The effect of water vapour on the normalized difference vegetation index derived for the Sahelian region from NOAA AVHRR data

Abstract

The near-infrared channel of the NOAA advanced very high resolution radiometer (AVHRR) contains a water vapour absorption band that affects the determination of the normalized difference vegetation index (NDVI). Daily and seasonal variations in atmospheric water vapour within the Sahel are shown to affect the use of the NDVI for the estimation of primary production. This water vapour effect is quantified for the Sahel by radiative transfer modelling and empirically using observations made in Mali in 1986. In extreme cases, changes in water vapour are shown to result in a reduction of the NDVI by 0.1. Variations of the NDVI of 001 would result from typical low atmospheric water vapour days within the wet season. If these conditions were to persist throughout the season it would lead to an overestimate of production of 200?kg ha^?1. The measurement of atmospheric water vapour using the AVHRR thermal channels, the high-resolution Infrared Sounder 2 (HIRS2), and the microwave sounding unit (MSU) sensors, which are all carried on the NOAA satellites, is discussed. A procedure for operational correction of the water vapour effect on the NDVI is suggested; however, additional studies over a wider range of Sahelian conditions are recommended.     

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.     

Hurricane Georges and vegetation change in Puerto Rico using AVHRR satellite data

This study assesses natural disturbances at Puerto Rico resulting from hurricane Georges in September 1998. The study was done using data from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) 14 satellite. Specifically, the Luquillo Experimental Forest, the Guanica Dry Forest and five cities were examined using AVHRR data. It was found that although there is probably no significant relationship between Georges and the before/after temperature data, there is a statistically significant relationship between distance to the location affected by the hurricane and the extent of changes in NDVI, a fact that suggests that it is possible to measure hurricane impacts on vegetation by using AVHRR data.     

Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data

AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference vegetation Index) data is available from 1981 to present time. The global coverage 8 km resolution 15-day composite data set has been used for numerous local to global scale vegetation time series studies during recent years. Several aspects however potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. More recent NDVI data sets from both Terra MODIS and SPOT VGT data are considered an improvement over AVHRR and these products in theory provide a possibility to evaluate the accuracy of GIMMS NDVI time series trend analysis for the overlapping period of available data. In this study the accuracy of the GIMMS NDVI time series trend analysis is evaluated by comparison with the 1 km resolution Terra MODIS (MOD13A2) 16-day composite NDVI data, the SPOT Vegetation (VGT) 10-day composite (S10) NDVI data and in situ measurements of a test site in Dahra, Senegal. Linear least squares regression trend analysis on eight years of GIMMS annual average NDVI (2000-2007) has been compared to Terra MODIS (1 km and 8 km resampled) and SPOT VGT NDVI data 1 km (2000-2007). The three data products do not exhibit identical patterns of NDVI trends. SPOT VGT NDVI data are characterised by higher positive regression slopes over the 8-year period as compared to Terra MODIS and AVHRR GIMMS NDVI data, possibly caused by a change in channels 1 and 2 spectral response functions from SPOT VGT1 to SPOT VGT2 in 2003. Trend analysis of AVHRR GIMMS NDVI exhibits a regression slope range in better agreement with Terra MODIS NDVI for semi-arid areas. However, GIMMS NDVI shows a tendency towards higher positive regression slope values than Terra MODIS in more humid areas. Validation of the different NDVI data products against continuous in situ NDVI measurements for the period 2002-2007 in the semi-arid Senegal revealed a good agreement between in situ measurements and all satellite based NDVI products. Using Terra MODIS NDVI as a reference, it is concluded that AVHRR GIMMS coarse resolution NDVI data set is well-suited for long term vegetation studies of the Sahel-Sudanian areas receiving < 1000 mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian-Guinean zones should be done with certain reservations.     

Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: a statistical approach

Ground station temperature data are not commonly used simultaneously with the Advanced Very High Resolution Radiometer (AVHRR) to model and predict air temperature or land surface temperature. Technology was developed to acquire near-synchronous datasets over a 1?000?000?km² region with the goal of improving the measurement of air temperature at the surface. This study compares several statistical approaches that combine a simple AVHRR split window algorithm with ground meterological station observations in the prediction of air temperature. Three spatially dependent (kriging) models were examined, along with their non-spatial counterparts (multiple linear regressions). Cross-validation showed that the kriging models predicted temperature better (an average of 0.9°C error) than the multiple regression models (an average of 1.4°C error). The three different kriging strategies performed similarly when compared to each other. Errors from kriging models were unbiased while regression models tended to give biased predicted values. Modest improvements seen after combining the data sources suggest that, in addition to air temperature modelling, the approach may be useful in land surface temperature modelling.     

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.     

Correction of the effect of Sun-sensor-target geometry in NOAA AVHRR data

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.     

Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983

Normalized difference vegetation index data derived from the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite for the 1983 growing season for the Sahelian Zone of Niger are compared with biomass estimates derived from an empirical grassland productivity model. The model used daily rainfall data to estimate the potential biomass production for fourteen meteorological stations through the growing season. A good general correspondence (r = 0·75) was seen between the productivity model and the satellite-derived integrated NDV1, although specific differences were apparent between actual and potential biomass. The study shows the utility of high-temporal-resolution satellite data for monitoring grassland conditions at a local and regional scale and emphasizes the importance of a maximum value compositing approach to the analysis. The study also shows the potential of the satellite data for quantifying phenological characteristics of vegetation     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

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.     

Monitoring peanut contamination in Mali (Africa) using AVHRR satellite data and a crop simulation model

Peanut (Arachis hypogaea L.) is the main legume crop of Mali, West Africa. It can be contaminated by aflatoxin, a natural toxin that can develop because of drought conditions at pre‐harvest stage or because of temperature‐ and humidity‐related factors that occur during post‐harvest storage. Consumption of aflatoxin‐contaminated peanut can cause liver diseases, such as jaundice, hepatitis or cancer. We present a case study for Mali, identifying weather‐ and satellite‐based variables that could be used to indicate aflatoxin in peanut. Based on this monitoring and predicting, a warning may be issued against eating contaminated peanut to keep the public health from deteriorating. Normalized difference vegetation index (NDVI) composites, derived from advanced very high resolution radiometer (AVHRR) satellite data, were averaged for the reproductive phase of peanut and examined for their relationship with the annual peanut yield, an indicator of drought and aflatoxin. The relationship was found to be moderate (R ² = 0.56). The commencement and termination dates for the reproductive phase of peanut were determined by using a crop simulation model. Amounts of aflatoxin were measured for peanut samples collected from various locations across Mali and were found to be linked to the NDVI, total precipitation, and maximum temperature averaged over the reproductive phase of peanut.     

An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. The recent abundance of remotely sensed data, such as the daily availability of MODIS imagery, raises the issue of appropriate temporal sampling when monitoring vegetation: under‐sampling may not accurately describe the phenomenon under consideration, whilst over‐sampling would increase the cost of the project without additional benefit. The aim of this work is to estimate the optimum temporal resolution for vegetation monitoring on a nationwide scale using 250 m MODIS/Terra daily images and composites. Specific objectives include: (i) an investigation into the optimum temporal resolution for monitoring vegetation condition during the dry season on a nationwide scale using time‐series analysis of Normalized Difference Vegetation Index, NDVI, datasets, (ii) an investigation into whether this temporal resolution differs between the two major vegetation categories of natural and managed vegetation, and (iii) a quality assessment of multi‐temporal NDVI composites following the proposed optimum temporal resolution. A time‐series of daily NDVI data is developed for Greece using MODIS/Terra 250 m imagery. After smoothing to remove noise and cloud influence, it is subjected to temporal autocorrelation analysis, and its level of significance is the adopted objective function. In addition, NDVI composites are created at various temporal resolutions and compared using qualitative criteria. Results indicate that the proposed optimum temporal resolution is different for managed and natural vegetation. Finally, quality assessment of the multi‐temporal NDVI composites reveals that compositing at the proposed optimum temporal resolution could derive products that are useful for operational monitoring of vegetation.     

Characterizing post-fire recovery of fynbos vegetation in the Western Cape Region of South Africa using MODIS data

Post-fire recovery trajectories of five fynbos vegetation stands in the Western Cape Region of South Africa were characterized using moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250 m data. Indices of NDVI recovery relative to pre-fire values or values from unburnt control plots indicated full recovery within 7 years and particularly rapid recovery in the first two post-fire years. Intra-stand variability of pixel NDVIs generally increased after fires and also exhibited a rapid recovery to pre-fire conditions. While stand age was the dominant determinant of NDVI recovery, drought interrupted the recovery pathways and this effect was amplified on drier, equator-facing slopes. Post-fire recovery characteristics of fynbos NDVI were found to be similar to those documented for chaparral vegetation in California despite contrasting rainfall and soil nutrient conditions in the two regions.     

On the monitoring of land surface temperatures with the NOAA/AVHRR: Removing the effect of satellite orbit drift

To study the inter-annual variability of land surface temperature with NOAA Advanced Very High Resolution Radiometer (AVHRR) data, one must account for changes in the observed radiances due to changes in the observation time caused by satellite orbit drift (SOD). This study proposes a simple method to remove the SOD component from the AVHRR thermal IR. Spurious trends in these data should be corrected for to prevent their misidentification as real trends in the Earth's climate system and to infer more reliable conclusions from the inter-annual land surface variability studies, such as monitoring droughts. The proposed correction requires information on the observation solar zenith angle and normalized difference vegetation index for the region of interest.     

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.     

Land surface temperature variation in relation to vegetation type using MODIS satellite data in Gujarat state of India

The Moderate Resolution Imaging Spectroradiometer (MODIS) has provided an improved capability for moderate resolution land surface monitoring and for studying surface temperature variations. Surface temperature is a key variable in the surface energy balance. To investigate the temporal variation of surface temperature in relation to different vegetation types, MODIS data from 2000–04 were used, especially in the reproductive phase of crops (September–October). The vegetation types used for this study were agriculture in desert areas, rainfed agriculture, irrigated agriculture, and forest. We found that among the different vegetation types, the desert‐based agriculture showed the highest surface temperature followed by rainfed agriculture, irrigated agriculture, and forest. The variation in surface temperature indicates that the climatic variation is mostly determined by the different types of vegetation cover on the Earth's surface rather than rapid climate change attributable to climatic sources. The mean land surface temperature (LST) and air temperature (T _a) were plotted for each vegetation type from September to October during 2000 and 2004. Higher temperatures were observed for each vegetation type in 2000 as compared to 2004 and lower total rainfall was observed in 2000. The relationship between MODIS LST and T _a measurements from meteorological stations was established and illustrated that years 2000 and 2004 had a distinct climatic variability within the time‐frame in the study area. In all test sites, the study found that there was a high correlation (r = 0.80–0.98) between LST and T _a.     

Calibration of satellite radiometers and the comparison of vegetation indices

Satellite technology provides a steadily improving capability to monitor surface land use and vegetation. However, the increasing number of satellite sensors has led to a variety of spectral indices which may be used to characterize vegetation. A basis is developed for comparing results from different sensors using instrument calibration coefficients, and the derived radiances are related to reflectances, principal component variables such as greenness, and spectral vegetation indices.     

Analysis of the temporal and spatial vegetation patterns in a semi-arid environment observed by NOAA AVHRR imagery and spectral ground measurements

The borderline between Israel and Sinai is characterized by a sharp contrast that is caused by the low spectral reflectance on the Israeli side (Negev desert) and the high spectral reflectance on the bare Egyptian side (Sinai desert). This contrast across the political border has been discussed in many publications over the last two decades. In this study, satellite images acquired by NOAA Advanced Very High Resolution Radiometer (AVHRR) over a time period of 3 years (June 1995 to June 1998) were analysed. In addition, extensive field studies were carried out on the Israel side of the border. The current research shows that the reflectance values in Sinai seem to be quite stable over the entire year, however reflectance values in the Negev show a significant difference between the dry and the rainy seasons. Comparison between the AVHRR-derived Normalized Difference Vegetation Index (NDVI ) values and rainfall data from the Negev shows that the highest AVHRR-derived NDVI values occur a few weeks after the main rainfall. Field observations, based on spectrometer measurements of different surface components (bare sands, biological soil crusts, annuals, and perennials) and estimation of vegetation cover on the Israeli side of the border, show that the peak NDVI of the perennials occurs at the same time as the satellite observed peak. The spectral difference between both sides of the border during the dry season is caused by the dense cover of the higher vegetation and the biological soil crusts and by the photosynthetic activity of perennials during the dry season. The highest difference between both sides during the rainy season is caused by the photosynthetic activity and vegetation cover of the annuals and perennials.     

Exploring relationships between ENSO and vegetation vigour in the south-east USA using AVHRR data

This research explores the relationship between El Nino/Southern Oscillation (ENSO), captured by equatorial Pacific Ocean Sea Surface Temperature (SST), and interannual variation in vegetation vigour in the southeast USA, captured by Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI), for the period 1982-1992. The moving average and 'baseline' methods (anomaly from the long term mean) were used to extract interannual patterns in the NDVI signature for croplands, deciduous forests and evergreen forests. The ENSO cycle was measured using mean SST anomalies and the percentage of SST cells above certain threshold values (e.g. 1.0° C above the long term mean). The baseline method indicated a weak, yet persistent, negative correlation between ENSO warm phase events and vegetation vigour in the south-east USA. The moving average method yielded similar results but produced higher correlation values (-0.45 to-0.76, significant at the 0.01 level). Use of the 2.0° C threshold SST anomaly was found to yield the highest correlation values as it captures not only the presence but also the intensity of ENSO warm phase events. These results indicate that there is a clear and recognizable, though inconsistent, relationship between ENSO and vegetation vigour in the south-east USA.     

Comparison of SeaWiFS,MODIS‐Terra and MODIS‐Aqua in the Southern Ocean

The surface chlorophyll‐a concentrations measured by SeaWiFS, MODIS‐Terra and MODIS‐Aqua are compared in the Southern Ocean in summer 2003. The radiometers generally agree within their estimated accuracy. Residual discrepancies could be reduced by regional calibrations of the bio‐optical algorithms.