDVI). Regional differences in the correlation between PM2.5 and O3 were observed, influenced by common precursors (SOx, NOx, and volatile organic compounds (VOCs)), seasonal temperature variations, and solar radiation differences. Notably, PM2.5 and O3 exhibited a heightened regression coefficient in summer, emphasizing the need for specific management targeting VOCs and NO2. The interplay between PM2.5 and NDVI revealed a negative overall impact but a positive effect in the central region of Korea, suggesting vegetation's role in the PM2.5 concentration increase due to atmospheric stagnation caused by mountain ranges. These findings enhance our understanding of PM2.5 distribution mechanisms, highlighting the need for tailored policies in each region for effective concentration reductions. [ABSTRACT FROM AUTHOR]

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices.

In this study, a multilevel drought-induced resistance and resilience analysis (MDRRA) approach was developed to investigate the stability of vegetation in the Yellow River Basin (YRB). MDRRA was quantified by utilizing the Normalized Difference Vegetation Index (NDVI). It was applied to YRB to assess vegetation resistance and resilience to various levels of drought by utilizing precipitation and NDVI data from 2000 to 2019. The results reveal that vegetation resistance and resilience in YRB are affected by drought severity. Monthly and annual changes in SPI over the warm–temperate humid zone of the YRB show a decreasing trend, with rates of 0.001 per decade and 0.034 per decade, respectively; however, the other climatic subregions exhibit an increasing trend, with rates ranging from 0.002 per decade to 0.82 per decade. Over 77.56% of the downstream areas show increases in the annual SPI averages. Drought severity differs across subregions in the YRB. More severe drought events occur in its upper and middle reaches, while less severe ones happen in its lower reaches. As the drought severity increases, the arid and semiarid regions of the mesothermal zone exhibit a decrease in the resistance and resilience indices. MDRRA can help improve the stability and resilience of the ecosystem in the YRB.

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region.

This study harnesses ground observation data collected between 1980 and 2021 and ERA5 hourly data to thoroughly implement trend and correlation analysis techniques to explore the spatiotemporal dynamic characteristics of daily and hourly extreme precipitation in the Sichuan Basin. The investigation delineates these characteristics and probes into the potential triggers of extreme hourly rainstorms. The findings unveil the following: (1) A general increase in extreme rainfall volume, contribution rate, intensity, and dispersion, along with a decline in frequency and proportion of rainstorm areas, indicating the concentration of daily-scale severe rainstorms. The basin's edge receives more precipitation than the bottom, exhibiting latitudinal variations. (2) The northernmost mountainous regions have less frequent, less intense rainstorms influenced by terrain, whereas the northeastern region experiences more frequent, dispersed rainstorms. (3) Extreme hourly rainstorms predominantly occur at night, with rainfall amount, intensity, and frequency declining at 21:00 compared to 19:00. (4) Summer experiences the highest risk of extreme rainstorms, with annual and monthly datasets displaying a rising trend in the frequency, dispersion, and intensity of intense hourly rainstorms. (5) Peak values of extreme hourly rainstorms are growing, with two distinct periods for their frequency: 1:00–9:00 and 10:00–24:00, with an increase in the former and a decrease in the latter. (6) Normalized difference vegetation index (NDVI) values ascend from southwest to northeast within the basin on a ten-day scale, correlating with the distribution of hourly extreme precipitation. [ABSTRACT FROM AUTHOR]

The term flash drought describes a special category of drought with rapid onset and strong intensity over the course of days or weeks. To characterize the impact of flash droughts on vegetation coverage, this study assessed the influence of soil water deficits on vegetation dynamics in the northeastern South America region by combining time series of vegetation index, rainfall, and soil moisture based on satellite products at a daily time scale. An 18-year analysis, from 2004 to 2022, of the Normalized Difference Vegetation Index (NDVI), Standard Precipitation Index (SPI), and surface soil moisture (SSM) was performed based on three different satellite remote sensing estimates: the spinning enhanced visible and infrared imager (SEVIRI) and the integrated multi-satellite retrievals algorithm (IMERG), and the soil moisture and ocean salinity (SMOS). The results revealed that flash drought events exerted dramatic impacts on terrestrial ecosystems in the study region during the first two decades of the 2000s, with changes in seasonal and regional vegetation dynamics. Further, the fixed-threshold values to characterize flash drought events were suggested as the timing when the water deficit was less than −1.0 units and vegetation index reached the value equal to +0.3 during five consecutive weeks or more, coupled with soil moisture rates below 40% percentile, leading to a strong region-wide drought throughout the entire region. Additionally, the results of linear least squares trend analyses revealed a negative trend in the pentad-SEVIRI radiance for the solar channel 1 within the semiarid ecosystems of the study region (i.e., the Caatinga biome) that was suggested as a reduction in clouds in the 18 years of the study. Developing combined threshold measures of flash drought based on satellite remote sensing may lead to an accurate assessment of flash drought mitigation. [ABSTRACT FROM AUTHOR]

The Kerkennah archipelago in Tunisia is one of the most vulnerable areas where the influence of climate change is undeniable. Soil salinization has emerged as a major consequence of climate variation on this island. In this study, remote sensing techniques were implemented to develop a model for predicting soil salinity from satellite images. Machine learning algorithms, Sentinel-1 and Sentinel-2 data, and ground truth measurements were used to estimate soil salinity. Several algorithms were considered to achieve accurate findings. These algorithms are categorized as polynomial regression, random forest regression, exponential regression, and linear regression. The results demonstrate that exponential regression is the pre-eminent algorithm for estimating soil salinity with high predictive accuracy of R2 = 0.75 and RMSE = 0.47 ds/m. However, spatiotemporal soil salinity maps reveal distinct and clear distribution patterns, highlighting salty areas (i.e., sebkhas) and agricultural parcels. Thus, through the model, we explore areas of moderately high salinity within agricultural lands that could be affected by irrigation practices. The present work demonstrates a reliable model for soil salinity monitoring in the Kerkennah archipelago and inspires more successful technologies such as remote sensing and machine learning to improve the estimation of soil salinity in climate-affected vulnerable areas. [ABSTRACT FROM AUTHOR]

Altered temperature and precipitation regimes associated with climate change generally result in improved conditions for plant growth. For Arctic and sub-Arctic ecosystems, this new climatic context promotes an increase in primary productivity, a phenomenon often referred to as "greening". Although this phenomenon has been widely documented at the circumpolar scale, little information is available at the scale of plant communities, the basic unit of the Arctic and sub-Arctic landscape mosaic. The objectives of this study were (1) to quantify the variation of NDVI within the different plant communities of Nunavik (Québec, QC, Canada) in order to identify which ones contributed the most to the greening and (2) to identify the climatic and biophysical drivers of the greening. To do so, we used Landsat imagery to produce a time series of summer NDVI for the period 1984 to 2020. A fine-resolution map of Northern Québec vegetation was then overlaid on the time series of NDVI imagery and on maps of surficial deposits, topography, and gridded climate data to obtain information at the plant community level. We found that greening was more important in shrub-dominated communities, particularly near the tree line. Summer temperature, fall and winter precipitation, and surficial deposits were identified as drivers of greening. Through utilizing detailed vegetation maps to accurately quantify changes in Nunavik's plant communities, this study provides valuable insights into the dynamics of the region's ecosystem under rapid climate change. [ABSTRACT FROM AUTHOR]

PM2.5, a critical air pollutant, requires health-conscious management, with concentrations varying across regions due to diverse sources. This study, conducted in South Korea in 2021, employed the geographically weighted regression model to analyze the spatiotemporal correlations of PM2.5 with O3 and the normalized difference vegetation index (NDVI). Regional differences in the correlation between PM2.5 and O3 were observed, influenced by common precursors (SOx, NOx, and volatile organic compounds (VOCs)), seasonal temperature variations, and solar radiation differences. Notably, PM2.5 and O3 exhibited a heightened regression coefficient in summer, emphasizing the need for specific management targeting VOCs and NO2. The interplay between PM2.5 and NDVI revealed a negative overall impact but a positive effect in the central region of Korea, suggesting vegetation’s role in the PM2.5 concentration increase due to atmospheric stagnation caused by mountain ranges. These findings enhance our understanding of PM2.5 distribution mechanisms, highlighting the need for tailored policies in each region for effective concentration reductions.

Based on the NDVI data of vegetation in Inner Mongolia from 1982 to 2015, the variational mode decomposition (VMD) method, which has been well applied in the field of signal decomposition, is introduced to study the periodicity of vegetation index in Inner Mongolia. The VMD method is used to extract the monthly and annual NDVI and the long time series cycle characteristics of temperature and precipitation in the same period from April 1982 to October 2015 in Inner Mongolia. The results show that temperature and precipitation are important factors affecting the growth of vegetation, and there are 6.99 and 3.49 months of the same oscillation cycle for monthly NDVI and temperature and precipitation time series; when the central frequency is the same, the amplitude of the monthly temperature and precipitation time series increases with the increase of the lag period. The annual scale NDVI has the same period of 16.95, 6.8a, and 4.85a with precipitation, and the same period of 6.8a and 4.85a with temperature. The Residue component shows that the overall NDVI and temperature in Inner Mongolia have shown a significant slow growth trend in the past 30 years. Although the precipitation has shown a significant slow decline trend in the same time period (p = 0.000), the grassland is still in the process of continuous improvement. [ABSTRACT FROM AUTHOR]

Land-use/land-cover change and climate change have changed the spatial–temporal distribution of water resources. The Huai River Basin shows the spatial and temporal changes of climate from 1960 to 2016 and land-use/land-cover changes from 1995 to 2014. Thus, this study aims to investigate climate change, land use, and vegetation evolution in the Upper Huai River Basin. The Mann–Kendall test (MK), Innovative Trend Analysis Method (ITAM), and Sen's slope estimator test were used to detect climate change trends. The land-use/land-cover change was also examined using a transformation matrix and Normalized Difference Vegetation Index (NDVI). The results of this study revealed that precipitation has shown a slightly decreasing trend during the past 56 years. However, the air temperature has increased by 1.2 °C. The artificial and natural vegetation and wetland were decreased by 12,097 km2, 3207 km2, and 641 km2, respectively. On the other hand, resident construction land and artificial water bodies increased by 2277 km2 and 3691 km2, respectively. This indicates that the land cover has significantly changed during the past 30 years. The findings of this study will have implications for predicting the water resources safety and eco-environment of The Huai River Basin. The spatial distribution showed an uneven change in the Huai River Basin. Together, we suggested that the variability of water resources availability in the Huai River Basin was mainly attributed to climate variability, while land use change plays a key role in the sub-basins, which experienced dramatic changes in land use. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson's correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson's correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation. [ABSTRACT FROM AUTHOR]

This study harnesses ground observation data collected between 1980 and 2021 and ERA5 hourly data to thoroughly implement trend and correlation analysis techniques to explore the spatiotemporal dynamic characteristics of daily and hourly extreme precipitation in the Sichuan Basin. The investigation delineates these characteristics and probes into the potential triggers of extreme hourly rainstorms. The findings unveil the following: (1) A general increase in extreme rainfall volume, contribution rate, intensity, and dispersion, along with a decline in frequency and proportion of rainstorm areas, indicating the concentration of daily-scale severe rainstorms. The basin’s edge receives more precipitation than the bottom, exhibiting latitudinal variations. (2) The northernmost mountainous regions have less frequent, less intense rainstorms influenced by terrain, whereas the northeastern region experiences more frequent, dispersed rainstorms. (3) Extreme hourly rainstorms predominantly occur at night, with rainfall amount, intensity, and frequency declining at 21:00 compared to 19:00. (4) Summer experiences the highest risk of extreme rainstorms, with annual and monthly datasets displaying a rising trend in the frequency, dispersion, and intensity of intense hourly rainstorms. (5) Peak values of extreme hourly rainstorms are growing, with two distinct periods for their frequency: 1:00–9:00 and 10:00–24:00, with an increase in the former and a decrease in the latter. (6) Normalized difference vegetation index (NDVI) values ascend from southwest to northeast within the basin on a ten-day scale, correlating with the distribution of hourly extreme precipitation.

The term flash drought describes a special category of drought with rapid onset and strong intensity over the course of days or weeks. To characterize the impact of flash droughts on vegetation coverage, this study assessed the influence of soil water deficits on vegetation dynamics in the northeastern South America region by combining time series of vegetation index, rainfall, and soil moisture based on satellite products at a daily time scale. An 18-year analysis, from 2004 to 2022, of the Normalized Difference Vegetation Index (NDVI), Standard Precipitation Index (SPI), and surface soil moisture (SSM) was performed based on three different satellite remote sensing estimates: the spinning enhanced visible and infrared imager (SEVIRI) and the integrated multi-satellite retrievals algorithm (IMERG), and the soil moisture and ocean salinity (SMOS). The results revealed that flash drought events exerted dramatic impacts on terrestrial ecosystems in the study region during the first two decades of the 2000s, with changes in seasonal and regional vegetation dynamics. Further, the fixed-threshold values to characterize flash drought events were suggested as the timing when the water deficit was less than −1.0 units and vegetation index reached the value equal to +0.3 during five consecutive weeks or more, coupled with soil moisture rates below 40% percentile, leading to a strong region-wide drought throughout the entire region. Additionally, the results of linear least squares trend analyses revealed a negative trend in the pentad-SEVIRI radiance for the solar channel 1 within the semiarid ecosystems of the study region (i.e., the Caatinga biome) that was suggested as a reduction in clouds in the 18 years of the study. Developing combined threshold measures of flash drought based on satellite remote sensing may lead to an accurate assessment of flash drought mitigation.

Several studies have assessed droughts and vegetation considering climatic factors, particularly El Niño-Southern Oscillation (ENSO) at different latitudes. However, there are knowledge gaps in the tropical Andes, a region with high spatiotemporal climatic variability. This research analyzed the relationships between droughts, vegetation, and ENSO from 2001–2015. Meteorological drought was analyzed using the Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 3 and 6 months. Normalized Difference Vegetation Index (NDVI) was used to evaluate vegetation, and ENSO indexes were used as climate drivers. The Wavelet coherence method was used to establish time-frequency relationships. This approach was applied in the Machángara river sub-basin in the Southern Ecuadorian Andes. The results showed significant negative correlations during 2009–2013 between the SPEI and NDVI, with the SPEI6 lagging by nine months and a return period of 1.5 years. ENSO–SPEI presented the highest negative correlations during 2009–2014 and a return period of three years, with ENSO leading the relationship for around fourteen months. ENSO-NDVI showed the highest positive correlations during 2004–2008 and a return period of one year, with the ENSO indexes continually delayed by approximately one month. These results could be a benchmark for developing advanced studies for climate hazards. [ABSTRACT FROM AUTHOR]

Sanjiang Plain is the most extensive marsh distribution region in China. Marshes in this region can protect biodiversity, regulate climate, and provide habitats for wild animals and plants. The normalized difference vegetation index (NDVI) is a crucial indicator of vegetation coverage, which may reflect ecosystem structure and functional features. Clarifying the spatiotemporal change of marsh coverage and its climatic drivers is vital for observing and predicting vegetation change in Sanjiang Plain. Using meteorological dataand MODIS NDVI data from 2000 to 2020, we analyzed the spatiotemporal variation in marsh vegetation coverage and climatic change effects in Sanjiang Plain. We found that the growing season vegetation NDVI of marsh increased significantly at a rate of 0.011/decade, indicating that the marsh vegetation growth has obviously improved during the past 21 years. Furthermore, we found that the increase of minimum (Tmin) and maximum (Tmax) temperature in July can significantly promote the marsh plant growth, and increasing nighttime Tmin has a stronger impact on promoting the growth than increasing daytime Tmax in this month. In addition, the increase of daytime Tmax in August can promote the marsh vegetation growth, whereas the increasing precipitation in August was unfavorable for the growth in Sanjiang Plain. [ABSTRACT FROM AUTHOR]

The ecological environment in permafrost regions is very sensitive to climate change and human activities. The effects of coal mining on the vegetation in permafrost regions have been poorly studied. Herein, on the basis of a field survey in the Juhugen mining area of Qilian Mountain, China, we investigated and quantified the influence of open-pit coal mining on vegetation coverage degradation in permafrost areas. According to the NDVI and field survey, the vegetation coverage was divided into five levels from low to high in the Arc GIS platform. Compared with the area not affected by coal mining, vegetation degradation was significant in the coal-mining-affected area, especially in the high-vegetation-coverage area. The vegetation coverage in Level 5 decreased from 51.99% to 21.35%. According to the conversion matrix, the transfer-out area in high coverage was larger, while the transfer-in area in low vegetation coverage was larger. The transfer-out area of five levels was significant in levels 2–5, accounting for 36.1% to 62.8% of the total area. The transfer-in area of five levels was significant in levels 1–4, accounting for 55.2% to 75.0% of the total area. Moreover, the ground surface temperature and water change were monitored in the vegetation degradation area. The results showed that the above degradation was related to an increase in the ground surface temperature and a decrease in the ground surface moisture. [ABSTRACT FROM AUTHOR]

Rapid urbanisation, economic growth, and urban spatial development in developing countries, such as Iran, have resulted in tremendous loss of green cover and associated ecological problems. Any effort to achieve sustainable urban development should be supported by recognising and evaluating the ecological health of vegetation cover. This study investigates vegetation cover reduction and changes in the Tehran Metropolis, Iran and identifies the most important factors influencing the observed changes. The aim of this study is two-fold: first, to assess the spatio-temporal changes in vegetation cover in Tehran between 1990 and 2020, and second, to identify the factors contributing to the changes. The Normalised Difference Vegetation Index (NDVI) is used as an indicator of green cover. The spatial and statistical data used in this study were extracted from Landsat satellite imagery and the last approved Master Plan of Tehran (2006). Geographically Weighted Regression (GWR) and geographical modelling methods were employed to analyse vegetation cover in all municipal districts of the Tehran metropolis. The results show that the vegetation density in the Tehran metropolis decreased significantly (from 38,936.80 hectares to 4663.23 hectares) between 1990 and 2020. The expansion of construction lands and the increase of population density were the most significant factors affecting the reduction in vegetation cover in Tehran. In contrast, the growth of industrial units in the urban areas of Tehran had no significant relationship with vegetation cover. The results of this study can help urban planners understand the significant drivers of vegetation loss and identify appropriate interventions to prevent it. [ABSTRACT FROM AUTHOR]

In the current study area (Faridabad, Gurugram, Ghaziabad, and Gautam Buddha Nagar), the aerosol concentration is very high, adversely affecting the environmental conditions and air quality. Investigating the impact of Land Use Land Cover (LULC) on Aerosol Optical Depth (AOD) helps us to develop effective solutions for improving air quality. Hence, the spectral indices derived from LULC ((Normalized difference vegetation index (NDVI), Soil adjusted vegetation index (SAVI), Enhanced vegetation index (EVI), and Normalized difference build-up index (NDBI)) with Moderate Resolution Imaging Spectroradiometer (MODIS) Multiangle Implementation of Atmospheric Correction (MAIAC) high spatial resolution (1 km) AOD from the years 2010–2019 (less to high urbanized period) has been correlated. The current study used remote sensing and Geographical Information System (GIS) techniques to examine changes in LULC in the current study region over the ten years (2010–2019) and the relationship between LULC and AOD. A significant increase in built-up areas (12.18%) and grasslands (51.29%) was observed during 2010–2019, while cropland decreased by 4.42%. A positive correlation between NDBI and SAVI (0.35, 0.27) indicates that built-up soils play an important role in accumulating AOD in a semi-arid region. At the same time, a negative correlation between NDVI and EVI (−0.24, −0.15) indicates the removal of aerosols due to an increase in vegetation. The results indicate that SAVI can play an important role in PM2.5 modeling in semi-arid regions. Based on these findings, urban planners can improve land use management, air quality, and urban planning. [ABSTRACT FROM AUTHOR]

Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China's temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows based on partial correlation analysis method, the temporal variations of responses of vegetation NDVI to rising air temperature during 1982–2015 in the TC were examined. The results showed that the interannual partial correlation between NDVI and air temperature (RNDVI−T, include RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin, represents the partial correlation between NDVI and Tmean, Tmax, and Tmin, respectively) for the growing season (GS) in a 17−year moving window showed a significant decreasing trend during the last 34 years, mainly due to decreasing RNDVI−T in summer and autumn. The area with a significant decrease of RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin for the GS approximately accounted for 52.36%, 45.63%, and 49.98% of the TC, respectively. For the seasonal patterns of RNDVI−T, the regions with a significant downward trend in all seasons were higher than those with a significant upward trend. We also found a more significant and accelerating decrease of RNDVI−T for warm years compared to cold years, implying a decoupling or even a reverse correlation between NDVI and air temperature with continuous climate warming over the TC. Overall, our study provided evidence that the impact of Tmean, Tmax, and Tmin on vegetation activities exhibited a weakening trend and cautioned using results from interannual time scales to constrain the decadal response of vegetation growth to future global warming. [ABSTRACT FROM AUTHOR]

The Kerkennah archipelago in Tunisia is one of the most vulnerable areas where the influence of climate change is undeniable. Soil salinization has emerged as a major consequence of climate variation on this island. In this study, remote sensing techniques were implemented to develop a model for predicting soil salinity from satellite images. Machine learning algorithms, Sentinel-1 and Sentinel-2 data, and ground truth measurements were used to estimate soil salinity. Several algorithms were considered to achieve accurate findings. These algorithms are categorized as polynomial regression, random forest regression, exponential regression, and linear regression. The results demonstrate that exponential regression is the pre-eminent algorithm for estimating soil salinity with high predictive accuracy of R2 = 0.75 and RMSE = 0.47 ds/m. However, spatiotemporal soil salinity maps reveal distinct and clear distribution patterns, highlighting salty areas (i.e., sebkhas) and agricultural parcels. Thus, through the model, we explore areas of moderately high salinity within agricultural lands that could be affected by irrigation practices. The present work demonstrates a reliable model for soil salinity monitoring in the Kerkennah archipelago and inspires more successful technologies such as remote sensing and machine learning to improve the estimation of soil salinity in climate-affected vulnerable areas.

Altered temperature and precipitation regimes associated with climate change generally result in improved conditions for plant growth. For Arctic and sub-Arctic ecosystems, this new climatic context promotes an increase in primary productivity, a phenomenon often referred to as “greening”. Although this phenomenon has been widely documented at the circumpolar scale, little information is available at the scale of plant communities, the basic unit of the Arctic and sub-Arctic landscape mosaic. The objectives of this study were (1) to quantify the variation of NDVI within the different plant communities of Nunavik (Québec, QC, Canada) in order to identify which ones contributed the most to the greening and (2) to identify the climatic and biophysical drivers of the greening. To do so, we used Landsat imagery to produce a time series of summer NDVI for the period 1984 to 2020. A fine-resolution map of Northern Québec vegetation was then overlaid on the time series of NDVI imagery and on maps of surficial deposits, topography, and gridded climate data to obtain information at the plant community level. We found that greening was more important in shrub-dominated communities, particularly near the tree line. Summer temperature, fall and winter precipitation, and surficial deposits were identified as drivers of greening. Through utilizing detailed vegetation maps to accurately quantify changes in Nunavik’s plant communities, this study provides valuable insights into the dynamics of the region’s ecosystem under rapid climate change.

Groundwater depletion has become a major concern all over the world. Recently, the rapid population growth and need for water and food have placed a massive strain on land and water resources. In this study, groundwater depletion resulting from land-use and climate change was investigated in the Faisalabad district, Pakistan, from 2000 to 2015. A Pearson correlation analysis between climatic parameters and land-use indices with groundwater was conducted to explore the major influencing factors. Interpolation maps of groundwater were generated using the inverse distance weighting interpolation (IDW) method. The Normalized Difference Built-up Index (NDBI) of five-year intervals demonstrated a strong increasing trend, whereas the Normalized Difference Vegetation Index (NDVI) presented a declining trend. The results also indicated a significant declining trend in groundwater levels in the region, with the annual average groundwater level decreasing at a rate of approximately 0.11 m/year. Climatic parameters (i.e., precipitation and temperature) further reveal an insignificant increasing trend estimated using the Mann–Kendall test and Sens's slope. Overall, spatial analysis results showed a statistically significant positive trend in the groundwater level of the Faisalabad district, where the NDBI ratio is high and the NDVI is low, owing to the extensive extraction of groundwater for domestic and industrial use. These findings may be useful for a better understanding of groundwater depletion in densely populated areas and could also aid in devising safety procedures for sustainable groundwater management. [ABSTRACT FROM AUTHOR]

Based on the NDVI data of vegetation in Inner Mongolia from 1982 to 2015, the variational mode decomposition (VMD) method, which has been well applied in the field of signal decomposition, is introduced to study the periodicity of vegetation index in Inner Mongolia. The VMD method is used to extract the monthly and annual NDVI and the long time series cycle characteristics of temperature and precipitation in the same period from April 1982 to October 2015 in Inner Mongolia. The results show that temperature and precipitation are important factors affecting the growth of vegetation, and there are 6.99 and 3.49 months of the same oscillation cycle for monthly NDVI and temperature and precipitation time series; when the central frequency is the same, the amplitude of the monthly temperature and precipitation time series increases with the increase of the lag period. The annual scale NDVI has the same period of 16.95, 6.8a, and 4.85a with precipitation, and the same period of 6.8a and 4.85a with temperature. The Residue component shows that the overall NDVI and temperature in Inner Mongolia have shown a significant slow growth trend in the past 30 years. Although the precipitation has shown a significant slow decline trend in the same time period (p = 0.000), the grassland is still in the process of continuous improvement.

The East Asian dust storms occur in western and northern China, and southern Mongolia every year, particularly in spring. In this study, we use satellite aerosol products to demonstrate the spatial and temporal variation in aerosol optical depth (AOD) from MODIS, and the absorbing aerosol index (AAI) from TOMS and OMI, over the main dust storm source regions (MDSR), and to investigate their relationship to vegetation coverage (NDVI), soil properties (surface soil moisture content and soil temperature 0–10 cm underground), and climatic factors (surface wind speed, air temperature at 2 m above the ground, and precipitation) in spring for the period of 2000–2021. Compared with dust storm occurrence frequency (DSF) observed at surface stations, MODIS AOD, TOMS AAI, and OMI AAI showed consistent spatial distributions and seasonal variations with DSF in the MDSR, with correlation coefficients of 0.88, 0.55, and 0.88, respectively. The results showed that AOD and AAI over the MDSR decreased during 2000–2005, 2006–2017, and 2000–2021, but increased during 2017–2021.The improvements in vegetation coverage and soil moisture together with favorable climatic factors (the increase in temperature and precipitation and the decrease in surface wind speed) resulted in the decreasing trend of AOD and AAI during 2000–2005, 2006–2017, and the entire period of 2000–2021. Conversely, the increase in surface wind speed, the decrease in temperature and the low soil moisture in 2018 and 2020 were the reasons for the increases in AOD and AAI over the MDSR during 2017–2021. The combination effects of surface wind, temperature, soil moisture, and vegetation coverage would determine DSF, AOD, and AAI, in the end, under global climate change. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in regulating terrestrial–atmospheric water circulation and energy balance. Most of the existing studies have explored the dynamic patterns of SM based on experimental methods. However, the analysis of large-scale regions and long-term SM sequences was limited. Alternatively, satellite remote sensing data is a potential source for SM analysis for large-scale basins. Therefore, the SM data from the European Space Agency (ESA) Climate Change Initiative (CCI) from 2000 to 2015 is used in this paper to analyze the SM spatial-temporal changes in the Yellow River Basin (YRB). Further, the Normalized Difference Vegetation Index (NDVI) and meteorological data are used to explore the relationships between SM and NDVI, precipitation, air temperature, and wind speed, respectively. The results showed that the overall trend of SM in the YRB was decreasing from southeast to northwest during the past 16 years. The upper reaches of the YRB had shown a humid trend, with a value of 0.00047 m3·m−3·year−1, mainly due to the increase in precipitation; there was an obvious drought trend in the middle reaches of the YRB, especially in Shanxi Province and Henan Province, with a value of −0.00030 m3·m−3·year−1, which may be owed to vegetation greening increasing the soil evaporation. Overall, it is determined that the main factors influencing SM changes were NDVI and precipitation, followed by air temperature and wind speed. This study can provide a scientific basis for the spatial-temporal distribution characteristics and attributions of SM in the YRB over a long time series. [ABSTRACT FROM AUTHOR]

We present the development of a physically-based dust source map for the GOCART-AFWA dust module in WRF-Chem model. The new parameterization is based on MODIS-NDVI and an updated emission strength map is computed every 15 days from the latest satellite observations. Modeling simulations for the period April–May 2017 over the Mediterranean, north Africa, and the Middle East are compared with observations of AOD at 31 AERONET stations. The new module is capable of reproducing the dust sources at finer detail. The overall performance of the model is improved, especially for stronger dust episodes with AOD > 0.25. For this threshold the model BIAS decreases from −0.20 to −0.02, the RMSE from 0.38 to 0.30, the Correlation Coefficient improves from 0.21 to 0.47, the fractional gross error (FGE) from 0.62 to 0.40, and the mean fractional bias (MFB) from −0.49 to −0.08. Similar improvement is also found for the lower AOD thresholds (>0.0 and >0.1), especially for the stations in Europe, the Mediterranean, Sahel, the Middle East, and Arabian Peninsula, which are mostly affected by dust transport during the experimental period. An overprediction of AOD, compared to the original dust-source scheme, is found for some stations in the Sahara desert, the Atlantic Ocean, and the Iberian Peninsula. In total, 124 out of the 170 statistical scores that are calculated indicate improvement of model performance. [ABSTRACT FROM AUTHOR]

Global warming is not an expectation but a reality in the "oreums" (common local name for rising, small defunct volcanoes on Jeju Island, Republic of Korea). The oreums exhibit wide biodiversity. However, their ecology is threatened by its associated climate change and their ecological changes have rarely been monitored or recorded. We used three years of Sentinel-2 image data to generate a normalized difference vegetation index (NDVI) map of the Geum-oreum area. We found that the NDVI was highly associated with temperature, implying that Sentinel-2 images could be utilized to monitor the temperature variation in the oreums to assist in planning and preparation to conserve their ecosystems before they are jeopardized. The results indicated that the NDVI maps derived from Sentinel-2 images were highly associated with temperature in Geum-oreum. We expect this method could be applied in other regions to detect temperature variation for ecological management planning in large areas (such as forests). [ABSTRACT FROM AUTHOR]

Land-use/land-cover change and climate change have changed the spatial–temporal distribution of water resources. The Huai River Basin shows the spatial and temporal changes of climate from 1960 to 2016 and land-use/land-cover changes from 1995 to 2014. Thus, this study aims to investigate climate change, land use, and vegetation evolution in the Upper Huai River Basin. The Mann–Kendall test (MK), Innovative Trend Analysis Method (ITAM), and Sen’s slope estimator test were used to detect climate change trends. The land-use/land-cover change was also examined using a transformation matrix and Normalized Difference Vegetation Index (NDVI). The results of this study revealed that precipitation has shown a slightly decreasing trend during the past 56 years. However, the air temperature has increased by 1.2 °C. The artificial and natural vegetation and wetland were decreased by 12,097 km2, 3207 km2, and 641 km2, respectively. On the other hand, resident construction land and artificial water bodies increased by 2277 km2 and 3691 km2, respectively. This indicates that the land cover has significantly changed during the past 30 years. The findings of this study will have implications for predicting the water resources safety and eco-environment of The Huai River Basin. The spatial distribution showed an uneven change in the Huai River Basin. Together, we suggested that the variability of water resources availability in the Huai River Basin was mainly attributed to climate variability, while land use change plays a key role in the sub-basins, which experienced dramatic changes in land use.

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson’s correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson’s correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation.

The ecological environment in permafrost regions is very sensitive to climate change and human activities. The effects of coal mining on the vegetation in permafrost regions have been poorly studied. Herein, on the basis of a field survey in the Juhugen mining area of Qilian Mountain, China, we investigated and quantified the influence of open-pit coal mining on vegetation coverage degradation in permafrost areas. According to the NDVI and field survey, the vegetation coverage was divided into five levels from low to high in the Arc GIS platform. Compared with the area not affected by coal mining, vegetation degradation was significant in the coal-mining-affected area, especially in the high-vegetation-coverage area. The vegetation coverage in Level 5 decreased from 51.99% to 21.35%. According to the conversion matrix, the transfer-out area in high coverage was larger, while the transfer-in area in low vegetation coverage was larger. The transfer-out area of five levels was significant in levels 2–5, accounting for 36.1% to 62.8% of the total area. The transfer-in area of five levels was significant in levels 1–4, accounting for 55.2% to 75.0% of the total area. Moreover, the ground surface temperature and water change were monitored in the vegetation degradation area. The results showed that the above degradation was related to an increase in the ground surface temperature and a decrease in the ground surface moisture.

Sanjiang Plain is the most extensive marsh distribution region in China. Marshes in this region can protect biodiversity, regulate climate, and provide habitats for wild animals and plants. The normalized difference vegetation index (NDVI) is a crucial indicator of vegetation coverage, which may reflect ecosystem structure and functional features. Clarifying the spatiotemporal change of marsh coverage and its climatic drivers is vital for observing and predicting vegetation change in Sanjiang Plain. Using meteorological dataand MODIS NDVI data from 2000 to 2020, we analyzed the spatiotemporal variation in marsh vegetation coverage and climatic change effects in Sanjiang Plain. We found that the growing season vegetation NDVI of marsh increased significantly at a rate of 0.011/decade, indicating that the marsh vegetation growth has obviously improved during the past 21 years. Furthermore, we found that the increase of minimum (Tmin) and maximum (Tmax) temperature in July can significantly promote the marsh plant growth, and increasing nighttime Tmin has a stronger impact on promoting the growth than increasing daytime Tmax in this month. In addition, the increase of daytime Tmax in August can promote the marsh vegetation growth, whereas the increasing precipitation in August was unfavorable for the growth in Sanjiang Plain.

Several studies have assessed droughts and vegetation considering climatic factors, particularly El Niño-Southern Oscillation (ENSO) at different latitudes. However, there are knowledge gaps in the tropical Andes, a region with high spatiotemporal climatic variability. This research analyzed the relationships between droughts, vegetation, and ENSO from 2001–2015. Meteorological drought was analyzed using the Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 3 and 6 months. Normalized Difference Vegetation Index (NDVI) was used to evaluate vegetation, and ENSO indexes were used as climate drivers. The Wavelet coherence method was used to establish time-frequency relationships. This approach was applied in the Machángara river sub-basin in the Southern Ecuadorian Andes. The results showed significant negative correlations during 2009–2013 between the SPEI and NDVI, with the SPEI6 lagging by nine months and a return period of 1.5 years. ENSO–SPEI presented the highest negative correlations during 2009–2014 and a return period of three years, with ENSO leading the relationship for around fourteen months. ENSO-NDVI showed the highest positive correlations during 2004–2008 and a return period of one year, with the ENSO indexes continually delayed by approximately one month. These results could be a benchmark for developing advanced studies for climate hazards.

Rapid urbanisation, economic growth, and urban spatial development in developing countries, such as Iran, have resulted in tremendous loss of green cover and associated ecological problems. Any effort to achieve sustainable urban development should be supported by recognising and evaluating the ecological health of vegetation cover. This study investigates vegetation cover reduction and changes in the Tehran Metropolis, Iran and identifies the most important factors influencing the observed changes. The aim of this study is two-fold: first, to assess the spatio-temporal changes in vegetation cover in Tehran between 1990 and 2020, and second, to identify the factors contributing to the changes. The Normalised Difference Vegetation Index (NDVI) is used as an indicator of green cover. The spatial and statistical data used in this study were extracted from Landsat satellite imagery and the last approved Master Plan of Tehran (2006). Geographically Weighted Regression (GWR) and geographical modelling methods were employed to analyse vegetation cover in all municipal districts of the Tehran metropolis. The results show that the vegetation density in the Tehran metropolis decreased significantly (from 38,936.80 hectares to 4663.23 hectares) between 1990 and 2020. The expansion of construction lands and the increase of population density were the most significant factors affecting the reduction in vegetation cover in Tehran. In contrast, the growth of industrial units in the urban areas of Tehran had no significant relationship with vegetation cover. The results of this study can help urban planners understand the significant drivers of vegetation loss and identify appropriate interventions to prevent it.

In the current study area (Faridabad, Gurugram, Ghaziabad, and Gautam Buddha Nagar), the aerosol concentration is very high, adversely affecting the environmental conditions and air quality. Investigating the impact of Land Use Land Cover (LULC) on Aerosol Optical Depth (AOD) helps us to develop effective solutions for improving air quality. Hence, the spectral indices derived from LULC ((Normalized difference vegetation index (NDVI), Soil adjusted vegetation index (SAVI), Enhanced vegetation index (EVI), and Normalized difference build-up index (NDBI)) with Moderate Resolution Imaging Spectroradiometer (MODIS) Multiangle Implementation of Atmospheric Correction (MAIAC) high spatial resolution (1 km) AOD from the years 2010–2019 (less to high urbanized period) has been correlated. The current study used remote sensing and Geographical Information System (GIS) techniques to examine changes in LULC in the current study region over the ten years (2010–2019) and the relationship between LULC and AOD. A significant increase in built-up areas (12.18%) and grasslands (51.29%) was observed during 2010–2019, while cropland decreased by 4.42%. A positive correlation between NDBI and SAVI (0.35, 0.27) indicates that built-up soils play an important role in accumulating AOD in a semi-arid region. At the same time, a negative correlation between NDVI and EVI (−0.24, −0.15) indicates the removal of aerosols due to an increase in vegetation. The results indicate that SAVI can play an important role in PM2.5 modeling in semi-arid regions. Based on these findings, urban planners can improve land use management, air quality, and urban planning.

Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China’s temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows based on partial correlation analysis method, the temporal variations of responses of vegetation NDVI to rising air temperature during 1982–2015 in the TC were examined. The results showed that the interannual partial correlation between NDVI and air temperature (RNDVI−T, include RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin, represents the partial correlation between NDVI and Tmean, Tmax, and Tmin, respectively) for the growing season (GS) in a 17−year moving window showed a significant decreasing trend during the last 34 years, mainly due to decreasing RNDVI−T in summer and autumn. The area with a significant decrease of RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin for the GS approximately accounted for 52.36%, 45.63%, and 49.98% of the TC, respectively. For the seasonal patterns of RNDVI−T, the regions with a significant downward trend in all seasons were higher than those with a significant upward trend. We also found a more significant and accelerating decrease of RNDVI−T for warm years compared to cold years, implying a decoupling or even a reverse correlation between NDVI and air temperature with continuous climate warming over the TC. Overall, our study provided evidence that the impact of Tmean, Tmax, and Tmin on vegetation activities exhibited a weakening trend and cautioned using results from interannual time scales to constrain the decadal response of vegetation growth to future global warming.

Groundwater depletion has become a major concern all over the world. Recently, the rapid population growth and need for water and food have placed a massive strain on land and water resources. In this study, groundwater depletion resulting from land-use and climate change was investigated in the Faisalabad district, Pakistan, from 2000 to 2015. A Pearson correlation analysis between climatic parameters and land-use indices with groundwater was conducted to explore the major influencing factors. Interpolation maps of groundwater were generated using the inverse distance weighting interpolation (IDW) method. The Normalized Difference Built-up Index (NDBI) of five-year intervals demonstrated a strong increasing trend, whereas the Normalized Difference Vegetation Index (NDVI) presented a declining trend. The results also indicated a significant declining trend in groundwater levels in the region, with the annual average groundwater level decreasing at a rate of approximately 0.11 m/year. Climatic parameters (i.e., precipitation and temperature) further reveal an insignificant increasing trend estimated using the Mann–Kendall test and Sens’s slope. Overall, spatial analysis results showed a statistically significant positive trend in the groundwater level of the Faisalabad district, where the NDBI ratio is high and the NDVI is low, owing to the extensive extraction of groundwater for domestic and industrial use. These findings may be useful for a better understanding of groundwater depletion in densely populated areas and could also aid in devising safety procedures for sustainable groundwater management.

The East Asian dust storms occur in western and northern China, and southern Mongolia every year, particularly in spring. In this study, we use satellite aerosol products to demonstrate the spatial and temporal variation in aerosol optical depth (AOD) from MODIS, and the absorbing aerosol index (AAI) from TOMS and OMI, over the main dust storm source regions (MDSR), and to investigate their relationship to vegetation coverage (NDVI), soil properties (surface soil moisture content and soil temperature 0–10 cm underground), and climatic factors (surface wind speed, air temperature at 2 m above the ground, and precipitation) in spring for the period of 2000–2021. Compared with dust storm occurrence frequency (DSF) observed at surface stations, MODIS AOD, TOMS AAI, and OMI AAI showed consistent spatial distributions and seasonal variations with DSF in the MDSR, with correlation coefficients of 0.88, 0.55, and 0.88, respectively. The results showed that AOD and AAI over the MDSR decreased during 2000–2005, 2006–2017, and 2000–2021, but increased during 2017–2021.The improvements in vegetation coverage and soil moisture together with favorable climatic factors (the increase in temperature and precipitation and the decrease in surface wind speed) resulted in the decreasing trend of AOD and AAI during 2000–2005, 2006–2017, and the entire period of 2000–2021. Conversely, the increase in surface wind speed, the decrease in temperature and the low soil moisture in 2018 and 2020 were the reasons for the increases in AOD and AAI over the MDSR during 2017–2021. The combination effects of surface wind, temperature, soil moisture, and vegetation coverage would determine DSF, AOD, and AAI, in the end, under global climate change.

Soil moisture (SM) plays an important role in regulating terrestrial–atmospheric water circulation and energy balance. Most of the existing studies have explored the dynamic patterns of SM based on experimental methods. However, the analysis of large-scale regions and long-term SM sequences was limited. Alternatively, satellite remote sensing data is a potential source for SM analysis for large-scale basins. Therefore, the SM data from the European Space Agency (ESA) Climate Change Initiative (CCI) from 2000 to 2015 is used in this paper to analyze the SM spatial-temporal changes in the Yellow River Basin (YRB). Further, the Normalized Difference Vegetation Index (NDVI) and meteorological data are used to explore the relationships between SM and NDVI, precipitation, air temperature, and wind speed, respectively. The results showed that the overall trend of SM in the YRB was decreasing from southeast to northwest during the past 16 years. The upper reaches of the YRB had shown a humid trend, with a value of 0.00047 m3·m−3·year−1, mainly due to the increase in precipitation; there was an obvious drought trend in the middle reaches of the YRB, especially in Shanxi Province and Henan Province, with a value of −0.00030 m3·m−3·year−1, which may be owed to vegetation greening increasing the soil evaporation. Overall, it is determined that the main factors influencing SM changes were NDVI and precipitation, followed by air temperature and wind speed. This study can provide a scientific basis for the spatial-temporal distribution characteristics and attributions of SM in the YRB over a long time series.

We present the development of a physically-based dust source map for the GOCART-AFWA dust module in WRF-Chem model. The new parameterization is based on MODIS-NDVI and an updated emission strength map is computed every 15 days from the latest satellite observations. Modeling simulations for the period April–May 2017 over the Mediterranean, north Africa, and the Middle East are compared with observations of AOD at 31 AERONET stations. The new module is capable of reproducing the dust sources at finer detail. The overall performance of the model is improved, especially for stronger dust episodes with AOD > 0.25. For this threshold the model BIAS decreases from −0.20 to −0.02, the RMSE from 0.38 to 0.30, the Correlation Coefficient improves from 0.21 to 0.47, the fractional gross error (FGE) from 0.62 to 0.40, and the mean fractional bias (MFB) from −0.49 to −0.08. Similar improvement is also found for the lower AOD thresholds (>0.0 and >0.1), especially for the stations in Europe, the Mediterranean, Sahel, the Middle East, and Arabian Peninsula, which are mostly affected by dust transport during the experimental period. An overprediction of AOD, compared to the original dust-source scheme, is found for some stations in the Sahara desert, the Atlantic Ocean, and the Iberian Peninsula. In total, 124 out of the 170 statistical scores that are calculated indicate improvement of model performance.

Global warming is not an expectation but a reality in the “oreums” (common local name for rising, small defunct volcanoes on Jeju Island, Republic of Korea). The oreums exhibit wide biodiversity. However, their ecology is threatened by its associated climate change and their ecological changes have rarely been monitored or recorded. We used three years of Sentinel-2 image data to generate a normalized difference vegetation index (NDVI) map of the Geum-oreum area. We found that the NDVI was highly associated with temperature, implying that Sentinel-2 images could be utilized to monitor the temperature variation in the oreums to assist in planning and preparation to conserve their ecosystems before they are jeopardized. The results indicated that the NDVI maps derived from Sentinel-2 images were highly associated with temperature in Geum-oreum. We expect this method could be applied in other regions to detect temperature variation for ecological management planning in large areas (such as forests).

Alpine grassland in the Qinghai-Tibet Plateau is known to be sensitive to climate change. To quantify the impacts of climate change on alpine ecosystems at small scale, Wulan County in Qinghai Province was taken as the research object, and the relationships between vegetation dynamics and climate changes and the direct and indirect effects of climate factors on vegetation dynamics were analyzed using the methods of ordinary least squares, Pearson correlation analysis and path analysis, on the basis of MOD13A3 data and meteorological data from 2001 to 2020. The results showed that the Normalized Difference Vegetation Index (NDVI) in the growing season of the county and 5 vegetation types showed similar fluctuation processes and relationships with climate factors during the study period. The growing season NDVI (GSN) of shrubland and desert steppe respectively were the highest and lowest. The yearly mean values of GSN over the county ranged from 0.151 to 0.264, and increased extremely significantly with years at a rate of 0.0035 yr−1. Spatially, GSN gradually decreased from northeast to southwest, and 97.2% of the county area showed an increasing trend in GSN. With years, growing season evaporation (GSE) decreased extremely significantly at a rate of 29.6 mm yr−1, while growing season average relative humidity (GSARH) showed a significant increasing trend at a rate of 0.16% yr−1. The correlations and effects of GSE, GSARH, and growing season precipitation (GSP) on vegetation dynamics were weakened in turn. GSE was the main direct effect factor, and the latter two were the indirect effect factors through GSE. The total contribution rates of GSE, GSARH and GSP to vegetation dynamics was about 78.0%. However, growing season average temperature (GSAT) had little effect on vegetation dynamics. This study provides information for understanding the characteristics of vegetation dynamics of alpine grassland in the Qinghai-Tibet Plateau at a small scale.

Depending on the vegetation type, extreme climate and drought events have a greater impact on the end of the season (EOS) and start of the season (SOS). This study investigated the spatial and temporal distribution characteristics of grassland phenology and its responses to seasonal and extreme climate changes in Sichuan Province from 2001 to 2020. Based on the data from 38 meteorological stations in Sichuan Province, this study calculated the 15 extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results showed that SOS was concentrated in mid-March to mid-May (80–140 d), and 61.83% of the area showed a significant advancing trend, with a rate of 0–1.5 d/a. The EOS was concentrated between 270–330 d, from late September to late November, and 71.32% showed a delayed trend. SOS was strongly influenced by the diurnal temperature range (DTR), yearly maximum consecutive five-day precipitation (RX5), and the temperature vegetation dryness index (TVDI), while EOS was most influenced by the yearly minimum daily temperature (TNN), yearly mean temperature (TEMP_MEAN), and TVDI. The RX5 day index showed an overall positive sensitivity coefficient for SOS. TNN index showed a positive sensitivity coefficient for EOS. TVDI showed positive and negative sensitivities for SOS and EOS, respectively. This suggests that extreme climate change, if it causes an increase in vegetation SOS, may also cause an increase in vegetation EOS. This research can provide a scientific basis for developing regional vegetation restoration and disaster prediction strategies in Sichuan Province.

Remote sensing of tornado damage can provide valuable observations for post-event surveys and reconstructions. The tornadoes of 3 March 2019 in the southeastern United States are an ideal opportunity to relate high-resolution satellite imagery of damage with estimated wind speeds from post-event surveys, as well as with the Rankine vortex tornado wind field model. Of the spectral metrics tested, the strongest correlations with survey-estimated wind speeds are found using a Normalized Difference Vegetation Index (NDVI, used as a proxy for vegetation health) difference image and a principal components analysis emphasizing differences in red and blue band reflectance. NDVI-differenced values across the width of the EF-4 Beauregard-Smiths Station, Alabama, tornado path resemble the pattern of maximum ground-relative wind speeds across the width of the Rankine vortex model. Maximum damage sampled using these techniques occurred within 130 m of the tornado vortex center. The findings presented herein establish the utility of widely accessible Sentinel imagery, which is shown to have sufficient spatial resolution to make inferences about the intensity and dynamics of violent tornadoes occurring in vegetated areas.

The within-growing-season correlations (WGSC) and the inter-growing-season correlations (IGSC) are widely used linear correlation analysis methods between vegetation index and climatic factors (such as temperature, precipitation, and so on). The WGSC method usually calculates the linear correlation coefficient between vegetation index and climatic factors of each month in all the growing seasons, for instance, whether vegetation index or temperature had data of 204 months (12 months × 17 years) during 2000–2016 to get the WGSC. The IGSC calculates the linear correlation coefficient between the vegetation index and climatic factors in the same month of each growing season among all the years, for example, only 17 couples’ data of vegetation index and temperature during 2000–2016 were used to get the linear correlation of IGSC. What is the difference between the results of the two methods and why do the results show that difference? Which is the more suitable method for the analysis of the relationship between the vegetation index and climatic conditions? To clarify the difference of the two methods and to explore more about the relationship between the vegetation index and climatic factors, we collected the data of 2000–2016 moderate resolution imaging spectroradiometer (MODIS) 13A1 normalized difference vegetation index (NDVI) and the meteorological data-temperature and precipitation, then calculated WGSC and IGSC between NDVI and the climatic factor in three river-headwater regions of China. The results showed that: (1) As for WGSC, the more of the years included, the higher the correlation coefficient between NDVI and the temperature/precipitation. The correlation coefficient of WGSC is dependent on how many years’ the data were included, and it was increased with the more year’s data included, while the correlation coefficients of IGSC are relatively independent on the amount of the data; (2) the WGSC showed a pseudo linear correlation between NDVI and climatic conditions caused by the accumulation of data amount, while the IGSC can more accurately indicate the impact of climatic factors on vegetation since it did not rely on the data amount.

This paper investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and extracted rainfall in the Global Precipitation Climatology Project (GPCP) in Central Africa between latitudes 15°S and 20°N and longitudes 0°E and 31°E. Monthly NDVI and GPCP datasets for the period 1982–2000 have been used. The Index of Segmentation of Fourier Components (ISFC) has been applied on the NDVI dataset to segment Central Africa into four bioclimatic ecoregions (BCERs). In order to compare the differential response of vegetation growth to rainfall, an analysis of the inter-annual, intra-annual and seasonal variability for each BCER has been carried out, and the correlations between NDVI and rainfall have been assessed. The plot of the annual cycles of both variables revealed a coherent onset, peak and decay, with a time lag of 1 month for almost all the zones, except the zones, semi-desert and steppe, where a season of short and intense rainfall was observed. The correlation coefficients computed between the two variables are relatively high, especially in brush-grass savannah, where they reach up to 0.90 at a time lag of 1 month. The phenological transition points and phases show that the range between the +1 and

Aerodynamic roughness (AR) is an important parameter that influences the momentum and energy exchange between the earth's surface and the atmosphere. In this study, profile wind data observed during the vegetation growing period (April-September) in 2013 and 2014 at the A'rou grassland station, which is in the upstream of the Heihe River Basin (HRB), were used to determine the relationship between the field AR and the Moderate-resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) bi-directional reflectance distribution function (BRDF) R index, the normalized difference vegetation index (NDVI), and a combination of these indices. In addition, the relationship between the average wind speed at a height of 1 m and the field AR is also presented. The results indicate that the MODIS NIR BRDF_R index and the NDVI are both sensitive indicators of the AR over grassland (R2: 0.5228 for NIR BRDF_R; R2: 0.579 for NDVI). Moreover, the combined index shows a significantly increased R2 value of 0.721, which is close to the result inferred from the wind speed (R2: 0.7411). The proposed remote sensing-based combination index (CI) has the potential for use in evaluations of the AR over grasslands during growing season and its sensitivity can reach levels that are comparable to considering the effects of wind speed, which usually requires ground-based observations. [ABSTRACT FROM AUTHOR]