MODIS Normalized Difference Vegetation Index Research Articles

Use of phenological features to identify cultivated areas in Asia

Accurate discrimination between cultivated and non‐cultivated, naturally vegetated coverage in the arid and semi‐arid zone of Asia is difficult because of their similar phenological characteristics. This study proposes a method based on phenological features. Ten phenological parameters linked to vegetative growth events were extracted from 250 m resolution, in a 16‐day composite time series of MODIS Normalized Difference Vegetation Index (NDVI) data. The short length of the growing season, rapid transition of NDVI values in peak vegetative stage, and NDVI‐related indicators were the main features for cultivated area that differentiated it from non‐cultivated areas. Each coverage type showed a distinct profile. The growing season of cultivated area is one month later in the eastern area than in the western area. Phenological features derived from MODIS NDVI data can permit discrimination between cultivated and non‐cultivated, naturally vegetated areas.

Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data

Information on soil moisture is important for crop and water management. This study investigated soil moisture variability in relation to rice cropping systems in the Mekong Delta (MD), Vietnam, using MODIS data acquired from January to April, 2002 to 2007. The soil moisture was estimated using the temperature vegetation dryness index (TVDI). This index was empirically calculated by parameterizing the relationship between the MODIS land surface temperature (LST) and the normalized difference vegetation index (NDVI) data. The rice cropping systems were classified using time-series MODIS NDVI data for 2002 and 2006. The data were processed using empirical mode decomposition (EMD), principal component analysis (PCA) and maximum likelihood classification (MLC) methods. Various spatial and non-spatial data were also collected for accuracy validation of the TVDI and classification results. Comparisons reveal good agreement between the TVDI and daily rainfall data. The areas with low soil moisture were mainly distributed in coastal areas from 2002 to 2005, but expanded into the middle of the MD in 2006 and 2007. The largest area of low soil moisture was observed in 2006. Classification of rice cropping systems reveals an overall accuracy and Kappa coefficient for 2002 of 76.3% and 0.68, respectively while the values for 2006 were 76.9% and 0.69, respectively. To relate soil moisture with rice cropping systems, we aggregated the composite soil moisture maps (considering dry and very dry classes only) with the rice crop maps for 2002 and 2006. The results indicate a remarkable increase in the area of double and triple irrigated–rice cropping systems in areas of low soil moisture (i.e., dry and very dry conditions) during this period. This study demonstrated the merits of using MODIS data for studying soil variability in relation to rice crops which is important for crop and water management.

A simple ecohydrological model captures essentials of seasonal leaf dynamics in semi-arid tropical grasslands

Abstract. Modelling leaf phenology in water-controlled ecosystems remains a difficult task because of high spatial and temporal variability in the interaction of plant growth and soil moisture. Here, we move beyond widely used linear models to examine the performance of low-dimensional, nonlinear ecohydrological models that couple the dynamics of plant cover and soil moisture. The study area encompasses 400 000 km2 of semi-arid perennial tropical grasslands, dominated by C4 grasses, in the Northern Territory and Queensland (Australia). We prepared 8-year time series (2001–2008) of climatic variables and estimates of fractional vegetation cover derived from MODIS Normalized Difference Vegetation Index (NDVI) for 400 randomly chosen sites, of which 25% were used for model calibration and 75% for model validation. We found that the mean absolute error of linear and nonlinear models did not markedly differ. However, nonlinear models presented key advantages: (1) they exhibited far less systematic error than their linear counterparts; (2) their error magnitude was consistent throughout a precipitation gradient while the performance of linear models deteriorated at the driest sites, and (3) they better captured the sharp transitions in leaf cover that are observed under high seasonality of precipitation. Our results showed that low-dimensional models including feedbacks between soil water balance and plant growth adequately predict leaf dynamics in semi-arid perennial grasslands. Because these models attempt to capture fundamental ecohydrological processes, they should be the favoured approach for prognostic models of phenology.

Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia

Remote sensing of vegetation and its monitoring using the normalised difference vegetation index (NDVI) offers the opportunity to provide a coverage of agricultural land at a large scale. The availability of MODIS NDVI at a resolution of 250 m provided the opportunity to evaluate the hypothesis that pasture growth rate (PGR) of individual paddocks can be accurately predicted using a model based on MODIS NDVI in combination with climate and soil data and a light-use efficiency model. Model estimates of PGR were compared with field measurements of PGR recorded in grazing enclosure cages collected over 3 years from six farms located across the south-west region of Western Australia. The estimates attained from the model explained 70% of the variation in PGR for individual paddocks on farms over the 3 years of the study, with an average error at the paddock scale of 10.4 kg DM/ha.day over all growing seasons and years. Across all farms studied, there was generally good agreement between satellite-derived PGR and ground-based measurements, although estimates of PGR varied between years and farms. The model explained 47% of the variation in pasture growth early in the season (from break of season to end of July), compared with 62% late in the season (from August to pasture senescence). The present study demonstrated that PGR for individual paddocks can be predicted at weekly intervals from MODIS imagery, climate and soil data and a light-use efficiency model at an accuracy sufficient to facilitate on-farm pasture and livestock management.

Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data to develop an annual crop type mapping capability throughout the 480,000 km 2 Great Lakes Basin (GLB). An ecoregion-stratified approach was developed using a two-step processing approach that included an initial differentiation of cropland versus non-cropland and subsequent identification of individual crop types. Major crop types were mapped for the calendar years of 2002 and 2007. National Agricultural Statistics Service (NASS) census data were used to assess county level accuracies on a unit area basis (2002), and the NASS Crop Data Layer (C DL ) was used to generate 231,616 reference data points to support a pixel-wise assessment of the MODIS crop type classification (2007) accuracy across the US portion of the CLB. County level comparisons for 2002 indicated 2.2, ―6.8, ―6.0, and ―5.8 percent of area bias errors for corn, soybeans, wheat, and hay, respectively. Detailed pixel-wise accuracy assessments resulted in an overall crop type classification accuracy of 84 percent (Kappa = 0.73) for 2007. Kappa coefficients ranged from 0.74 to 0.69 for individual ecoregions. The user's accuracies for corn, soybean, wheat, and hay were 87, 82, 81, and 70 percent, respectively. There were spatial variations of classification performances across ecoregions, especially for soybean and hay. Field sizes had a direct impact on the variable classification performances across the GLB.

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

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

Evaluation of 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.

A simplified data assimilation method for reconstructing time-series MODIS NDVI data

The Normalized Difference Vegetation Index (NDVI) is an important vegetation index, widely applied in research on global environmental and climatic change. However, noise induced by cloud contamination and atmospheric variability impedes the analysis and application of NDVI data. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. We extracted 16-Day L3 Global 1 km SIN Grid NDVI data sets for western China from MODIS vegetation index (VI) products (MOD13A2) for the period 2003–2006. NDVI data in the first three years (2003–2005) were used to generate the background field of NDVI based on a simple three-point smoothing technique, which captures annual features of vegetation change. NDVI data for 2006 were used to test our method. For every time step, the quality assurance (QA) flags of the MODIS VI products were adopted to empirically determine the weight between the background field and NDVI observations. Ultimately, more reliable NDVI data can be produced. The results indicate that the newly developed method is robust and effective in reconstructing high-quality MODIS NDVI time-series.

봄철 SWAT 모형의 산림 토양수분과 Terra MODIS 위성영상 NDVI와의 상관성 분석

The purpose of this study is to identify how much the MODIS NDVI (Normalized Difference Vegetation Index) can explain the forest soil moisture simulated from SWAT (Soil and Water Assessment Tool) model. For ChungjuDam watershed () which covers 82.2% of forest, the SWAT model was calibrated for four years (2003-2006) at two locations of the watershed using daily streamflow data and was verified for three years (2000-2002) with average Nash and Sutcliffe model efficiencies of 0.69 and 0.75 respectively. For the period from March to June, the average spatial correlation between 16 days composite MODIS NDVI and the corresponding SWAT forest soil moisture was 0.90. The two variables averaged for each data set during that period showed an inverse relation with the average coefficient of determination of 0.55.

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer (AVHRR)‐Normalized Difference Vegetation Index (NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non‐stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor‐specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at 1° is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.

Linking variability in species composition and MODIS NDVI based on beta diversity measurements

Finding an effective method to quantify species compositional changes in time and space has been an important task for ecologists and biogeographers. Recently, exploring regional floristic patterns using data derived from satellite imagery, such as the normalized difference vegetation index (NDVI) has drawn considerable research interests among ecologists. Studies have shown that NDVI could be a fairly good surrogate for primary productivities. In this study, we used plant distribution data in the North and the South Carolina states to investigate the correlations between species composition and NDVI within defined ecoregions using Mantel test and multi-response permutation procedure (MRPP). Our analytical approach involved generating compositional dissimilarity matrices by computing pairwise beta diversities of the 145 counties in the two states for species distribution data and by computing Euclidian distances for NDVI time series data. We argue that beta diversity measurements take the pairwise dissimilarities into consideration explicitly and could provide more spatial correlation information compared with uni- or multi-dimensional regressions. Our results showed a significant positive correlation between species compositional dissimilarity matrices and NDVI distance matrices. We also found for the first time that the strength of correlation increased at a lower taxonomic rank. Same trends were discovered when incorporating variability in phenological patterns in NDVI. Our findings suggest that remotely sensed NDVI can be viable for monitoring species compositional changes at regional scales.

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements.

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps.

Removal of Noise by Wavelet Method to Generate High Quality Temporal Data of Terrestrial MODIS Products

Time-series terrestrial parameters derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA, or AQUA/MODIS data, such as Normalized Difference Vegetation Index (NDVI), Leaf Index Area (LAI), and Albedo, have been extensively applied to global climate change. However, the noise impedes these data from being further analyzed and used. In this paper, a wavelet-based method is used to remove the contaminated data from time-series observations, which can effectively maintain the temporal pattern and approximate the true signals. The method is composed of two steps: (a), time-series values are linearly interpolated with the help of quality flags and the blue band, and (b), time series are decomposed into different scales and the highest correlation among several adjacent scales is used, which is more robust and objective than the threshold-based method. Our objective was to reduce noise in MODIS NDVI, LAI, and Albedo time-series data and to compare this technique with the BISE algorithm, Fourier-based fitting method, and the Savitzky-Golay filter method. The results indicate that our newly developed method enhances the ability to remove noise in all three time-series data products.

Mapping and assessing water use in a Central Asian irrigation system by utilizing MODIS remote sensing products

Spatial and temporal patterns of water depletion in the irrigated land of Khorezm, a region located in Central Asia in the lower floodplains of the Amu Darya River, were mapped and monitored by means of MODIS land products. Land cover and land use were classified by using a recursive partitioning and regression tree with 250 m MODIS Normalized Difference Vegetation Index (NDVI) time series. Seasonal actual evapotranspiration (ETact) was obtained by applying the Surface Energy Balance Algorithm for Land (SEBAL) to 1 km daily MODIS data. Elements of the SEBAL based METRIC model (Mapping Evapotranspiration at high Resolution and with Internalized Calibration) were adopted and modified. The upstream–downstream difference in irrigation was reflected by analyzing agricultural land use and amounts of depleted water (ETact) using Geographical Information Systems (GIS). The validity of the MODIS albedo and emissivity used for modeling ETact was assessed with data extracted from literature. The r 2 value of 0.6 indicated a moderate but significant association between ETact and class-A-pan evaporation. Deviations of ETact from the 10-day reference evapotranspiration of wheat and cotton were found to be explainable. In Khorezm, seasonal maximum values superior to 1,200 and 1,000 mm ETact were estimated for rice and cotton fields, respectively. Spatio-temporal comparisons of agricultural land use with seasonal ETact disclosed unequal water consumption in Khorezm. Seasonal ETact on agricultural land decreased with increasing distance to the water intake points of the irrigation system (972–712 mm). Free MODIS data provided reliable, exhaustive, and consistent information on water use relevant for decision support in Central Asian water management.

Relationships between Moderate Resolution Imaging Spectroradiometer water indexes and tower flux data in an old growth conifer forest

Methods to accurately estimate the biophysical and biochemical properties of vegetation are a major research objective of remote sensing. We assess the capability of the MODIS satellite sensor to measure canopy water content and evaluate its relationship to ecosystem exchange (NEE) for an evergreen forest canopy. A time-series of three vegetation indexes were derived from MODIS data, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII), which were compared to physically based estimates of equivalent water thickness (EWT) from the airborne AVIRIS hyperspectral instrument over a temperate conifer forest in southwestern Washington. After cross-calibration of the imagery, water indexes derived from MODIS showed good agreement with AVIRIS EWT, while the NDVI was insensitive to water content variation. Three years of NEE data from eddy covariance measurements at the Wind River AmeriFlux tower were compared with the time series of MODIS indexes, which show seasonal water content has similar trajectory with NEE. In contrast, the MODIS NDVI time series did not yield a good relationship with NEE. This study demonstrates the potential to use MODIS water indexes for spatial and temporal NEE estimation at regional and global scales in appropriate ecosystems.

Monitoring forage production for farmers’ decision making

Objective management of grazing livestock production systems needs monitoring of forage production at the managerial unit level. Our objectives were to develop a system that routinely estimates forage above-ground net primary production (ANPP) at the spatial and temporal resolution required by farmers in the Pampas of Argentina, and to facilitate adoption of the system by end users as a managerial support tool. Our approach was based on the radiation use efficiency (RUE) logic, which proposes that ANPP is determined by the amount of photosynthetically active radiation absorbed by the canopy (APAR), and the efficiency with which that energy is transformed in above-ground dry matter (radiation use efficiency, RUE). APAR is the product of incoming photosynthetically active radiation (PAR) and the fraction absorbed by the canopy (fPAR). We estimated fPAR as a non-linear function of MODIS normalized difference vegetation index (NDVI). RUE was empirically estimated for the two principal forage resources of the region, yielding the following relations: ANPP = 0.6 × APAR + 12, ( R 2 = 0.86; p < 0.001; n = 18) for the upland sown pastures, and ANPP = 0.27 × APAR + 26, ( R 2 = 0.74; p < 0.001; n = 18) for the lowland naturalized pastures, with ANPP in g/m 2/60 days and APAR in MJ/m 2/60 days. The models were able to predict independent ANPP values with acceptable accuracy. Computational procedures were automated and run in a Relational Data Base Manager System that stored and managed all the information. The system is currently monitoring 212,794 ha in 83 farms and provides monthly ANPP values for the previous month and a history of the last 6 years. The data so generated show ANPP differences between the two major forage resources, considerable variability of a given month’s ANPP among years and paddocks, and contrasting among-farm differences in the efficiency of conversion of ANPP and forage supplements into beef production. The system was well accepted by end users who utilize it mainly for making near real time decisions according to last month ANPP, and explaining results of previous production cycles by incorporating ANPP as an explicative variable. However, there were differences among farmers in the degree of utilization, apparently related to the advisor’s attitude toward this new technology. Our results indicate that (1) forage production of large extensions can be monthly monitored at the paddock level by a small laboratory with capabilities in geographic information systems, and (2) advisors and farmers apply this information to their managerial decisions.

An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA‐9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA‐16 for 2000–2003 and NOAA‐17 for 2003–2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10‐day Africa‐only dataset. Post‐processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge‐of‐orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/gimms/).

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI–LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998–2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year. The results suggest that the NDVI–LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI–LAI relationship was found to be poor ( R 2 varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI. Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships ( R 2=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI–LAI relationships.

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350 nm and 2500 nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R 2=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750 nm) and longer wavelengths of the red edge (750–780 nm), yielded higher correlations with biomass (mean R 2=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R 2=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.

Comparison of MODIS and AVHRR 16‐day normalized difference vegetation index composite data

Normalized difference vegetation index (NDVI) data derived from visible and near‐infrared data acquired by the MODIS and AVHRR sensors were compared over the same time periods and a variety of land cover classes within the conterminous USA. The relationship between the AVHRR derived NDVI values and those of future sensors is critical to continued long term monitoring of land surface properties. The results indicate that the 16‐day composite values are quite similar over the 23 intervals of 2001 that were analyzed, and a linear relationship exists between the NDVI values from the two sensors. The composite AVHRR NDVI data were associated with over 90% of the variation in the MODIS NDVI values.