Relationship between AVHRR surface temperature and NDVI in Arctic tundra ecosystems

A negative, linear relationship between thermal emissions and a spectral vegetation index has been demonstrated for numerous mid‐latitude ecosystems. In this study, it is hypothesized that the relationship between surface temperature and the normalized difference vegetation index (NDVI) will be linear, but positive in Arctic tundra ecosystems due to the contrast between warm vegetation and the cold soil/moss background. This hypothesis is tested using Advanced Very High Resolution Radiometer (AVHRR) data collected over the North Slope of Alaska on three days during the summer of 1999. Results of the study generally provide support for this hypothesis. However, a consistent relationship observed across two contrasting physiographic provinces on one study day was shown to change the following day and could not be readily explained by differences in satellite zenith angle or observed air temperature. Surface temperatures are shown to respond directly to spatial and temporal variations in air temperature.     

Rainfall and vegetation monitoring in the Savanna Zone of the Democratic Republic of Sudan using the NOAA Advanced Very High Resolution Radiometer

NOAA-6 and NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (4?km ground resolution) data were obtained at three-day intervals throughout each of the four-month periods covering the 1980, 1983 and 1984 growing seasons, between latitudes 10° and 22° North in the Democratic Republic of Sudan. Daily rainfall data for twelve meteorological stations spanning the Savanna Zone were analysed. Rainfall in Sudan during 1980 was below normal, but in 1983 and 1984 there were moderate and severe droughts. The satellite data were used to calculate normalized difference vegetation index (NDVI) values from the visible and near-infrared bands of the satellite data. These were processed into ten-day composite data sets using the AVHRR thermal-infrared channel as a cloud screen and a temporal compositing procedure that reduces cloud contamination and selects viewing angles closest to nadir.

The ten-day composite NDVI values and the integrals of NDVI for each growing season were found to be closely correlated with rainfall. The constants of regressions between NDVI and rainfall were lower in 1983 and 1984 than in 1980, which suggests there was reduced water-use efficiency by the rangeland vegetation in drought years. It was found that July and August NDVI values were closely related to the integrated NDVI values; hence early- and mid-season NDVI data could be used to predict annual primary production. Images showing the geographical distribution of values of NDVI prepared for the three years clearly illustrate the effects of the 1983 and 1984 droughts, compared with the higher rainfall of 1980. The precision of the relationship between rainfall and the vegetation indices for the meteorological stations encourages the view that NOAA AVHRR GAC composite NDVI values can be used to monitor effective rainfall in the Savanna Zone of the Democratic Republic of Sudan     

Synergism between NOAA-AVHRR and Meteosat data for studying vegetation development in semi-arid West Africa

Abstract

Rainfall estimates, based on cold cloud duration estimated from Meteosat data, are compared with vegetation development depicted by data of the normalized difference vegetation index (NDVI) from the National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) for part of the Sahel. Decadal data from the 1985 and 1986 growing seasons are examined to determine the synergism of the datasets for rangeland monitoring. There is a general correspondence between the two datasets with a marked lag between rainfall and NDVI of between 10 and 20 days. This time lag is particularly noticeable at the beginning of the rainy season and in the more northern areas where rainfall is the limiting factor for growth. Principal component analysis was used to examine deviations from the general relationship between rainfall and the NDVI. Areas of low NDVI values for a given input of rainfall were identified: at a regional scale, they give an indication of areas of low production potential and possible degradation of ecosystems. This study demonstrates in a preliminary way the synergism of such datasets derived from satellite--borne sensors with coarse spatial resolution, which may provide new information for the improved management of the Sahelian grasslands.     

Response of the normalized difference vegetation index to varying cloud conditions in Arctic tundra environments

Frequent cloud cover is a major environmental constraint to optical remote sensing studies in Arctic locations, including studies based on ground or aircraft observations. The objective of this study was to determine how cloud induced variations in solar illumination affect the normalized difference vegetation index (NDVI) of representative vegetation types on the North Slope of Alaska. Illumination conditions were quantified using a cloud index (incident shortwave radiation at the surface divided by radiation at the top of the atmosphere). The results indicated that the NDVI was stable across a wide range of cloud index values, particularly when the value exceeded 0.5. It is concluded that a cloud index threshold may be used to select unbiased NDVI values from a data set collected under varying illumination conditions.     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models

Fraction of green vegetation, fg, and green leaf area index, Lg, are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction models regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (fg) and vertical (Lg) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate fg=(NDVI-NDVI0)/(NDVI8-NDVI0), where NDVIo (bare soil) and NDVI (dense vegetation) are specified as global constants independent of vegetation/soil type. Global (0.15o)2 spatial resolution monthly maps of fg were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface fluxes were improved.     

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.     

Suivi du développement végétal au cours de l'été 1984 dans le Sahel Malien

A ground data-collection programme was initiated to establish a calibration between the normalized difference vegetation index (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and grassland biomass. Thirty sites were selected representing a range of Sahclian vegetation communities in the Gourma region of Mali and monitored during the 1984 growing season. The sites were 1?km square and located within larger areas of homogeneous terrain. The herbaceous and woody strata were sampled every fourteen days, and above-ground green biomass and rainfall data were collected. Ground and airborne radiometer data were recorded to facilitate interpretation of the satellite data, and aerial photographs were taken to provide estimates of tree and shrub density. AVHRR LAC and GAC data were acquired and a thermal cloud mask was applied to the data. NDVI values were extracted for the ground sites and correlation analysis performed. Low correlation coefficients were calculated for the ground measured green biomass and satellite NDVI (0,67). The correlation between the maximum NDVI and the total biomass produced during the season was 0,73. A value of 0,05 was determined as the NDVI associated with the minimum vegetation cover identifiable by the satellite (100 kg/ha). Explanation is given for the possible causes for such low correlations, including the very low biomass production associated with the 1984 drought conditions, atmospheric haze and dust and poor locational accuracy of the satellite data     

Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.     

Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Evaluating different NDVI composite techniques using NOAA-14 AVHRR data

Normalized Difference Vegetation Index (NDVI) data derived from Advanced Very High Resolution Radiometer (AVHRR) data are influenced by cloud contamination, which is common in individual AVHRR scenes. Maximum value compositing (MVC) of NDVI data has been employed to minimize cloud contamination. Two types of weekly NDVI composites were built for crop seasons in summer: one from all available AVHRR data (named the traditional NDVI composite) and the other from solely cloud-free AVHRR data (named the conditional NDVI composite). The MVC method was applied to both composites. The main objective of this study was to compare the two types of NDVI composites using Texas data. The NDVI seasonal profiles produced from the conditional NDVI composites agreed with the field measured leaf area index (LAI) data, reaching maximum values at similar times. However, the traditional NDVI composites showed irregular patterns, primarily due to cloud contamination. These study results suggest that cloud detection for individual AVHRR scenes should be strongly recommended before producing weekly NDVI composites. Appropriate AVHRR data pre-processing is important for composite products to be used for short-term vegetation condition and biomass studies, where the traditional NDVI composite data do not eliminate cloud-contaminated pixels. In addition, this study showed that atmosphere composition affected near-infrared reflectance more than visible reflectance. The near-infrared reflectance was increasingly adjusted through atmospheric correction.     

基于AVHRR和TM数据的时间序列较高分辨率NDVI数据集重构方法

由于技术条件的限制,一个传感器很难同时具有高空间分辨率和高时间分辨率。然而,在高分辨率尺度上监测地表景观季节性变化的能力是全球的迫切需要,融合周期短、覆盖范围广与分辨率高、周期长的遥感数据是一种较好的方法。基于AVHRR时间分辨率高和TM空间分辨率高及其数据积累时间长的特点,选择若尔盖高原为研究区域,在改进ESTARFM方法的基础上,对TM NDVI和AVHRR NDVI进行融合,构建高时空分辨率的NDVI数据集。研究结果表明:该方法能有机结合AVHRR NDVI的时间变化信息与TM NDVI的空间差异信息,有效实现高时空分辨率NDVI数据集的重构,3景预测高分辨率NDVI与MODIS NDVI产品相关系数分别达到了0.89、0.91和0.85。该方法能够在时间上保留高时间分辨率数据的时间变化信息,同时在空间上反映高空间分辨率数据的空间差异信息,从而为有效构建相对高分辨率时间序列NDVI数据集提供了可能的方法。     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

Agreement evaluation of AVHRR and MODIS 16‐day composite NDVI data sets

Satellite‐derived normalized difference vegetation index (NDVI) data have been used extensively to detect and monitor vegetation conditions at regional and global levels. A combination of NDVI data sets derived from AVHRR and MODIS can be used to construct a long NDVI time series that may also be extended to VIIRS. Comparative analysis of NDVI data derived from AVHRR and MODIS is critical to understanding the data continuity through the time series. In this study, the AVHRR and MODIS 16‐day composite NDVI products were compared using regression and agreement analysis methods. The analysis shows a high agreement between the AVHRR‐NDVI and MODIS‐NDVI observed from 2002 and 2003 for the conterminous United States, but the difference between the two data sets is appreciable. Twenty per cent of the total difference between the two data sets is due to systematic difference, with the remainder due to unsystematic difference. The systematic difference can be eliminated with a linear regression‐based transformation between two data sets, and the unsystematic difference can be reduced partially by applying spatial filters to the data. We conclude that the continuity of NDVI time series from AVHRR to MODIS is satisfactory, but a linear transformation between the two sets is recommended.     

Potential operational multi-satellite sensor mapping of snow cover in maritime sub-polar regions

Scottish snow cover as an example of a maritime sub-polar region has two principal problems for an operational monitoring programme: the often ephemeral nature of the snow cover, and the loss of direct access to snow imagery due to clouds. At present only the NOAA AVHRR series provides images with the required temporal and spatial resolutions. Based on the availability of data from the Dundee satellite data receiving station a range of NOAA-12, -14 and -15 day and night passes were collected and processed. Three snow cover products were produced from the NOAA AVHRR/2 data: snow area based on channel 134 ISODATA classifications, percentage snow cover based on multi-temporal Normalized Difference Vegetation Index (NDVI), and daily maximum snow surface temperature maps using split-window combinations of thermal channels. Noted improvements were evident in the accuracy and resolution of snow cover classifications based on provisional testing of AVHRR/3 data. Maximum snow surface temperature maps indicated a potential for mapping areas of snow melt. The principal limitation in the operational snow cover mapping with AVHRR, however, remains the loss of temporal resolution due to cloud cover.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.     

On the relationship between monthly mean and maximum-value composite normalized vegetation indices

Abstract

A relationship between the maximum-value composite and monthly mean normalized difference vegetation index (NDVI) is derived statistically using data over the U.S. Great Plains during 1986. The monthly mean NDVI is obtained using a simple nine-day compositing technique based on the specifics of the scan patterns of the NOAA-9 Advanced Very High Resolution Radiometer (AVHRR). The results indicate that these two quantities are closely related over grassland and forest during the growing season. It is suggested that in such areas a monthly mean NDVI can be roughly approximated by 80 per cent of the monthly maximum NDVI, the latter being a standard satellite data product. The derived relationship was validated using data for the growing season of 1987.     

Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI

Arctic vegetation distribution is largely controlled by climate, particularly summer temperatures. Summer temperatures have been increasing in the Arctic and this trend is expected to continue. Arctic vegetation has been shown to change in response to increases in summer temperatures, which in turn affects arctic fauna, human communities and industries. An understanding of the relationship of existing plant communities to temperature is important in order to monitor change effectively. In addition, variation along existing climate gradients can help predict where and how vegetation changes may occur as climate warming continues. In this study we described the spatial relationship between satellite-derived land surface temperature (LST), circumpolar arctic vegetation, and normalized difference vegetation index (NDVI). LST, mapped as summer warmth index (SWI), accurately portrayed temperature gradients due to latitude, elevation and distance from the coast. The SWI maps also reflected NDVI patterns, though NDVI patterns were more complex due to the effects of lakes, different substrates and different-aged glacial surfaces. We found that for the whole Arctic, a 5 °C increase in SWI along the climate gradient corresponded to an increase in NDVI of approximately 0.07. This result supports and is of similar magnitude as temporal studies showing increases of arctic NDVI corresponding to increases in growing season temperatures over the length of the satellite record. The strongest positive relationship between NDVI and SWI occurred in partially vegetated and graminoid vegetation types. Recently deglaciated areas, areas with many water bodies, carbonate soil areas, and high mountains had lower NDVI values than predicted by SWI. Plant growth in these areas was limited by substrate factors as well as temperature, and thus is likely to respond less to climate warming than other areas.     

Correlated change in normalized difference vegetation index and the seasonal trajectory of photosynthetic capacity in a conifer stand

Seasonal changes in canopy photosynthetic activity play an important role in carbon assimilation. However, few simulation models for estimating carbon balances have included them due to scarcity in quality data. This paper investigates some important aspects of the relationship between the seasonal trajectory of photosynthetic capacity and the time series of a common vegetation index (normalized difference vegetation index, NDVI), which was derived from on site micrometeorological measurements or smoothed and downscaled from satellite‐borne NDVI sensors. A parameter indicating the seasonality of canopy physiological activity, P _E, was retrieved through fitting a half‐hour step process model, PROXEL_NEE, to gross primary production (GPP) estimates by inversion for carboxylation and light utilization efficiencies. The relative maximum rate of carboxylation (V _rm), a parameter that indicates the seasonality of CO₂ uptake potential under prevailing temperature, was then calculated from P _E and daily average air temperature. Statistical analysis revealed that there were obvious exponential relationships between NDVI and the seasonal courses for both canopy physiological activities P _E and V _rm. Among them, the on‐site broadband NDVI provided a robust and consistent relationship with canopy physiological activities (R ² = 0.84). The relationships between satellite‐borne NDVI time series with instantaneous canopy physiological activities at the time of satellite passing were also checked. The results indicate that daily step NDVI time series (data downscaled from composite temporal resolution NDVI) better represent the daily average activity of the canopy. These findings may enable us to retrieve the seasonal course of canopy physiological activity from widely available NDVI data series and, thus, to include it into carbon assimilation models. However, both smoothing methods for satellite‐borne NDVI time series may generate incorrect estimates and must be treated with care.