Normalized Difference Vegetation Index Response Research Articles

Asymmetric warming has different impacts on vegetation growth across various vegetation types on the Tibetan Plateau

ABSTRACT Global warming could affect vegetation growth, while land surface temperature has exhibited an asymmetric warming pattern over the past 50 years. The Tibetan Plateau, known as “the roof of the world,” experiences the surface warming almost twice as high as the global average. However, previous research has largely overlooked the impacts of this asymmetrical warming on vegetation growth and the temporal changes in these impacts. In this study, we assess the effects of asymmetric warming on vegetation growth at regional and different vegetation types by using partial correlation analysis, and reveal the temporal changes of impacts strength using time moving window. The results showed that there had been a significant greening trend (1.01 × 10−3 yr−1, p < 0.01) in the Normalized Difference Vegetation Index (NDVI) during the growing season, as well as significant warming trends in both maximum temperatures (Tmax, 0.0354°C yr−1, p < 0.01) and minimum temperatures (Tmin, 0.0333°C yr−1, p < 0.01) during 2000–2021. Under the background of asymmetric warming, the grassland showed stronger impacts of Tmin than Tmax on NDVI, while the opposite effects were observed for forests. Over time, the response of NDVI to Tmax and Tmin has intensified. This study highlights importance of asymmetric warming in the projection of climate change on vegetation in similarly vulnerable ecosystems worldwide.

The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection.

Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: Insights for sustainable management and climate resilience

Understanding the intricate relationship between climate variables and the Normalized Difference Vegetation Index (NDVI) is essential for effective ecosystem management. This study focuses on the spatiotemporal dynamics of NDVI and its interaction with climate variables in the ecologically diverse Khyber Pakhtunkhwa (KPK) Province, Pakistan, from 2000 to 2022. The research methodology involves analyzing satellite images and meteorological datasets to examine NDVI and surface latent heat flux (SHF), total precipitation (TPP), temperature (T), soil temperature (ST), and total pressure (TP). KPK Province's ecological significance and complex climate-vegetation interactions drive the selection of this study area. The study uses multiple linear regression analysis to investigate how T, TPP, SHF, and TP influence NDVI. The Mann-Kendall test detects trends, with Sen's slope estimator quantifying trend magnitudes. Additionally, correlation coefficients provide insights into long-term changes and association strengths. The findings highlight a consistent upward trend in mean NDVI over the 23 years, revealing an overall increase in NDVI, particularly in vegetation-dense areas where it rose from 0.27 to 0.32. The research showed an annual growth rate of 0.84% in the entire area, with specific vegetated zones exhibiting a slightly lower rate of 0.80%. However, the average yearly increase in NDVI is higher in vegetation-specific zones (0.00237) compared to the whole area (0.00151). This increase in NDVI occurs alongside a statistically significant decrease in SHF and PPT, suggesting a complex adaptation of vegetation to changing climate conditions in the KPK Province. In contrast, SHF exhibits a statistically significant negative slope of −5.952e-06 (p < 0.05), indicating a pronounced downward trend. Similarly, Sen's slope estimate for precipitation demonstrates a significant negative trend of −0.0001 (p < 0.05), showing diminishing precipitation. The study uncovers intricate linkages between climate variables and vegetation dynamics within KPK Province. These insights have far-reaching implications, guiding decision-making in land management, conservation efforts, and global climate resilience strategies. Ultimately, the research underscores the critical role of data-driven approaches in shaping a greener and more sustainable future.

Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels

Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.

Resilience and response: Unveiling the impacts of extreme droughts on forests through integrated dendrochronological and remote sensing analyses

Extreme droughts are anticipated to have detrimental impacts on forest ecosystems, especially in water-limited regions, due to the influence of climate change. However, considerable uncertainty remains regarding the patterns in species-specific responses to extreme droughts. Here, we conducted a study integrating dendrochronology and remote sensing methods to investigate the mosaic-distributed maple-oak (native) natural forests and poplar plantations (introduced) in the Horqin Sandy Land, Northeast China. We assessed the impacts of extreme droughts on tree performances by measuring interannual variations in radial growth and vegetation index. The results showed that precipitation and self-calibrated palmer drought severity index (scPDSI) are the major factors influencing tree-ring width index (RWI) and normalized difference vegetation index (NDVI). The severe droughts between 2000 and 2004 resulted in reduced RWI in the three studied tree species as well as led to NDVI reductions in both the maple-oak natural forests and the poplar plantations. The RWI reached the nadir during the 2000–2004 severe droughts and remained at low levels two years after the severe drought, creating a legacy effect. In contrast to the lack of significant correlation between RWI and scPDSI, NDVI exhibited a significant positive correlation with scPDSI indicating the greater sensitivity of canopy performance to droughts than radial growth. Furthermore, interspecific differences in RWI and NDVI responses were observed, with the fast-growing poplar species experiencing a more significant RWI decrease and more negative NDVI anomaly during severe droughts than native species, highlighting the species-specific trade-offs between drought resilience and growth rate. This study emphasizes the importance of combining tree-level radial growth with landscape-scale canopy remote sensing to understand forest resilience and response. Our study improves our understanding of forest responses to extreme drought and highlights species differences in climate responses, offering crucial insights for optimizing species selection in sustainable afforestation and forest management in water-limited regions under the influence of climate change.

Quantifying the influence of tree species richness on community drought resistance using drone-derived NDVI and ground-based measures of Plant Area Index and leaf chlorophyll in a young tree diversity experiment

Tree diversity may buffer the negative impact of drought events according to the diversity insurance hypothesis. During the extreme pan-European drought of 2018, we tested whether tree species richness modulated drought impacts on communities of a young tree diversity experiment in Freiburg, Germany. We utilized drone-based hyperspectral images to capture early symptoms of drought stress indicated by variation in Normalized Difference Vegetation Index (NDVI), and ground-based measures that are indicative of tree canopy function, including Plant Area Index (PAI) and leaf chlorophyll content (Chl). Measures were taken the fifth growing season after planting on six broadleaved and six conifer species originating from Europe and North America in monocultures, two-, four- and six-species mixtures. NDVI decreased successively in response to the summer drought. In contrast to our expectation, tree species richness did not reduce declines in NDVI at the plot level. NDVI values were generally lower in six-species mixtures, compared to monocultures or two-species mixtures, which may be indicative of antagonistic tree species interactions in mixtures of high diversity. Changes in PAI in response to the drought were similar to changes in NDVI; however, modeled differences in PAI between mixtures were not statistically significant. Chl initially decreased, but recovered within a period of two months following the drought. Remotely sensed NDVI facilitated monitoring of the drought response of our large field experiment and could aid in monitoring canopy health in response to extreme drought events. Our observations suggest that NDVI responses are likely more strongly related to leaf shedding within tree canopies than decreases in Chl. Tree stands with a high abundance of deciduous trees may therefore be especially sensitive in triggering NDVI changes in response to drought during the stages leading up to tree mortality. Future studies of drought-related canopy responses should include monitoring of leaf shedding as well as the Chl of shed leaves.

Ecosystem stability of temperate grasslands in response to variability of hydrological conditions

AbstractThe relationship between biodiversity and the stability of ecosystem functioning over time has been widely studied. The current global context has refloated this topic for biodiversity's role in buffering the effects of different disturbances. In general, the results of these studies show that ecosystem functioning is more stable over time in more diverse systems. However, these results are derived from empirical research on small‐scale studies, where species and disturbances conditions are manipulated. In this work, we used climate and floristic information data obtained from surveys over an extended period on Flooding Pampa grasslands (Argentina) with a remotely sensed indicator of the stability of net primary productivity at a regional scale over a broad temporal range to evaluate the relationship between species diversity and the stability of ecosystem functioning under different water conditions. We found a close correlation between Normalized Difference Vegetation Index responses of natural grasslands and climate variability in the study area. Besides, grasslands with higher species richness and diversity showed greater stability in ecosystem functioning at different water conditions. The results obtained could be relevant in natural resource management for the close relationship between diversity–stability in a local and regional productive context characterized by a simplified landscape of space and time.

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

BackgroundMara River Basin is an ecologically fragile area in East Africa, with a pattern of alternating wet and dry seasons shaped by periodic precipitation. Considering the regional biological traits and climatic change, the vegetation's response to seasonal variation is complicated and frequently characterized by time lags. This study analyzed the variation of the Normalized Difference Vegetation Index (NDVI) and investigated its time lag to precipitation at the monthly scale. NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.ResultsThe results showed that the Anomaly Vegetation Index could identify low precipitation in 2006, 2009, and 2017. The NDVI showed an increasing trend in 75% of areas of the basin, while showed a decreased significance in 3.5% of areas, mainly in savannas. As to the time lag, the 1-month lag effect dominated most months, and the spatiotemporal disparities were noticeable. Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days. Based on the time distribution of NDVI characteristic peaks, the average time lag was 35.5 days and increased with the range of seasons.ConclusionsThe findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020, while the trends were most obvious in the downstream related to human activities. The results could reflect the time lag of NDVI response to precipitation, and the 1-month lag effect dominated in most months with spatial heterogeneity. Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.

Vegetation Dynamics and Its Response to Extreme Climate on the Inner Mongolian Plateau during 1982–2020

The impact of extreme climate change on terrestrial ecosystems continues to intensify. This study was conducted to understand extreme climate–vegetation interactions under exacerbated frequency, severity, and duration of extreme climatic events. The Inner Mongolian Plateau (IMP) was selected due to its sensitive natural location, which is particularly vulnerable to climate change. Based on the Normalized Difference Vegetation Index (NDVI) and daily meteorological station data from 1982 to 2020, changes in the patterns of vegetation and extreme climate in the three ecological zones (forest, steppe, and desert steppe) of the IMP were identified. Furthermore, the effects of extreme climate on vegetation were quantified using correlation analysis and a geographical detector. The results showed that the annual NDVI of 95.1%, 50.6%, and 19.5% of the area increased significantly in the forest, steppe, and desert steppe, respectively. The Tx90p (warm days) and Tn90p (warm nights) increased significantly at the rate of 0.21 and 0.235 day·yr−1, respectively, while the Tx10p (cold days) and Tn10p (cold nights) showed a significantly decreasing trend at the rate of −0.105 and −0.117 day·yr−1. An extreme warming phenomenon was observed in all extreme temperature indices on the IMP. The results of both the correlation analysis and factor detector indicated that extreme temperature intensity and frequency greatly affected forest vegetation. In contrast, extreme precipitation intensity and frequency were relatively more important to the vegetation of the desert steppe. The lag in NDVI response to extreme temperature intensity was not less than three months in the IMP; however, extreme precipitation intensity exhibited a two-month time lag in the NDVI. This study can improve our understanding of extreme climate–vegetation interactions, provide theoretical support for disaster mitigation, and aid in understanding the ecological environment of the IMP.

Assessing the interrelation between NDVI and climate dependent variables by using granger causality test and vector auto-regressive neural network model

Changes in ecosystem structure and function can be revealed by examining the prevailing patterns in vegetation growth and the forces that shape those patterns. The mechanism of ecosystem behaviour may be better understood if the trend of vegetation change and its sensitivity to climatic variation are well understood. The interaction of vegetation and climatic factors (it's driving variables) is non-linear in behaviour and affected by time lag and time accumulation. Jharkhand has a typical plateau in eastern India, having a mixed climate (arid and semi-arid), taken as the study area, and the spatiotemporal distributions of the normalized difference vegetation index (NDVI) were explored with interaction driving factors. This study investigated the time-lag and time-accumulation effects of the NDVI response to climate factors Evapotranspiration (ET), Land Surface Temperature (LST), Potential Evapotranspiration (PET), Precipitation (PREC), and Soil Moisture (SM) and identified the primary controlling factors that affect the vegetation dynamics. The observations indicate that the correlation between NDVI and summer LST (- 0.838) was discovered to be greater than the correlation of NDVI with SM (r = 0.90) and PREC (r = 0.751), showing NDVI as more sensitive to LST when comparing to SM, and PREC, while PET exhibits the significant positive correlation (r = −0.751) with the NDVI in autumn during the studied duration. Higher NDVI values were seen during the monsoon (0.54 ± 0.12), which is correlated with a decrease in the monsoon LST (25.8 ± 0.20), followed by the winter (0.47 ± 0.13), summer (0.33 ± 0.18), and fall (0.37 ± 0.04). Vegetation growth is influenced by both the time-lag and time-accumulation effects of temperature and the time-accumulation impact of precipitation. Regarding the climate-vegetation response mechanism, the application of the Granger Causality (GC) Test and GC-based Vector Auto-Regressive Neural Network (VARNN) Model test reveals that the 0–2 month optimum time lag effect is prevalent in the study area. In addition, the LST and SM have a more prominent stimulating influence on plant growth in the study region than precipitation. The above findings highlight the need to effectively monitor vegetation dynamics under environmental changes by considering the temporal impacts of vegetation response to climate when the current climate models research vegetation-climate interactions.

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen’s slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales.

Regulation of NDVI and ET negative responses to increased atmospheric vapor pressure deficit by water availability in global drylands

Atmospheric vapor pressure deficit (VPD, indicative of atmospheric water conditions) has been identified as a major driver of global vegetation dynamics. Drylands, including deserts, temperate grasslands, savannas, and dry forests, are more sensitive to water conditions and affect carbon, nitrogen, and water cycles. However, our knowledge is limited on the way increasing VPD affects vegetation growth and evapotranspiration (ET) in global drylands. In this study, we used long-term satellite datasets combined with multiple statistical analyses to examine the relationship between the satellite-derived normalized difference vegetation index (NDVI), a proxy for vegetation growth, and ET to VPD across global drylands. We found that significant decreases in NDVI and ET predominantly influenced the NDVI (RVPD − NDVI) and ET (RVPD − ET) responses to VPD in both the savannas and dry forests of South American, African, and Australian savannas and dry forests, as well as in temperate grasslands (e.g., Eurasian steppes and American prairies). Notably, more than 60% of global drylands exhibited significantly negative RVPD − NDVI and RVPD − ET values. In contrast, the percentage of significantly negative RVPD − NDVI and RVPD − ET decreased to &amp;lt;10% in cold drylands (&amp;gt;60° N). In predominantly warm drylands (60° N~60° S), negative VPD effects were significantly and positively regulated by soil water availability, as determined by multiple linear regression models. However, these significant regulatory effects were not observed in cold drylands. Moving-window analyses further revealed that temporal changes in RVPD − NDVI and RVPD − ET were positively correlated with changes in the Standardized Precipitation Evapotranspiration Index (SPEI). In warm drylands, areas with increasing RVPD − NDVI and RVPD − ET over time showed an increasing trend in the SPEI, whereas areas with a decreasing SPEI showed a negative trend in RVPD − NDVI and RVPD − ET values over time. Given the increasing atmospheric dryness due to climate change, this study highlighted the importance of re-evaluating the representation of the role of water availability in driving the response of the carbon-water cycle to increased VPD across global drylands.

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Normalized difference vegetation index (NDVI) is the most widely used factor in the growth status of vegetation, and improving the prediction of NDVI is crucial to the advancement of regional ecology. In this study, a novel NDVI forecasting model was developed by combining time series decomposition (TSD), convolutional neural networks (CNN) and long short-term memory (LSTM). Two forecasting models of climatic factors and four NDVI forecasting models were developed to validate the performance of the TSD-CNN-LSTM model and investigate the NDVI's response to climatic factors. Results indicate that the TSD-CNN-LSTM model has the best prediction performance across all series, with the RMSE, NSE and MAE of NDVI prediction being 0.0573, 0.9617 and 0.0447, respectively. Furthermore, the TP-N (Temperature & Precipitation-NDVI) model has a greater effect than the T-N (Temperature-NDVI) and P-N (Precipitation-NDVI) models, according to the climatic factors-based NDVI forecasting model. Based on the results of the correlation analysis, it can be concluded that changes in NDVI are driven by a combination of temperature and precipitation, with temperature playing the most significant role. The preceding findings serve as a helpful reference and guide for studying vegetation growth in response to climate changes.

Variability in Tree-ring Width and NDVI Responses to Climate at a Landscape Level

Inter-annual climatically driven growth variability of above-ground biomass compartments (for example, tree stems and foliage) controls the intensity of carbon sequestration into forest ecosystems. However, understanding the differences between the climatic response of stem and foliage at the landscape level is limited. In this study, we examined the climate-growth response of stem and leaf biomass and their relationship for Pinus sylvestris (PISY) and Picea abies (PCAB) in topographically complex landscapes. We used tree-ring width chronologies and time series of the normalized difference vegetation index (NDVI) derived from high-resolution Landsat scenes as proxies for stem and leaf biomass, respectively. We then compared growth variability and climate-growth relationships of both biomass proxies between topographical categories. Our results show that the responses of tree rings to climate differ significantly from those found in NDVI, with the stronger climatic signal observed in tree rings. Topography had distinct but species-specific effects: At moisture-limited PISY stands, stem biomass (tree rings) was strongly topographically driven, and leaf biomass (NDVI) was relatively insensitive to topographic variability. In landscapes close to the climatic optimum of PCAB, the relationship between stem and leaf biomass was weak, and their correlations with climate were often inverse, with no significant effects of topography. Different climatic signals from NDVI and tree rings suggest that the response of canopy and stem growth to climate change might be decoupled. Furthermore, our results hint toward different prioritizations of biomass allocation in trees under stressful conditions which might change allometric relationships between individual tree compartments in the long term.

Climate Change and Diurnal Warming: Impacts on the Growth of Different Vegetation Types in the North–South Transition Zone of China

Since land use/cover change profoundly impacts climate change and global warming has become an irreversible trend in the Anthropocene, there have been numerous global studies on the impact of climate change on vegetation growth (VG). However, the effects of climate extremes on the growth and direction of various vegetation types need to be better investigated, especially in the climate transition zones. In this paper, we examined the effect of diurnal warming on the growth of various types of vegetation in China’s north–south transition zone. Based on the daily observation data of 92 meteorological stations in the Qinling-Daba (Qinba) mountainous area from 1982 to 2015, coupled with the Normalized Difference Vegetation Index (NDVI) and data on the type of vegetation. This research examined the temporal changes in the highest and lowest temperatures during the last 33 years using trend analysis. Second-order correlation analysis was used to investigate vegetation NDVI response characteristics to diurnal warming and to examine the effect of diurnal warming on the growth of different vegetation types. Our results showed that maximum temperature (Tmax) and minimum temperature (Tmin) showed an obvious upward trend, with the daytime temperature increase rate 1.2 times that at night, but failing the t-test. In addition, diurnal warming promoted vegetation growth, with NDVI associated positively correlated with Tmax at approximately 91.2% of the sites and 3492 rasters and with Tmin at roughly 53.25% of the sites and 2864 rasters. Spatial significance analysis showed an apparent difference, but few areas passed the t-test. Furthermore, daytime warming enhanced the growth of grasses, shrubs, deciduous broad-leaved forests, crops, and conifers, while the effect of nighttime warming on VG had a positive effect only on the growth of evergreen broad-leaved forest vegetation. These findings reveal the mechanisms of the impact of climate extremes on VG under global change, particularly the extent to which different vegetation types in climatic transitional zones respond to climate extremes.

Exploring the Ability of Solar-Induced Chlorophyll Fluorescence for Drought Monitoring Based on an Intelligent Irrigation Control System

Drought is one of the most devastating disasters and a serious constraint on agricultural development. The reflectance-based vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used for drought monitoring, but there is a lag in the response of VIs to the changes of photosynthesis induced by drought. Solar-induced chlorophyll fluorescence (SIF) is closely related to photosynthesis of vegetation and can capture changes induced by drought timely. This study investigated the capability of SIF for drought monitoring. An intelligent irrigation control system (IICS) utilizing the Internet of Things was designed and constructed. The soil moisture of the experiment plots was controlled at 60–80% (well-watered, T1), 50–60% (mild water stress, T2), 40–50% (moderate water stress, T3) and 30–40% (severe water stress, T4) of the field water capacity using the IICS based on data collected by soil moisture sensors. Meanwhile, SIF, NDVI, Normalized Difference Red Edge (NDRE) and Optimized Soil Adjusted Vegetation Index (OSAVI) were collected for a long time series using an automated spectral monitoring system. The differences in the responses of SIF, NDVI, NDRE and OSAVI to different drought intensities were fully analyzed. This study illustrates that the IICS can realize precise irrigation management strategies and the construction of regulated deficit irrigation treatments. SIF significantly decreased under mild stress, while NDVI, NDRE and OSAVI only significantly decreased under moderate and severe stress, indicating that SIF is more sensitive to drought. This study demonstrates the excellent ability of SIF for drought monitoring and lays the foundation for the future application of SIF in agricultural drought monitoring.

Vegetation dynamics and their responses to climate factors in the Qinghai-Tibet plateau: insights from the terrestrial ecosystems

This study examined the structural changes of vegetation ecosystems, multi-scale cyclical variations of normalized difference vegetation index (NDVI), as well as their responses to climate factors in the QTP during 2001–2018. Results showed the annual increment of NDVI in forest ecosystem was the largest, followed by grassland, and the increment of NDVI in desert ecosystem was the smallest. Vegetation activities showed a steady upward trend in forest, grassland and desert ecosystems, a fluctuating upward trend in the wetland ecosystem, and a slow downward trend in the farmland ecosystem. The intra-annual cycles of vegetation activities in these ecosystems were between 28 and 42 days, and only the forest ecosystem had a clear inter-annual cycle of 2.1 years. The responses of NDVI to precipitation and temperature in grassland, wetland, and farmland ecosystems all had a lag time of 16 days, while those in desert and forest ecosystems were weak.

Nonlinear effects of thermokarst lakes on peripheral vegetation greenness across the Qinghai-Tibet Plateau using stable isotopes and satellite detection

Thermokarst lakes (TLs) widespread in thawing permafrost can degrade vegetation through thermomechanical erosion and waterlogging. However, whether TLs change water sources and alleviate water stress for plants is unknown, and previous knowledge about regional TL effects on surrounding vegetation greenness is rare. Here, we synthesized field investigations, stable isotopes, and remote sensing images from an unmanned aerial vehicle and Sentinel-2 to determine the effects of over 160,000 TLs on their surrounding growing-season normalized difference vegetation index (NDVI) in the Qinghai-Tibet Plateau. The results are as follows: 1) With the largest water source shifting from 0 to 10 cm soil water to lake water, TL-affected plants can grow better than TL-unaffected plants, which is associated with soil texture and the development stage of the lake. 2) Overall, the median affecting distances of TLs on surrounding surface moisture (tasseled cap wetness, TCW) and NDVI were 50 m with their 25th–75th percentiles of 40–70 m and 40–60 m, respectively; compared with the unaffected areas, TLs increased peripheral TCW but reduced NDVI with their median change rates of 19.84% and −10.42%, respectively. 3) However, the nonlinear response of NDVI to the TCW gradient was dominant, which was featured by a local peak NDVI due to the coexistence of the physical destruction and improved water availability, and both TCW and NDVI at the peak area were explicitly larger than those at the unaffected area. 4) Along southeast-northwest environmental gradients (topographic, climatic, and edaphic factors), the affected range and degradation ratio of NDVI were greater under drier climate, larger lake area, sand-richer soil, fewer soil nutrients, and worse vegetation type (e.g., alpine desert). Briefly, the net adverse consequence on NDVI suggests that TLs primarily represent permafrost-supported ecosystems deteriorating, but the clear nonlinear effects advance the knowledge of this landscape; additionally, these local vegetation changes can help interpret the regional greening/browning and promote the research on the biogeochemical interaction among TL, soil, and plants under climate warming and permafrost thawing.

Explicating the responses of NDVI and GDP to the poverty alleviation policy in poverty areas of China in the 21st century.

The economy in the poverty-stricken areas of China has grown rapidly in response to poverty alleviation policies in the 21st century. To explicate the response of the eco-environment to rapid economic growth in the 14 contiguous areas of dire poverty in China, we developed a method of evaluating the impact of poverty alleviation policies on ecological health. Based on the yearly data of gross domestic product (GDP) per capita and normalized difference vegetation index (NDVI) from 2000 to 2019, the dynamic changes in NDVI and GDP were calculated, and the development patterns in the 14 contiguous areas of dire poverty were evaluated and classified. The results show that both annual GDP per capita and average annual NDVI exhibited an increasing trend, increasing by 43.81% and 0.84% per year, respectively. The development of the 14 contiguous areas of dire poverty all presented a coordinated and sustainable (A) development pattern during the period from 2000 to 2019. The consistency of economic and ecological health development between 2000 and 2013 was less than that between 2014 and 2019. Moreover, the result indicates that it is necessary to make timely adjustments to poverty alleviation strategies based on the positive consistency between economic growth and ecological health.

Variation characteristics of normalized difference vegetation index in Northwestern Sichuan Plateau and its response to extreme climate during 2001-2020

The northwestern Sichuan Plateau is a typical eco-climate sensitive area, where vegetation condition is closely related to climate change. We used the MODIS-NDVI and the meteorological data during 2001-2020 to investigate the change trend of normalized difference vegetation index (NDVI) and the mechanism underlying its responses to climate factors in the growing season of northwestern Sichuan Plateau by using the methods of maximum value composite, geodetector model, trend analysis, and correlation analysis. The results showed that vegetation coverage in northwestern Sichuan Plateau was overall good during the study period. 86.8% of the regional vegetation was stable, 12.6% of the regional NDVI was weakly and continuously increasing, and 0.6% of the regional NDVI was decreasing. The ecological environment of the whole region exhibited a steady and good development trend. The vegetation coverage in the study area exhibited apparent spatial variation with a general tendency of increase from southwest to northeast, as well as obvious variation with elevation. The NDVI rose with elevation below 1350 m, varied slightly from 1350 to 3650 m, dropped from 3650 to 5900 m, with a rapid drop between 4750 to 5900 m. There was almost no vegetation above 5900 m. The NDVI of northwestern Sichuan Plateau was affected by the interactions of natural factors. Thermal factors were the dominant climate factors, including monthly maximum value of daily maximum temperature, monthly minimum value of daily minimum temperature, growing season length, annual mean temperature, mean temperature over the growing season. All these factors were positively correlated with NDVI excepted for monthly maximum value of daily maximum temperature. The response of NDVI to temperature index was higher than that of precipitation index. Under the background of climate warming, extreme temperature warming index played a major role in promoting the growth and improvement of vegetation in northwestern Sichuan Plateau, especially in high-altitude areas.