Enhanced Vegetation Index Data Research Articles

Site-specific apparent optimum air temperature for vegetation photosynthesis across the globe

The apparent optimum air temperature for vegetation photosynthesis (Topt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m2/day). To date, most models use biome-specific Topt (Topt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific Topt (Topt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the Topt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global Topt-site data products from 2000 to 2019. The Topt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used Topt-biome. Therefore, using this global dataset of Topt-site estimates might significantly affect GPP simulation in current ecosystem models.

Consistency in Verreaux's sifaka home range and core area size despite seasonal variation in resource availability as assessed by Enhanced Vegetation Index (EVI).

Primates are adept at dealing with fluctuating availability of resources and display a range of responses to minimize the effects of food scarcity. An important component of primate conservation is to understand how primates adapt their foraging and ranging patterns in response to fluctuating food resources. Animals optimize resource acquisition within the home range through the selection of resource-bearing patches and choose between contrasting foraging strategies (resource-maximizing vs. area-minimizing). Our study aimed to characterize the foraging strategy of a folivorous primate, Verreaux's sifaka (Propithecus verreauxi), by evaluating whether group home range size varied between peak and lean leaf seasons within a seasonally dry tropical forest in Madagascar. We hypothesized that Verreaux's sifaka used the resource maximization strategy to select high-value resource patches so that during periods of resource depression, the home range area did not significantly change in size. We characterized resource availability (i.e., primary productivity) by season at Kirindy Mitea National Park using remotely-sensed Enhanced Vegetation Index data. We calculated group home ranges using 10 years of focal animal sampling data collected on eight groups using both 95% and 50% kernel density estimation. We used area accumulation curves to ensure each group had an adequate number of locations to reach seasonal home range asymptotes. Neither 95% home ranges nor 50% core areas differed across peak and lean leaf resource seasons, supporting the hypothesis that Verreaux's sifaka use a resource maximization strategy. With a better understanding of animal space use strategies, managers can model anticipated changes under environmental and/or anthropogenic resource depression scenarios. These findings demonstrate the value of long-term data for characterizing and understanding foraging and ranging patterns. We also illustrate the benefits of using satellite data for characterizing food resources for folivorous primates.

Examination of the Effects of Climate Change on Türkiye through the Google Earth Engine Platform

The objective of this research is to assess the effects of climate change on Türkiye by utilizing data catalogues provided by the Google Earth Engine (GEE) cloud-computing platform. The utilized data catalogues encompassed precipitation, Land Surface Temperature (LST), EvapoTranspiration (ET), Potential EvapoTranspiration (PET), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Condition Index (VCI), and Forest Area Loss (FAL). Data spanning the years 2001-2022 were collected, and analyses were conducted separately for seven geographical regions of Türkiye on both an annual and seasonal basis. Initially, trend analysis techniques were applied to the individual data sets, followed by an examination of correlations among them. Notably, significant decreasing and increasing trends were observed in annual precipitation and LST data in the Eastern Anatolia region, respectively. Furthermore, a significant increasing trend was identified in annual ET data across all regions except Eastern Anatolia. Conversely, significant increasing trends were noted in annual PET data in Eastern Anatolia and the Aegean regions. Additionally, significant increasing trends were discerned in annual NDVI, EVI, and VCI data across all regions. Experiments revealed that the ET exhibited robust correlations with the NDVI (0.77), EVI (0.79) and VCI (0.81). Furthermore, the NDVI demonstrated strong correlations with EVI (0.99) and VCI (0.96).

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs.

Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology

IntroductionForest and grassland are the two main carbon-collecting terrestrial ecosystems, and detecting their solar-induced chlorophyll fluorescence (SIF) enables evaluation of their photosynthetic intensity and carbon-collecting capacity. Since SIF that is retrieved directly from satellite observations suffers from low spatial resolution, discontinuity, or low temporal resolution, some vegetation indexes (VIs) and meteorological factors are used as predictors to reconstruct SIF products. Yet, unlike VIs, certain meteorological factors feature a relatively low space resolution and their observations are not always accessible. This study aimed to explore the potential of reconstructing SIF from fewer predictors whose high-resolution observations are easily accessible.MethodsA total of six forest and grassland regions across low, mid, and high latitudes were selected, and the commonly used predictors-normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST)—were compared for their correlation with SIF. Results show that the combination of EVI and LST is more strongly correlated with SIF, but each contributed differently to SIF at differing growth stages of forest and grassland. Accordingly, we proposed the idea of a combined sampling approach that considers both location and phenological phase, to explore the extent to which time and space coverage samples' span could enlarge the disparity of EVI data in particular regions at specific growth stages. To do that, three kinds of sample combination methods were proposed: monthly regression at a global scale, seasonal regression at a regional scale, and monthly regression at a regional scale. Following this, Sentinel-3 EVI and MODIS LST data were used to reconstruct 500 m SIF in the six regions by implementing the proposed methodology.Results and discussionThese results showed that the R2 values were ≥0.90 between the reconstructed SIF and MODIS GPP (gross primary productivity), 0.70 with GOME-2 SIF and 0.77 with GOSIF, thus proving the proposed methodology could produce reliable results for reconstruction of 500 m SIF. This proposed approach, which bypasses dependence of traditional SIF reconstruction model on numerous predictors not easy to obtain, can serve as a better option for more efficient and accurate high-resolution SIF reconstructions in the future.

Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study

Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study’s findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping.

Variations in Phenology Identification Strategies across the Mongolian Plateau Using Multiple Data Sources and Methods

Satellite data and algorithms directly affect the accuracy of phenological estimation; therefore, it is necessary to compare and verify existing phenological models to identify the optimal combination of data and algorithms across the Mongolian Plateau (MP). This study used five phenology fitting algorithms—double logistic (DL) and polynomial fitting (Poly) combined with the dynamic threshold method at thresholds of 35% and 50% (DL-G35, DL-G50, Poly-G35, and Poly-G50) and DL combined with the cumulative curvature extreme value method (DL-CUM)—and two data types—the enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF)—to identify the start (SOS), peak (POS), and end (EOS) of the growing season in alpine meadow (ALM), desert steppe (DRS), forest vegetation (FV), meadow grassland (MEG), and typical grassland (TYG) of the MP. The optimal methods for identifying the SOS, POS, and EOS of typical grassland areas were Poly-G50 (NSE = 0.12, Pbias = 0.22%), DL-G35/50 (NSE = −0.01, Pbias = −0.06%), and Poly-G35 (NSE = 0.02, Pbias = 0.08%), respectively, based on SIF data. The best methods for identifying the SOS, POS, and EOS of desert steppe areas were Poly-G35 (NSE = −0.27, Pbias = −1.49%), Poly-G35/50 (NSE = −0.58, Pbias = −1.39%), and Poly-G35 (NSE = 0.29, Pbias = −0.61%), respectively, based on EVI data. The data source explained most of the differences in phenological estimates. The accuracy of polynomial fitting was significantly greater than that of the DL method, while all methods were better at identifying SOS and POS than they were at identifying EOS. Our findings can help to facilitate the establishment of a phenological estimation system suitable for the Mongolian Plateau and improve the observation methods of vegetation phenology.

Magnitude and direction of green-up date in response to drought depend on background climate over Mongolian grassland

Increasing drought is one major consequence of ongoing global climate change and is expected to cause significant changes in vegetation phenology, especially for naturally vulnerable ecosystems such as grassland. However, the linkage between the response characteristic of green-up date (GUD) to drought and background climate remains largely unknown. Here, we focused on how the GUD of Mongolian grassland responds to extreme drought events (EDE). We first extracted the GUD from the MODIS Enhanced Vegetation Index data during 2001–2020 and identified the preseason EDE using the standardized precipitation evapotranspiration index data. Subsequently, we quantified the response of GUD to preseason EDE (DGUD) in each pixel as the difference in GUD between drought and normal years. The effect of 12 factors on DGUD was analyzed using the random forest algorithm. The results showed that the GUD under EDE may delay or advance by > 20 days compared to normal years. For the regions with mean annual temperature > −2 °C, the GUD was delayed under EDE due to the dominant role of water restriction on GUD, while the GUD was advanced under EDE in colder areas due to the warmer temperature during drought. However, the magnitude of delay in GUD under drought was greater in regions with less precipitation and more severe droughts. Our results could help to develop appropriate management strategies to mitigate the impacts of drought on grasslands.

Impact of Climate Change on Alpine Phenology over the Qinghai–Tibet Plateau from 1981 to 2020

Climate change is a major driver of cyclical and seasonal changes in alpine phenology. This study investigated how climate change affects vegetation phenology’s spatial and temporal responses on the Qinghai–Tibet plateau (QTP) from 1981 to 2020. We used the daily two-band enhanced vegetation index (EVI2) at a 0.05° spatial resolution for 1981–2014, 16-day moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index data for 2000–2020 at 250 m spatial resolution, and climate records from 106 meteorological stations from 1981 to 2020 to construct linear regression models and Mann–Kendall point test to understand the changes on QTP vegetation in response to climate change. We found that the temperature in April, July, and September controls vegetation growth, and spring precipitation (p < 0.05) influences the length of the growing period, with a partial correlation coefficient of −0.69. Over the past 40 years, temperature and precipitation changes on the QTP have not shown abrupt shifts despite the increasingly warm and dry spring climate. We observed a meridional distribution trend in the correlation between precipitation and alpine vegetation greening, browning and the length of the growing period. In regions experiencing strong warming, vegetation growth was hindered by a lack of precipitation. We conclude that climatic factors alone cannot fully explain the changing trends in vegetation phenology across the QTP.

Revisiting statistical spectral-agrometeorological wheat yield models for Punjab using MODIS EVI and NCMRWF re-analysis temperature data

Use of space-based spectral information with weather inputs for wheat yield modeling by empirical and crop simulation models is reviewed and extended with enhanced spectral modeling approach for districts in central Punjab. The study uses multi-date and multi-year MODIS data at 250m resolution to both identify wheat crop and develop temporal spectral Enhanced Vegetation Index (EVI) profile for 2001-2019 period. Recently developed high resolution (12km) gridded temperature data from NCMRWF, namely India Monsoon Data Assimilation and Analysis (IMDAA) has been used for computing district-level average of daily (AV) and night-time (NT) temperatures. Multiple linear regression analysis with statistical tests on significance of coefficients for various inputs is used to investigate significance of various input parameters in yield models on multi-district data set. Results identify both area under spectral profile (AS-EVI) and mean peak value (PK-EVI) have significant control on yield. Individual district-level trend based yield (YT) is a significant coefficient in the multi-district models. Model performance is significantly improved by the inclusion of phase-specific temperatures and at specific post-flowering phases the night temperature figured in best models. Significance of the results in development of spatially resolved yields for applications like yield forecast, crop insurance and climate change studies is discussed.

Multi-source remote sensing data shows a significant increase in vegetation on the Tibetan Plateau since 2000

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 ( p < .01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively ( p < .05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions.

Effects of Effective Precipitation and Accumulated Temperature on the Terrestrial EVI (Enhanced Vegetation Index) in the Yellow River Basin, China

To identify the vegetation dynamics and relationship with the hydrothermal conditions in the Yellow River basin (YRB), the spatial–temporal variations of EVI, effective precipitation (Epr), accumulated temperature (At), and their relationships were obtained based on the MODIS EVI data and meteorological data from the YRB during 2001–2020. The results indicate that EVI trends increased during 2001 to 2020, especially in the farmland, forestland, and grassland ecosystems. Epr and At have also increased over the last 20 years. Epr mostly increased faster in the grassland, and water bodies and wetland ecosystems. At mostly increased faster in the water bodies and wetland, desert, and forest ecosystems. Affected by Epr and At, the correlation between the EVI and hydrothermal conditions varied under different hydrothermal conditions. Compared to the At, the Epr was the restrictive factor for the EVI variations in the terrestrial ecosystem in the YRB. In addition, the dynamical thresholds of the EVI, Epr, and At were confirmed. This study can improve the understanding of vegetation variations and their response to regional climate change, which is critical for ecological conservation and the high-quality development of the YRB.

Responses of vegetation to changes in terrestrial water storage and temperature in global mountainous regions

As an important component of terrestrial ecosystem, vegetation acts as a sensitive recorder of changes in hydroclimatic conditions. Long-term time series of remote sensing-based vegetation indices and their influencing environmental driving factors, such as human activities and climate change, have been widely discussed in the literature. Globally, however, little is known about the hydroclimatic processes controlling vegetation changes in mountainous regions, which are conceived as more sensitive to climate change than other landscapes. The present study aims to quantify the respective roles of two dominant hydroclimatic factors, namely, TWS (i.e., terrestrial water storage) and Tair (i.e., temperature), in the spatio-temporal changes of mountainous vegetation over global six contrasting climate zones (i.e., tropical, arid, subtropical, temperate, sub-frigid, and frigid zones) during the period 2003–2016 based on EVI (i.e., enhanced vegetation index), TWS, Tair, and elevation data. Results indicate that the mean EVI shows a larger increasing trend (+0.85 %/decade, p-value < 0.01) and a larger decreasing trend in TWS (−85 mm/decade, p-value < 0.01) across the global mountainous regions than other global regions combined together (+0.61 %/decade, p-value < 0.01), particularly over high latitudes. With the increasing latitudes, the positive effect of temperature more dominates mountainous vegetation growth than moisture, as evidenced by the increasing trends of EVI with warming. However, in certain low-latitude mountainous regions (e.g., East Africa, South Asia, the western Tibetan Plateau, Brazil Plateau, and the southern Rocky Mountains), mountainous vegetation may face degradation due to water deficit induced by increased snowmelt, especially among the high-elevation ecosystems. The water availability controls vegetation activities more than Tair in the mid- and low-latitude regions, including the tropical, arid, and subtropical climate zones. These findings indicate that the potential shifts in mountainous vegetation may occur under the notable interactions with hydroclimatic factors, as the high-latitudes are experiencing ongoing warming and the mid- and low-latitudes are getting dryer.

Climatic limiting factors of North American ecosystems: a remote-sensing based vulnerability analysis

Remote-sensing based vulnerability assessments to climate change are a research priority of critical importance for landscape-scale efforts to prioritize conservation and management of ecosystems. Limiting climatic factors can serve as a proxy for quantifying ecosystem vulnerability, since theory predicts that ecosystems close to critical climate thresholds will be more sensitive to interannual variation in limiting climate factors. Here, we analyze time series of enhanced vegetation index data for continental-scale vulnerability assessments. The analytical approach is a lagged monthly correlation analysis that accounts for memory effects from the previous growing season. Mapping multivariate correlation coefficients reveals that drought vulnerabilities can be found across the continent, including a distinct geographic band across the western boreal forest. The analytical approach reveals climate dependencies at high spatial and temporal resolution, with the direction and strength of correlation coefficients indicating the risk of threshold transgressions at the edge of species and ecosystem tolerance limits. The approach is further useful for hypothesis testing of contributing non-climatic factors to climatic vulnerability, allowing locally targeted management interventions to address climate change.

Identification of forest vegetation types in southern China based on spatio-temporal fusion of GF-1 WFV and MODIS data

It is difficult to obtain long time series of high spatial resolution remote sensing images in southern China because of the complex terrain and frequent cloudy and rainy weather. In contrast, the spatio-temporal fusion can sychonorously obtain remote sensing data with high spatial-temporal resolution, which is beneficial to extract forest vegetation type information. With Xingguo County of Jiangxi Province as the study area, we fused the Landsat8 OLI and GF-1 WFV images with high spatial resolution with high temporal resolution of MODIS09 A1 image on the basis of enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), reconstructed the time series data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI with 8 d step of enhanced vegetation index (EVI), obtained the phenology (PH) characteristics, and identified the forest vegetation types by using random forest classification model. The results showed that the correlation coefficients between the fusion data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI and the real images were all greater than 0.7, and had good consistency in spatial distribution, which could be used to supplement the missing data with high spatial resolution. The extraction accuracy of random forest classification with different combination modes was EVI+PH>EVI>PH and the classification accuracy of fusion data GF-1 was higher than that of Landsat8. A total of 43 variables were selected as the optimal feature variables for classification. The overall accuracy and Kappa coefficient were 95.6% and 94.9%, respectively, including 37 sequential EVI values and 6 phenological feature information. The sequential EVI data contributed more to the identification of forest vegetation types, while the phenological feature information was beneficial to improve the classification accuracy. The ESTARFM fusion algorithm was suitable for GF-1 and MODIS data, which could solve the problem of insufficient long-term sequence of high spatial resolution images. The GF-1 temporal fusion images had high accuracy in the identification of forest vegetation types in southern China under complex terrain and frequent cloudy and rainy weather.

Carbon dioxide spatial variability and dynamics for contrasting land uses in central Brazil agricultural frontier from remote sensing data

Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study evaluates the CO2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO2) dry air mole fraction in the atmosphere, set as XCO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO2 flux in the SMT. The results showed that the temporal variability of CO2 flux is correlated positively with rainfall, while XCO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO2 occurred in the dry and rainy months, respectively, for XCO2, which might be the result of differences in the vertical resolution of the CO2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only XCO2 but also CO2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration.

Spatial Variations in Vegetation Greening in 439 Chinese Cities From 2001 to 2020 Based on Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index Data

Vegetation is essential for maintaining urban ecosystems, climate regulation, and resident health. To explore the variations in city-level vegetation greening (VG) and its relationship to urban expansion, VG in 439 Chinese cities was extracted using the Theil–Sen and Mann–Kendall algorithms based on Moderate Resolution Imaging Spectroradiometer EVI (enhanced vegetation index) data from 2001 to 2020. The spatial variations in VG and its patterns, as well as its relationship with urban expansion, were then analyzed. The following results were obtained: (1) cities with larger greening areas were primarily located in the central and eastern provinces of China, followed by the southeastern, southwestern, and western provinces. The 48 cities with the largest greening areas accounted for 60.47% of the total greening area. (2) VG patches in northern China exhibited better integrity. (3) The centralization trend of VG was evident; the location of VG patterns was influenced by the form of urban expansion. (4) The intensity of artificial impervious area expansion had a weak negative correlation with the VG. Therefore, we must enhance vegetation in new urban areas to improve the spatial balance of VG. The present results of this study can provide a foundation for developing effective policies for the construction and management of urban greenery projects.

Land surface phenology retrievals for arid and semi-arid ecosystems

Land surface phenology (LSP) plays a critical role in the regulation of photosynthesis, evapotranspiration, and energy fluxes. Significant progress has been made in extracting LSP information over large areas using satellite data, yet LSP retrievals remain a challenge over vast arid and semi-arid ecosystems because of sparse greenness, high variability and the lack of distinct annual patterns; for example, the MODerate Imaging Spectrometer (MODIS) Land Cover Dynamics Product MCD12Q2 that provides LSP metrics globally often failed to provide LSP information in these ecosystems. In this study, we used a modified threshold algorithm to extract LSP timing metrics, including the start, peak, and end of growing seasons, using the 16-day composite Enhanced Vegetation Index (EVI) time series from MODIS data. We applied this regionally customized algorithm across all arid and semi-arid climate regions of Australia (75% of the continental land area) encompassing shrublands, grasslands, savannas, woodlands, and croplands, extracting LSP metrics annually from 2003 to 2018, with up to two (phenology) seasons accounted for in each year. Our algorithm yielded an average of 64.9% successful rate of retrieval (proportion of pixels with retrieved LSP metrics) across 16 years in Arid and Semi-arid AUStralia (AS-AUS), which was a significant increase compared to the 14.5% rate of retrieval yielded in our study area by the global product and the major cause of the different performances between these two approaches was the different EVI amplitude restrictions utilized to avoid spurious peaks (i.e. EVI amplitude ≥ 0.1 used by the global product and peak EVI ≥ time series average EVI used by our algorithm). Gross primary productivity (GPP) measurements at OzFlux eddy covariance (EC) tower sites were used to cross-compare with the presence/absence of growing seasons detected by our algorithm, and 97% of our retrieved seasons matched with those extracted using EC data. Preliminary tests at five OzFlux sites showed that our algorithm was robust to view angle-induced sensitivity of the input data and showed similar performance when using EVI data calculated using MODIS Nadir BRDF-Adjusted Reflectance product. Our retrieved LSP metrics revealed that vegetation growth in arid ecosystems is highly irregular and can occur at any time of the year, more than once in a year, or can skip a year. The proportion of pixels with two growing seasons was found to be correlated with the average annual precipitation of the study area (p < 0.01), providing an estimation approach of LSP via rainfall. Our study improves the detection and measurement of vegetation phenology in arid and semi-arid regions by improving the spatial extend of LSP retrievals, which contributes to studies on LSP variations and dryland ecosystem resilience to climate change. More evaluation is planned for future work to assess and further improve the accuracy of the retrieved LSP metrics.

Ecosystem stability at the landscape scale is primarily associated with climatic history

Abstract There is an increasing interest in landscape‐scale perspectives of ecosystem functioning to inform policy and conservation decisions. However, we need a better understanding of the stability of ecosystem functioning (e.g. plant productivity) at the landscape scale to inform policy around topics such as global food security. We investigate the role of the ecological and environmental context on landscape‐scale stability of plant productivity in agricultural pasture using remotely sensed enhanced vegetation index data. We determine whether four measures of stability (variability, magnitude of extreme anomalies, recovery time and recovery rate) are predicted by (a) species richness of vascular plants, (b) regional land cover heterogeneity and (c) climatic history. Stability of plant productivity was primarily associated with climatic history, particularly a history of extreme events. These effects outweighed any positive effects of species richness in the agricultural landscape. A history of variable and extreme climates both increased and decreased contemporary ecosystem stability, suggesting both cumulative and legacy effects, whereas land cover heterogeneity had no effect on stability. The landscape scale is a relevant spatial scale for the management of an ecosystem's stability. At this scale, we find that past climate is a stronger driver of stability in plant productivity than species richness, differing from results at finer field scales. Management should take an integrated approach by incorporating the environmental context of the landscape, such as its climatic history, and consider multiple components of stability to maintain functioning in landscapes that are particularly vulnerable to environmental change. A free Plain Language Summary can be found within the Supporting Information of this article.

Estimating global maximum gross primary productivity of vegetation based on the combination of MODIS greenness and temperature data

Accurate estimation of the spatial-temporal variation of the maximum gross primary productivity (GPPmax) of vegetation is of great significance for predicting carbon fluxes and vegetation-climate feedback. In this study, the GPPmax estimation models were constructed based on driving variables derived from the MODIS enhanced vegetation index (EVI) and land surface temperature (LST) time series data using a stepwise regression analysis. The models were calibrated and validated with the observed GPPmax from 145 FLUXNET sites with 734 site-year data from 2000 to 2014. The GPPmax estimates and the changes in their trends at the global scale were also compared with other GPPmax products from the vegetation photosynthesis model and the eddy covariance-light use efficiency model. The results showed that the GPPmax for both forest and non-forest vegetation types were estimated well, with R2 of 0.47–0.86 and 0.47–0.95 and relative root mean square error of 10.14–35.14% and 11.25–30.02% for calibration and validation data, respectively. Summer EVI and spring EVI and LST played important roles in explaining the variation in GPPmax. The GPPmax estimates from this study and the changes in their trends were highly correlated with GPPmax estimates from the vegetation photosynthesis model, with R2 > 0.70 for most vegetation types. The GPPmax significantly increased in western North America, northern Europe, and eastern China, but decreased in tropical regions. This study concluded that the variation in GPPmax for various vegetation types on a global scale can be accurately estimated based on MODIS EVI and LST time series data, which provides a simple but effective way for large-scale estimation of GPPmax.