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

Xinjiang, an arid region of China, has experienced a substantial warming–wetting trend over the past five decades. However, climate change has affected vegetation growth/greening in arid Central Asia in unexpected ways due to complex ecological effects. We found a significant greening trend (consistent increase in the normalized difference vegetation index or NDVI) from 1982 to 1996, during the growing season; however, the NDVI consequently decreased and plateaued from 1997 to 2015, especially in naturally vegetated regions. Atmospheric vapor pressure deficit (VPD) is a critical driver of vegetation growth, is a direct measure of atmospheric aridity, and has increased sharply in recent decades. A partial correlation analysis indicated a significant relationship between growing season NDVI and VPD from 1997 to 2015. This implies that decreased VPD corresponds to increasing NDVI, and increasing VPD corresponds to a decrease and plateauing in the NDVI trend. Using the partial derivative equation method, our results suggest that the trend in growing season NDVI was affected primarily by increasing VPD (contributing 87.57%) from 1997 to 2015, especially in the grassland and desert biomes. Rising temperatures lead to a greater VPD, resulting in exacerbated evaporative water loss. Soil drought and atmospheric aridity limit plant stomatal conductance and could effectively lead to a decrease in the greening trend and increased vegetation mortality in arid Xinjiang. Our results emphasize the importance of VPD as a limiting factor of greening trends in arid regions. The influence of VPD on vegetation growth should be considered when evaluating arid ecosystem functioning under global warming. [ABSTRACT FROM AUTHOR]

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

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

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

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

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