MODIS Enhanced Vegetation Index Research Articles

Changes in peak greenness timing and senescence duration codetermine the responses of leaf senescence date to drought over Mongolian grassland

Leaf senescence is a key trait for vegetation in temperate regions to overcome winter temperatures, and extreme drought events have profoundly affected its timing. The effects of drought on autumn phenology have been investigated by correlating leaf senescence date (LSD) to multiple drought indices. However, leaf senescence is a dynamic process starting from peak greenness timing (PGT) and lasting several months (senescence duration, SD). How PGT and SD changes affected the LSD's response to drought is still poorly understood. Here, we investigated LSD's response to drought in the Mongolian grassland based on PGT and SD. We first extracted the LSD, PGT, and SD from the MODIS Enhanced Vegetation Index (EVI) during 2001–2020 and determined the late-season drought events (LDE) using the Standardized Precipitation Evapotranspiration Index. Subsequently, we quantified the response of LSD to LDE as the difference between drought and normal years (ΔLSD), and then quantified the contributions of changes in PGT and SD on ΔLSD. We also analyzed the effects of drought on late-season productivity. The results showed that LSD may advance or delay under LDE, but the earlier LSD (in 68.4 % of areas) was mainly distributed in warmer and drier regions. Earlier LSD under drought was more controlled by earlier PGT, while later LSD under drought was more controlled by extended SD. Changes in late-season productivity under LDE were more correlated with changes in annual maximum EVI (EVImax) than ΔLSD. These results enhanced our understanding of how LSD of temperate grassland responds to drought events and emphasized the importance of investigating the response of autumn phenology to drought from a process-based perspective.

Vegetation Stress Monitor—Assessment of Drought and Temperature-Related Effects on Vegetation in Germany Analyzing MODIS Time Series over 23 Years

Over the past two decades, and particularly since 2018, Central Europe has experienced several droughts with strong impacts on ecosystems and food production. It is expected that under accelerating climate change, droughts and resulting vegetation and ecosystem stress will further increase. Against this background, there is a need for techniques and datasets that allow for monitoring of the timing, extent and effects of droughts. Vegetation indices (VIs) based on satellite Earth observation (EO) can be used to directly assess vegetation stress over large areas. Here, we use a MODIS Enhanced Vegetation Index (EVI) time series to analyze and characterize the vegetation stress on Germany’s croplands and grasslands that has occurred since 2000. A special focus is put on the years from 2018 to 2022, an extraordinary 5-year period characterized by a high frequency of droughts and heat waves. The study reveals strong variations in agricultural drought patterns during the past major drought years in Germany (such as 2003 or 2018), as well as large regional differences in climate-related vegetation stress. The northern parts of Germany showed a higher tendency to be affected by drought effects, particularly after 2018. Further, correlation analyses showed a strong relationship between annual yields of maize, potatoes and winter wheat and previous vegetation stress, where the timing of strongest relationships could be related to crop-specific development stages. Our results support the potential of VI time series for robustly monitoring and predicting effects of climate-related vegetation development and agricultural yields.

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.

Important role of precipitation in controlling a more uniform spring phenology in the Qinba Mountains, China.

Under global warming, the gradual pattern of spring phenology along elevation gradients (EG) has significantly changed. However, current knowledge on the phenomenon of a more uniform spring phenology is mainly focused on the effect of temperature and neglected precipitation. This study aimed to determine whether a more uniform spring phenology occurs along EG in the Qinba Mountains (QB) and explore the effect of precipitation on this pattern. We used Savitzky-Golay (S-G) filtering to extract the start of season (SOS) of the forest from the MODIS Enhanced Vegetation Index (EVI) during 2001-2018 and determined the main drivers of the SOS patterns along EG by partial correlation analyses. The SOS showed a more uniform trend along EG in the QB with a rate of 0.26 ± 0.01 days 100 m-1 per decade during 2001-2018, but there were differences around 2011.A delayed SOS at low elevations was possibly due to the reduced spring precipitation (SP) and spring temperature (ST) between 2001 and 2011. Additionally, an advanced SOS at high elevations may have been caused by the increased SP and reduced winter temperature (WT). These divergent trends contributed to a significant uniform trend of SOS with a rate of 0.85 ± 0.02 days 100 m-1 per decade. Since 2011, significantly higher SP (especially at low elevations) and rising ST advanced the SOS, and the SOS at lower altitudes was more advanced than at higher altitudes, resulting in greater SOS differences along EG (0.54 ± 0.02 days 100 m-1 per decade). The SP determined the direction of the uniform trend in SOS by controlling the SOS patterns at low elevations. A more uniform SOS may have important effects on local ecosystem stability. Our findings could provide a theoretical basis for establishing ecological restoration measures in areas experiencing similar trends.

Vegetation Dynamics Due to Urbanization in the Coastal Cities along the Maritime Silk Road

Substantial research indicates the effects of urbanization on vegetation cover; however, a view of this scenario from a regional scale is absent. Nowadays, coastal cities have become the new engine for the development of countries in coastal areas. To capture the effects of rapid urbanization on vegetation dynamics, 35 coastal cities along the Maritime Silk Road (MSR) were selected to study the related research using quantitative tools. We calculated spatiotemporal trends of vegetation dynamics along an urban development intensity (UDI) gradient using the MODIS-enhanced vegetation index (EVI) during the period of 2000–2015. We found a significant reduction (p < 0.05) in the EVI in the core area against that in the rural area (ΔEVI) of 14 cities and an insignificant change in vegetation in the peri-urban areas or urban outskirts. EVI decreased significantly along the UDI gradients in 12 coastal cities with a linear pattern and in seven coastal cities with a concave pattern; only Bangkok exhibited a convex pattern. The nonlinear pattern between the EVI and UDI reflected the fact that vegetation dynamics were affected by complicated factors, including climate change and human interventions, over a long period of time. In conclusion, our work provided a scientific reference for the sustainable development of coastal cities along the MSR; further work is necessary to explore the mechanic details of the positive and negative influences of urban factors and related policies on vegetation conditions.

Different Temporal Stability and Responses to Droughts between Needleleaf Forests and Broadleaf Forests in North China during 2001–2018

Droughts can affect the physiological activity of trees, damage tissues, and even trigger mortality, yet the response of different forest types to drought at the decadal time scale remains uncertain. In this study, we used two remote sensing-based vegetation products, the MODIS enhanced vegetation index (EVI) and MODIS gross primary productivity (GPP), to explore the temporal stability of deciduous needleleaf forests (DNFs) and deciduous broadleaf forests (DBFs) in droughts and their legacy effects in North China from 2001 to 2018. The results of both products showed that the temporal stability of DBFs was consistently much higher than that of DNFs, even though the DBFs experienced extreme droughts and the DNFs did not. The DBFs also exhibited similar patterns in their legacy effects from droughts, with these effects extending up to 4 years after the droughts. These results indicate that DBFs have been better acclimated to drought events in North China. Furthermore, the results suggest that the GPP was more sensitive to water variability than EVI. These findings will be helpful for forest modeling, management, and conservation.

Drought sensitivity of pastures related to soil and landform in the eastern Amazon

Livestock in the Amazon is mainly limited by the lack of forage biomass production in the dry season. The sensitivity of pasture to drought depends on pasture management and on certain natural factors that enable the grass cover to stay green for longer under low rainfall. The sustainability of livestock production systems depends on identifying these natural factors to make better use of their forage production potential without consuming additional water resources or resorting to deforestation. We built an index of pasture dependence on rainfall at the beginning of the dry season by calculating the slope of the linear regression between the MODIS enhanced vegetation index and monthly cumulative rainfall obtained from the Tropical Rainfall Measuring Mission. The index was calculated in Paragominas municipality. A 15-year time series (2000 to 2014) was used to assess the sensitivity of pasture to drought. A map of geomorphological units was overlaid on the index map to check whether the soil and the landform influence drought sensitivity at a large scale. Drought resistance was shown to be higher in lowland areas with loamy sand with a shallow groundwater table and on plateaus covered by fertile sedimentary clay. The escarpments with a ferruginous duricrust and mottled clay were shown to be the most sensitive to drought. These results underline the importance of natural factors in pasture productivity in tropical humid areas.

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

BackgroundVegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.ResultsUsing satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland.ConclusionsOur results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

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.

Exploring potential of C band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity.

Development of satellite technology over decades established unique approach to analyze rice crop phenological parameters and supervise growth and production. Advancement in technology leads to the development of microwave remote sensing that is operational round the clock irrespective of weather conditions. An attempt has been carried out in the present study to classify and map phenological stages, namely, transplanting stage, heading stage, and harvesting stage of rice crop using Sentinel-1, MODIS Enhanced vegetation index (EVI) data. Puddling stage, transplanting stage, heading stage, and harvesting stages are identified on 05th and 15th January, 28th February, and 27th March of the year 2017, respectively. Field visits are performed frequently at sampling locations for an effective study on rice phenological stages, rice yield estimation, and mapping large-scale area using regression analysis. Estimated yield is compared with ground truth data; average yield produced from study area is 3.03 tons/acre. Methodology adopted reflected satisfactory performance with minimal error for mapping of rice phenological stages, and it is suggested to experiment with other agricultural crops.

Shrub decline and expansion of wetland vegetation revealed by very high resolution land cover change detection in the Siberian lowland tundra

Vegetation change, permafrost degradation and their interactions affect greenhouse gas fluxes, hydrology and surface energy balance in Arctic ecosystems. The Arctic shows an overall “greening” trend (i.e. increased plant biomass and productivity) attributed to expansion of shrub vegetation. However, Arctic shrub dynamics show strong spatial variability and locally “browning” may be observed. Mechanistic understanding of greening and browning trends is necessary to accurately assess the response of Arctic vegetation to a changing climate. In this context, the Siberian Arctic is an understudied region. Between 2010 and 2019, increased browning (as derived from the MODIS Enhanced Vegetation Index) was observed in the Eastern Siberian Indigirka Lowlands. To support interpretation of local greening and browning dynamics, we quantified changes in land cover and transition probabilities in a representative tundra site in the Indigirka Lowlands using a timeseries of three very high resolution (VHR) (0.5 m) satellite images acquired between 2010 and 2019. Using spatiotemporal Potts model regularization, we substantially reduced classification errors related to optical and phenological inconsistencies in the image material. VHR images show that recent browning was associated with declines in shrub, lichen and tussock vegetation and increases in open water, sedge and especially Sphagnum vegetation. Observed formation and expansion of small open water bodies in shrub dominated vegetation suggests abrupt thaw of ice-rich permafrost. Transitions from open water to sedge and Sphagnum, indicate aquatic succession upon disturbance. The overall shift towards open water and wetland vegetation suggests a wetting trend, likely associated with permafrost degradation. Landsat data confirmed widespread expansion of surface water throughout the Indigirka Lowlands. However, the increase in the area of small water bodies observed in VHR data was not visible in Landsat-derived surface water data, which suggests that VHR data is essential for early detection of small-scale disturbances and associated vegetation change in permafrost ecosystems.

A USER-FRIENDLY REMOTE-SENSING WEB-PLATFORM FOR BIODIVERSITY CONSERVATION AND MANAGEMENT IN PROTECTED AREAS

Abstract. The Chilean SNASPE is a complex network of 104 protected areas covering 18.5 million hectares of continental and insular Chile in South America. The geographical complexity and high biodiversity of the SNASPE make difficult to develop a unified monitoring system for conservation and management. In this contribution, we introduce a novel and remote-sensing web-platform for monitoring SNASPE units based completely in open acces data and software. The platform was designed in close cooperation with the Chilean forest service CONAF in order to make it applicable to the whole SNASPE. Following the framework of the Group on Earth Observation - Biodiversity Observation Network (GEO-BON), we used the Essential Biodiversity Variable (EBV) Phenology and MODIS Enhanced Vegetation Index (EVI) data to detect in near-real-time anomalies from the normal annual phenological cycle of vegetation. The platform is based on a flexible non-parametric probabilistic algorithm (the “npphen” R package) capable to reconstruct any type of leaf phenology and to quantify its inter-annual variation by means of confidence intervals around the most probable annual curve. Phenological anomalies are then calculated as a deviation from the expected annual cycle and judged based on their location within the confidence intervals. Anomalies located above 95% confidence interval trigger a “red alert” which is displayed on the web application as soon as the MODIS data become available. This user-friendly platform was implemented in the La Campana National Park giving early alerts of a severe drought in 2019, warning Conaf to implement actions to protect the park from potential wild fires.

Spatial distribution of GDP based on integrated NPS-VIIRS nighttime light and MODIS EVI data: a case study of Turkey

Satellite-derived nighttime light data have been increasingly used as a proxy measure for investigating economic activity. However, there are few studies focusing on the spatialised mapping of the GDP at pixel level and further analysis of the economic differences in agricultural and non-agricultural sectors for the different regions using the VIIRS-NPP data. This paper aims to fill this gap in the literature through developing a pixel-level agricultural and non-agricultural GDP map for Turkey in 2015 by combining the VIIRS-NPP nighttime imagery, Terra MODIS-Enhanced Vegetation Index, and land use/cover data from CORINE. The inclusion of vegetation indices and land cover data would significantly improve the estimates of sectorial GDP for Turkey where agriculture is one of the dominating sectors in the Country. GDP density map offers a significant database for both researchers and policy makers in the analysis of regional economic dynamics that will assist in formulating sustainable regional growth strategies.

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

AbstractSoil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1‐D diffusion‐reaction model to calculate depth‐resolved CO2 production rates from soil CO2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016–2018. While soil respiration correlated with temperature on diel timescales (p < 0.05), seasonal variability was dominated by soil moisture and plant phenology (p < 0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest that the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore‐summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Finally, we observed significant late season CO2 fluxes from the deep subsoil (>165 cm) that support growing evidence for the importance of subsoil processes in driving integrated respiration fluxes.

Increasing Summer Rainfall and Asymmetrical Diurnal and Seasonal Warming Enhanced Vegetation Greenness in Temperate Deciduous Forests and Grasslands of Northern China

Temperate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001–2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are 3.97 ± 0.062 × 10−3 and 4.8 ± 0.116 × 10−3 yr−1, respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of 0.72 ± 0.01. While wet-year (0.57 ± 0.01) and nongrowing-season precipitation (0.68 ± 0.01) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of 0.66 ± 0.01, but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of −0.70 ± 0.01. Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition.

Downscaling of MODIS leaf area index using landsat vegetation index

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model.

Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495–5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments with drainage areas ranging from 80 to 1000 km2 in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture estimates, the MODIS enhanced vegetation index (EVI), gauge-corrected Stage IV radar rainfall, and USGS streamflow data. We analyze the event runoff ratio as a function of event-scale rainfall, antecedent SMAP soil moisture and soil-moisture-deficit-normalized rainfall for the events in a period from March 31, 2015 to October 31, 2018. Our goal is to confirm the relationships identified by Crow et al. (2017) in heavily managed agricultural landscapes and to refine some of their methodological steps to quantify the role of additional variables controlling runoff production. To this end, we define three different strategies to identify rainfall-runoff events and add a baseflow separation step to better insulate event scale stormflow runoff. We test the effects of antecedent soil moisture, rainfall, and vegetation on the event-scale runoff ratio. The antecedent SMAP soil moisture and event-scale rainfall are found to have significant predictive power in estimating event runoff ratio. Soil moisture deficit-normalized rainfall, introduced as the ratio of event-scale rainfall to available space in top soil before initiation of the event, exhibited a more distinct relationship with runoff ratio. The long-term analysis of runoff ratio, rainfall, and MODIS EVI indicated that, in an agricultural region, vegetation plays a significant role in determining event-scale runoff ratios. The methodology and outcome of our study have direct implications on real-time flood forecasting and long-term hydrologic assessments.

Investigating the urbanization process and its impact on vegetation change and urban heat island in Wuhan, China.

Rapid urbanization significantly changes vegetation coverage and heat distribution, which threatens the sustainable development and the quality of life. As the largest developing city in Central China, Wuhan was chosen as the experimental region. This study investigated the urbanization process of Wuhan from 1989 to 2917 based on Landsat data. Combined with MODIS EVI (Enhanced Vegetation Index) and LST (Land Surface Temperature) data, vegetation disturbance and surface urban heat island (SUHI) caused by urbanization were discussed for 2001-2017. Furthermore, correlation between ∆EVI (urban EVI minus rural EVI) and ∆LST (urban LST minus rural LST) was also conducted. The results were as follows: (1) Wuhan experienced a strong urbanization over the past 29years, with an increasing urban expansion rate and the altered dominant urban expansion pattern (edge expansion and infilling). After the enhanced vegetation functions and urban increased structures, the urbanization finally caused the fragmented patches and irregular urban shapes. (2) Urbanization had a positive effect on LST but a negative effect on EVI. From 2001 to 2017, the highest increasing rate of ∆LST for the old urban area (OUA) and urbanized area (UA) was both observed in summer daytime (OUA, 0.106°C/a; UA, 0.207°C/a). The decreasing rate of ∆EVI reached the highest value in summer (OUA, 0.00697/a; UA, 0.00298/a). (3) There was a strong negative correlation (except spring and winter for OUA) between ∆EVI and ∆LST in daytime, which proved that the activity of vegetation in daytime could relieve LST to a certain extent. This study clarifies the dynamic urbanization process of Wuhan and discusses its impacts on vegetation change and SUHI. Efficiently investigating urbanization process and quantifying its impacts on urban environment are critical for regional ecological conservation.

Comparing in-situ leaf observations in early spring with flux tower CO2 exchange, MODIS EVI and modeled LAI in a northern mixed forest

Changes in the timing and duration of spring leaf development have implications for the start of the carbon uptake period and are therefore fundamental to the accurate calculation of carbon budgets, and in determining the potential for forests to sequester CO2. Here, we examined trends in CO2 exchange (Net Ecosystem Exchange (NEE), Gross Primary Production (GPP) and Ecosystem Respiration (ER)) (1997–2016) and satellite derived measures (Enhanced Vegetation Index (EVI) and modeled Leaf Area Index (LAI)) of the start of spring from the MODIS product MOD13Q1 (2001–2016) for a mixed forest landscape in northern Wisconsin, USA. We then explored the relationship between these indirect determinants of spring phenology and the timing and duration of spring phenophases (bud-burst, leaf-out, full-leaf unfolded) of trees over a 5-year period (2006–2010). Contrary to earlier studies focus’, our analysis did not find a consistent link between the early transition, of the forest stand from C source to sink with increased annual productivity. Interestingly, while annual regional NEE trended from a source to a sink over the study period, there were no significant concomitant trends in the timing of the start of the season, peak season or the duration of the season derived from (i) satellite data (2001–2016), (ii) flux data (1997–2016) nor from in situ observations (2006–2010). The range of time periods used and difference in phenological determinants examined likely contributed to a lack of expected relationships. The results highlight the need for in situ observations of different forest layers, in particular shrubs, which could help explain current discrepancies between direct and indirect determinants of spring phenology. Furthermore, characterization of abiotic influences on C flux measurements may further explain some of the observed discrepancies.

Relationship between MODIS EVI and LAI across time and space

The Leaf Area Index (LAI) is used as input in hydrological and bio-chemical models for the estimation of water-cycle characteristics, agricultural primary production and other processes. Vegetation Indices (VIs) are used to monitor vegetation state and health. Considering that easily computed VIs can be used for the estimation of LAI, this study applied a regression analysis between MODIS Enhanced Vegetation Index (EVI) and LAI data in five sites around the world. A linear model was found to provide a good description of the LAI–EVI relationship across all examined vegetation types and times. Medium accuracy models were improved when variability of time and vegetation type were considered, indicating that these parameters highly affect the LAI–EVI relationship. Sensitivity of EVI to LAI was higher in periods of high biomass production. Regression analysis between LAI–EVI showed a stronger relationship for the study sites characterized by dry and warm tropical climatic conditions.