Processing of GAC NDVI data for yield forecasting in the Sahelian region

Recent investigations have demonstrated that inter-year NOAAAVHRR NDVI variations in the middle of the rainy season can provide information on final crop yield in Sahelian countries. The present work continues this line of research by the use of 10-day Global Area Coverage (GAC) NDVI Maximum Value Composites, which are widely available and cost-effective in Africa. This use actually posed some problems which were mitigated by a multistep methodology aimed at forecasting millet and sorghum yield in Niger. The soil effect was first minimized in the NDVI images, and a geographical standardization was applied to the sub-district mean NDVI values and to the relevant ground yield estimates in order to remove most of the noninteresting information related to variations in land resources. A correlation analysis on the data obtained showed that the best period for yield forecasting was from the end of August to the middle of September. A further improvement in the forecasting capability of the procedure was then achieved by an image-based statistical identification of the most intensively cultivated areas. The final result of the complete methodology was the forecast of crop yield within the middle of September with an acceptable level of accuracy (mean error of 72 kg ha^-1).     

Operational maize yield model development and validation based on remote sensing and agro‐meteorological data in Kenya

Remote‐sensing data acquired by satellite have a wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near‐infrared remote‐sensing data have been extensively used for crop characterization, biomass estimation, and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant‐water stress. This paper reports the development of an operational spectro‐agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOT‐VEGETATION, meteorological data obtained from the European Centre for Medium‐Range Weather Forecast (ECMWF) model, and crop‐water status indicators estimated by the Crop‐Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted, and production were used in the model. The statistical multiple regression linear model has been developed for six large maize‐growing provinces in Kenya. The spectro‐agrometerological yield model was validated by comparing the predicted province‐level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model, the actual evapotranspiration (ETA) performs best. CNDVI and ETA in the model explain 83% of the maize crop yield variance with a root square mean error (RMSE) of 0.3298 t ha^?1. Very encouraging results were obtained when the Jack‐knife re‐sampling technique was applied, thus proving the validity of the forecast capability of the model (r ² = 0.81 and RMSE = 0.359 t ha^?1). The optimal prediction capability of the independent variables is 20 days and 30 days for the short and long maize crop cycles, respectively. The national maize production during the first crop season for the years 1998–2003 was estimated with an RMSE of 185 060 t and coefficient of variation of 9%.     

Environmental monitoring and crop forecasting in the Sahel through the use of NOAA NDVI data. A case study: Niger 1986–89

Abstract

Several investigations have shown that NOAA NDVI data accumulated during a rainy season can be related to total rainfall or final primary productivity in the Sahel. However, serious problems can arise when looking for quantitative relations to monitor and forecast crop yield from NDVI values. Geographical variability can affect such relations, while the use of data taken from a whole season is impractical for forecasting. The present paper proposes a complete methodology of NDVI data processing which only utilizes NOAA AVHRR scenes from the first part of successive rainy seasons. A series of basic corrections are first applied to the original data to obtain reliable NDVI maximum value composites at the middle of the rainy seasons considered. Next, the variability in land resources is accounted for by means of a standardization process which normalizes the mean NDVI levels of some areas on the relevant multi-temporal averages and standard deviations. In this way, good estimates of the actual condition of vegetation can be obtained in relation to the local seasonal trend

The methodology was applied to the Sahelian sub-departments of Niger with data from four years (1986–1989). The most interesting result achieved concerns the estimation of final grain (millet and sorghum) yield for the sub-departments by the end of July with a mean error of about 0·08 T ha ^?1. This timely evaluation could be of great utility in the context of an efficient drought early warning system.     

Natural vegetation responses to warming climates in Qaidam Basin 1982–2003

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April–September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982–2003 and in 24.1% over the first half period, 1982–1992. In addition, few areas were shown to have negative trends during these periods. In 1993–2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May–August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions.     

NDVI response to rainfall in different ecological zones in Jordan

The response of NDVI to rainfall was analyzed using NOAA/AVHRR satellite imagery acquired over a time period of ten growing seasons (1981 to 1992) and rainfall data from 16 weather stations in four ecological zones in Jordan. Results of linear regression analysis showed better response of NDVI to cumulative rainfall than to 10-day rainfall with best correlation in the Mediterranean zone. Significant relationships were found between seasonal rainfall and NDVI range (Δ_NDVI) with better correlations for logarithmic and power relationships than for linear relationship. A strong linear relationship occurred between the annual rainfall and end-of-season NDVI in the Mediterranean zone and weak or no correlation in other zones. The correlations were improved when the rainfall data were averaged, summed and correlated with the average NDVI. More agreement, however, was observed between the maximum NDVI image and rainfall than for the average NDVI image and rainfall. Results also showed that stratification of the data according to soil type and/or land cover would not necessarily improve the correlation. However, stratification of the data according to ecological zone demonstrated obvious differences between the NDVI-rainfall in the different zones.     

Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-à-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.     

Image masking for crop yield forecasting using AVHRR NDVI time series imagery

One obstacle to successful modeling and prediction of crop yields using remotely sensed imagery is the identification of image masks. Image masking involves restricting an analysis to a subset of a region's pixels rather than using all of the pixels in the scene. Cropland masking, where all sufficiently cropped pixels are included in the mask regardless of crop type, has been shown to generally improve crop yield forecasting ability, but it requires the availability of a land cover map depicting the location of cropland. The authors present an alternative image masking technique, called yield-correlation masking, which can be used for the development and implementation of regional crop yield forecasting models and eliminates the need for a land cover map. The procedure requires an adequate time series of imagery and a corresponding record of the region's crop yields, and involves correlating historical, pixel-level imagery values with historical regional yield values. Imagery used for this study consisted of 1-km, biweekly AVHRR NDVI composites from 1989 to 2000. Using a rigorous evaluation framework involving five performance measures and three typical forecasting opportunities, yield-correlation masking is shown to have comparable performance to cropland masking across eight major U.S. region-crop forecasting scenarios in a 12-year cross-validation study. Our results also suggest that 11 years of time series AVHRR NDVI data may not be enough to estimate reliable linear crop yield models using more than one NDVI-based variable. A robust, but sub-optimal, all-subsets regression modeling procedure is described and used for testing, and historical United States Department of Agriculture crop yield estimates and linear trend estimates are used to gauge model performance.     

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r² values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI “cancellation effect” where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes.     

Use of NDVI/AVHRR time-series profiles for soybean crop monitoring in Brazil

In Brazil there is a need for less subjective, more efficient and less expensive methodologies for crop yield forecast. Owing to the continental dimensions of the country, orbital images have been used to estimate the productive potential of crops. In this study, NDVI (Normalized Difference Vegetation Index) time-series, derived from AVHRR/NOAA (Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration) imagery were used for the soybean crop monitoring in a large production region in Brazil in the 2002/2003 and 2003/2004 cropping seasons. NDVI temporal profiles describing the biomass condition of crops throughout the phenological stages were generated in 18 municipalities. Quantitative parameters were measured from the temporal profiles, based on the full time or partial phenological cycle. Linear regressions between the quantitative parameters and the municipal average yields in both seasons have shown that the most significant correlations occurred when the full time period was considered. When considering periods prior to harvest, the correlations showed a tendency to decline. The NDVI monitoring during these two cropping seasons, which presented different weather conditions, could explain a major part of the soybean yield variability at the municipal level. Results showed the potential of the NDVI time-series analysis in generating parameters to be employed by agrometeorological–spectral models for soybean yield estimations. The automatic system for temporal profiles generation developed in this study sped up the analysis and can be used for further studies at a regional scale.     

Biophysical modelling and NDVI time series to project near‐term forage supply: spectral analysis aided by wavelet denoising and ARIMA modelling

Point‐based biophysical simulation of forage production coupled with 1‐km AVHRR NDVI data was used to determine the feasibility of projecting forage conditions 84 days into the future to support stocking decision making for livestock production using autoregressive integrated moving average (ARIMA) with Box and Jenkins methodology. The study was conducted at three highly contrasting ecosystems in South Texas over the period 1989–2000. Wavelet transform was introduced as a mathematical tool to denoise the NDVI time series. The simulated forage production, NDVI and denoised NDVI (DeNDVI) were subject to spectral decomposition for the detection of periodicities. Spectral analysis revealed bimodal vegetation growth patterns in Southwestern Texas. A yearly cycle (364 days) of peak vegetation production was detected for the three study sites, another peak forage production was revealed by spectral analysis at 182 days following the first peak in vegetation production. A similar trend was found for the NDVI imageries sensing the study sites. Wavelet denoising of NDVI signal was effective in revealing clear periodicities in one study site where maximum variability of NDVI was noted.

The Box and Jenkins ARIMA modelling approach was used as a forecasting method for near‐term forage production to assist range managers in proactive operational stocking decisions to mitigate drought risk. Using denoised NDVI provided forage projections with the lowest standard error prediction (SEP) throughout the forecast 84‐day periods. However, acceptable SEP was only achieved up to 6 weeks into a projection for the forage‐only based forecasts. The ARIMA forecasting methodology appears to offer a new approach to help managers of livestock production through the creation of near real‐time early warning systems. Using satellite‐derived NDVI data as a covariate improved the forecast quality and reduced the standard error of forecast in three highly contrasting sites. Denoising the NDVI data using wavelet methods further improved the forecast quality in all study sites.

The integration of AVHRR NDVI data and biophysical simulation of forage production appears a promising approach for assisting decision makers in a positive manner by assessing forage conditions in response to emerging weather conditions and near real‐time projection of available forage for grazing animals.     

Estimating maize production in Kenya using NDVI: Some statistical considerations

A regression model approach using a normalized difference vegetation index (NDVI) has the potential for estimating crop production in East Africa. However, before production estimation can become a reality, the underlying model assumptions and statistical nature of the sample data (NDVI and crop production) must be examined rigorously. Annual maize production statistics from 1982-90 for 36 agricultural districts within Kenya were used as the dependent variable; median area NDVI (independent variable) values from each agricultural district and year were extracted from the annual maximum NDVI data set. The input data and the statistical association of NDVI with maize production for Kenya were tested systematically for the following items: (1) homogeneity of the data when pooling the sample, (2) gross data errors and influence points, (3) serial (time) correlation, (4) spatial autocorrelation and (5) stability of the regression coefficients. The results of using a simple regression model with NDVI as the only independent variable are encouraging (r 0.75, p 0.05) and illustrate that NDVI can be a responsive indicator of maize production, especially in areas of high NDVI spatial variability, which coincide with areas of production variability in Kenya.     

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.     

BMDP program for piecewise linear regression

Piecewise linear regression has potentially broad applications in medical data analysis as well as other types of regression. Various kinds of algorithms have been proposed for finding optimum piecewise linear regressions. This paper presents a BMDP program for obtaining near optimum piecewise linear regression equations. An idea intrinsic to the method is that restricting parameter space to a discrete set makes the difficult problems become standard problems. Any software having the variable selection feature in the multiple linear regression can be used to apply the method.     

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.     

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Sugarcane is a semi-perennial grass whose cultivation is characterized by an extended harvest season lasting several months leading to very high spatio-temporal variability of the crop development and radiometry. The objective of this paper is to understand this variability in order to propose appropriate spectral indicators for yield forecast. To do this, we used ground observations and Satellite Pour l‘Observation de la Terre (SPOT4) and SPOT5 time series acquired monthly over a 2-year period over Reunion Island and Guadeloupe (French West Indies). We showed that variations in the Normalized Difference Vegetation Index (NDVI) of sugarcane at the field scale are the result of the interaction between the sugarcane crop calendar and plant phenology in a given climatic environment. We linked these variations to crop variables measured in the field (leaf area index and leaf colour), and derived simple, appropriate NDVI-based indicators of sugarcane yield components at the field scale (cane yield and sugar content). For biomass forecast, the best correlation (R² = 0.78) was obtained with images acquired about 2 months before the harvest season, when all the fields are fully developed but before the maturation stage. For sugar content, a polynomial relationship (R² = 0.75) was observed between the field NDVI acquired during the maturation stage and sugar content in the stalk.     

Relaxation vegetation index in non-linear modelling of ground plant cover by satellite remote-sensing data

Abstract

In order to obtain a model equation for the calculation of percentage plant cover by multi-spectral radiances remotely-sensed by satellites, a regression procedure is used to connect space remote-sensing data to ground plant cover measurement. A traditional linear regression model using the normalized difference vegetation index (NDVI) is examined by remote-sensing data of the SPOT satellite and ground measurement of LCTA project for a test site at Hohenfels. Germany. A relaxation vegetation index (RVI) is proposed in a non-linear regression modelling to replace the NDVI in linear regression modelling to get a better calculation of percentage plant cover. The definition of the RVI is

where X _i is raw remote-sensing data in channel i. Using the RVI, the correlation coefficient between calculated and observed percentage plant cover for a test scene in 1989 reaches 0·9 while for the NDVI it is only 0·7; the coefficient of multiple determination R ² reaches 0·8 for the RVI while it is only 0·5 for the NDVI. Numerical testing shows that the ability of using the RVI to predict percentage plant cover by space remote-sensing data for the same scene or the scene in other years is much stronger than the NDVI.     

Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh )India)

The area under wheat was estimated and a forecast of production made in a predominantly un-irrigated region (36 per cent irrigated wheal crop, geographical area 5-61 Mha) of Madhya Pradesh (India) using digital data from LISS-I (Linear Imaging Self Scanner) onboard Indian Remote Sensing Satellite (IRS-IB), for the crop season 1991-92. A stratified sampling approach based on 5 km by 5 km sample segments, 10 per cent sampling fraction in conjunction with supervised maximum likelihood (MXL) classification was used for wheat acreage estimation. Yield forecasts were based on an optimal combination of forecasts from two different methodologies, viz., wheat yield-spectral relationship and time series analysis using ARIMA (Auloregressive Integrated Moving Average) approach. In the former, a two-year (1989-90, 1990-91) pooled regression relating LISS-I derived Near Infrared/Red (NIR/R) radiance ratio to district wheat yields was developed and used to forecast wheat yields for the year 1991-92 based on classified wheat pixels. In the latter case, historical district-wise wheat yield data of 35 years was used to develop appropriate ARIMA models and used to forecast 1991-92 yields. The relative deviation of remotely-sensed-based forecasted production, acreage and yield from the post-harvest estimates released later by the State Department of Agriculture were — 15.8, — 1002 and — 601 per cent, respectively. The acreage and yield meet the accuracy of 85 per cent at 90 and 95 per cent confidence levels, respectively.     

A review of “ Remote Sensing of Sea Ice” and Icebergs,Edited by S. Haykin,E. O. Lewis,R. K. Raney and J. Rossiter (New York: John Wiley & Sons,Inc., 1994) [ Pp.686] ISBN 0471 554944. Price £ 73, $ 110.

Operational millet yield forecast has been conducted in Senegal using AVHRR data. This was possible by establishing a linear relation between yield collected in the field and NDVI integrated during the reproductive period of millet growth. A bio-physical frame was adapted in order to understand and reduce inter-annual and environmental variability. This was done by limiting potential variation of plant physiological parameters by accounting for vegetation complexity and differences in productivity: (1) Subtracting a pre-growing season NDVI reference level from the NDVI integral improved significantly the level of explained yield variance and was interpreted to reduce the influence of non-crop vegetation within the agricultural domain. (2) Soil and vegetation maps were used to identify areas of homogeneous environmental production conditions. Using this information along with the NDVI integral, accorded a millet yield regression model with a correlation coefficient of r2 0.72 and a standard error of estimate of 190kg ha 1. Yield data could be available one month before the harvest.     

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.     

A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.