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     

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.     

Monitoring East African vegetation using AVHRR data

NOAA Advanced Very High Resolution Radiometer satellite data are applied to regional vegetation monitoring in East Africa. Normalized Difference Vegetation Index (NDVI) data for a one-year period from May 1983 are used to examine the phenology of a range of vegetation types. The integrated NDVI data for the same period are compared with an ecoclimatic zone map of the region and show marked similarities. Particular emphasis is placed on quantifying the phenology of the Acacia Commiphora bushlands. Considerable variation was found in the phenology of the bushlands as determined by the satellite NDVI, and is explained through the high spatial variability in the distribution of rainfall and the resulting green-up of the vegetation. The relationship between rainfall and NDVI is further examined for selected meteorological stations existing within the bushland. A preliminary estimate is made of the length of growing season using an NDVI thresholding technique     

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     

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.     

Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data

National Oceanic and Atmospheric Administration (NOAA) satellite data from the Advanced Very High Resolution Radiometer (AVHRR) sensor were analysed to document the vegetation biomass dynamics associated with the regional desert-locust upsurge in West Africa during 1980/81, which affected an area of some 600 000 km² in Mali, Niger and Algeria. Comparisons were made among locust population survey reports, rainfall records from eighteen stations in the same area, and the satellite data in vegetation index format. The satellite-recorded temporal and spatial distributions of desert vegetation biomass were closely correlated with both the locust population surveys and the available rainfall data. An attempt was made to develop a quantitative relationship between a satellite-derived potential breeding activity factor (PBAF) and the observed desert locust populations. Analysis of the multitemporal satellite data set indicates that, had the NOAA/AVHRR vegetation index data been operationally available in June 1980, effective preventive control measures would have only been necessary for an area of 600 km².     

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.     

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.     

Global analysis of empirical relations between annual climate and seasonality of NDVI

This paper describes the use of satellite data to calibrate a new climate vegetation greenness relation for global change studies. We examined statistical relations between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our understanding of intra-annual patterns and global controls on natural vegetation dynamics. Multiple linear regression results using global 1 gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80% of the geographical variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from 1 grid cells mapped as greater than 25% inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.     

Lowest order correction for solar zenith angle to Global Vegetation Index (GVI) data

Abstract

NOAA produces vegetation indices as part of a project to develop the uses of meteorological satellite data for global agricultural monitoring (Henderson-Sellers et al. 1986). However, no consideration is given to the variability of vegetation indices with the solar zenith angle. This paper focuses on this particular issue. A brief summary of an inversion technique is presented in which raw values of the normalized difference vegetation indices (NDVIs) for a variety of surface-cover types are simulated as a function of solar zenith angle. A relationship between a change in NDVI and solar zenith angle is presented. This relation is used to correct global vegetation index (GVI) data. The results show that for NOAA-7 and NOAA-9 data there is little correction in the neighbourhood of the equator (± 10") but the amount of correction increases with increasing latitude. Such corrections are also shown to be important in data comparison and integration. For example, in comparing the NOAA-6- and NOAA-8-derived NDVI with that derived from NOAA-7 and NOAA-9 for a given date and location the solar zenith angle correction is important.     

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.     

Satellite remote sensing of total herbaceous biomass production in the senegalese sahel: 1980–1984

NOAA-6 and NOAA-7 1-km and 4-km advanced very high resolution radiometer data were obtained at frequent intervals during the 1980, 1981, 1982, 1983, and 1984 rainy or growing seasons in the Sahel zone of northern Senegal. Above-ground herbaceous biomass clippings, visual estimates, and hand-held radiometer measurements of herbaceous vegetation were made during and at the conclusion of the rainy seasons for 4 of the 5 years. The satellite data were compared to sampled above-ground biomass data and the integral of the satellite data over time was compared to end-of-growing-season above-ground total dry biomass. A strong correlation between the integrated NOAA-7 satellite data and end-of-season above-ground dry biomass was found for ground samples collected over a 3-year period. The satellite data documented the highly variable precipitation regime in the Senegalese Sahel both within years and among years and suggest a direct method of monitoring Sahelian total herbaceous biomass production in areas where the percentage cover of woody species is less than 10%. Predicted average total dry biomass production was 1093 kg/ha for 1981, 536 kg/ha for 1982, 178 kg/ha in 1983, and 55 kg/ha in 1984 for the $\text{～ 30,000 km}{}^2$ study area.     

Estimating relations between AVHRR NDVI and rainfall in East Africa at 10-day and monthly time scales

Temporal relations between AVHRR NDVI and rainfall data over East Africa at 10-day and monthly time scales have been analysed using distributed lag models. On average, only 10 per cent of the variation in 10-day NDVI values could be explained by concurrent and preceding rainfall. Corresponding values for monthly data was 36 per cent. If it is assumed that rainfall data can be used as an indicator of vegetation development the study indicates that AVHRR NDVI may have limitations for temporal vegetation monitoring in these environments.     

Temporal and spatial vegetation cover changes in Israeli transition zone: AVHRR-based assessment of rainfall impact

The ability of NOAA/AVHRR data to monitor vegetation response to rainfall in three different vegetation zones was assessed along a north-south transect in Israel. The NDVI database was developed from atmospherically- and radiometrically-corrected NDVI composites from observations spanning three years. Three vegetation zones, Mediterranean region, transition zone, and semiarid region, were geographically separated by means of NDVI values. Based on three years of AVHRR observations during a relatively dry year and two years with near average rainfall, the phenological characteristics for all three vegetation zones were very similar and stable. The results showed that only a few AVHRR observations are necessary to monitor the seasonal and spatial variability of vegetation cover in different climatic zones located in Israel. The NDVI of the Israeli transition zone was found to be very sensitive to rainfall. The difference between maximum and minimum NDVI values in rainy season in the transition zone was at least two times higher than that in the Mediterranean and the semiarid regions. This phenomenon can be used as an indicator of any environmental changes in this region.     

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.     

Using the NDVI as auxiliary data for rapid quality assessment of rainfall estimates in Africa

Rainfall estimates derived from satellite imagery and global circulation models are frequently used for vegetation monitoring in many areas of Africa because of the shortage of observed rainfall data and the sparse network of meteorological stations. At the same time, this scarce density of rain gauge stations makes the calibration and validation of the modelled data nearly impossible. In this study we propose a methodology for a rapid quality assessment of rainfall estimates that is based on the well-known relationship between rainfall and the Normalized Difference Vegetation Index (NDVI). The results clearly confirm that the NDVI can be used as an indicator of the quality of rainfall estimates at the continental/regional scale and allow a rapid detection of major over- and underestimations of the two rainfall datasets examined for the African continent.     

The influence of temperature and precipitation climate regimes on vegetation dynamics in the US Great Plains: a satellite bioclimatology case study

Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data are widely used in global‐change research, yet relationships between the NDVI and ecoclimatological variables are not fully understood. This study attempts to model climate‐driven vegetation dynamics through the integration of satellite‐derived NDVI data with climate data collected from ground‐based meteorological stations in the US Great Plains. Monthly maximum value composites of NDVI data (8‐km resolution) and monthly temperature and precipitation records from 305 stations were collected from 1982 to 2001. Analyses involving deseasonalized datasets supported temperature as the dominant climate regime, demonstrating a higher average NDVI–temperature correlation (r = 0.73) than the NDVI–precipitation relationship (r = 0.38). Cluster analysis was used to develop a climate regionalization scheme based primarily on temperature, and NDVI characteristics of each subregion were compared. In the context of global climate change, findings from this study emphasize the influence of temperature and precipitation variability over vegetation cover in the Great Plains region.     

Systematic corrections of AVHRR image composites for temporal studies

For quantitative studies of vegetation dynamics, satellite data need to be corrected for spurious effects. In this study, we have applied several changes to an earlier advanced very high resolution radiometer (AVHRR) processing methodology (ABC3; [Remote Sens. Environ. 60 (1997) 35; J. Geophys. Res.-Atmos. 102 (1997) 29625; Can. J. Remote Sens. 23 (1997) 163]), to better represent the various physical processes causing contamination of the AVHRR measurements. These included published recent estimates of the NOAA-11 and NOAA-14 AVHRR calibration trajectories for channels 1 and 2; the best available estimates for the water vapour, aerosol and ozone amounts at the time of AVHRR data acquisition; an improved bidirectional reflectance algorithm that also takes into consideration surface topography; and an improved image screening algorithm for contaminated pixels. Unlike the previous study that compared the composite images to a single-date AVHRR image, we employed coincident TM images to approximate the AVHRR pixel field of view during the data acquisition. Compared to ABC3, the modified procedure ABC3V2 was found to improve the accuracy of AVHRR pixel reflectance estimates, both in the sensitivity (slope) of the regression and in r². The improvements were especially significant in AVHRR channel 1. In comparison with reference values derived from two full TM scenes, the corrected AVHRR surface reflectance estimates had average standard errors values of ±0.009 for AVHRR C1, ±0.019 for C2, and ±0.04 for NDVI; the corresponding r² values were 0.55, 0.80, and 0.50, respectively. The changes in ABC3V2 were not able to completely remove interannual variability for land cover types with little or no vegetation cover, which would be expected to remain stable over time, and they increased the interannual variability of mixed forest and grassland. These results are attributed to a combination of increased sensitivity to interannual dynamics on one hand, and the inability to remove all sources of noise for barren or sparsely vegetated northern land cover types on the other.     

NOAA AVHRR and its uses for rainfall and evapotranspiration monitoring

Abstract

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRJT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.     

Drought assessment and monitoring through remote sensing and GIS in western tracts of Tamil Nadu,India

Drought is an insidious hazard of nature and is considered to be the most complex but least understood of all natural hazards. Large historical datasets are required to study drought and these involve complex interrelationships between climatological and meteorological data. Rainfall is an important meteorological parameter; the amount and distribution influence the type of vegetation in a region. To analyse the changes in vegetation cover due to variation in rainfall and identify the land-use areas facing drought risk, rainfall data from 1981 to 2003 were categorized into excess, normal, deficit and drought years. The Advanced Very High Resolution Radiometer (AVHRR) sensor's composite dataset was used for analysing the temporal and interannual behaviour of surface vegetation. The various land-use classes – crop land (annual, perennial crops), scrub land, barren land, forest land, degraded pasture and grassland – were identified using satellite data for excess, normal, deficit and drought years. Normalized Difference Vegetation Indices (NDVIs) were derived from satellite data for each land-use class and the highest NDVI mean values were 0.515, 0.436 and 0.385 for the tapioca crop in excess, normal and deficit years, respectively, whereas in the drought year, the groundnut crop (0.267) showed the maximum. Grassland recorded the lowest value of NDVI in all years except for the excess year. Annual crops, such as groundnut (0.398), pulses (0.313), sorghum (0.120), tapioca (0.436) and horse gram (0.259), registered comparatively higher NDVI values than the perennial crops for the normal year. The Vegetation Condition Index (VCI) was used to estimate vegetation health and monitor drought. Among land-use classes, the maximum VCI value of 92.1% was observed in onions for the excess year, whereas groundnut witnessed the maximum values of 78.2, 64.5 and 55.2% for normal, deficit and drought years, respectively. Based on the VCI classification, all land-use classes fall into the optimal or normal vegetation category in excess and normal years, whereas in drought years most of the land-use classes fall into the drought category except for sorghum, groundnut, pulses and grasses. These crops (sorghum 39.7%, groundnut 55.2%, pulses 38.5% and grassland 38.6%) registered maximum VCI values, revealing that they were sustained under drought conditions. It is suggested that the existing crop pattern be modified in drought periods by selecting the suitable crops of sorghum, groundnut and pulses and avoiding the cultivation of onion, rice and tapioca.