APPLICATION OF THE WEIGHTED DIFFERENCE VEGETATION INDEX 
TO SATELLITE DATA 
Dr. J.G.P.W. Clevers, Remote Sensing Researcher in Agriculture, 
Dept, of Landsurveying and Remote Sensing, Wageningen Agricultural 
University, P.O. Box 339, 6700 AH Wageningen, the Netherlands 
ABSTRACT 
The application of the weighted (near-infrared - red) difference vegetation index (WDVI) to satellite data 
is evaluated. It was described earlier (Clevers 1988a, 1989) that the WDVI, based on reflectance factors, 
can be used for estimating green leaf area index (LM). This WDVI offered a good correction for soil 
background (soil moisture) in estimating the LAI of e.g. cereals. 
With satellites, digital numbers instead of reflectance factors are obtained. A procedure for applying 
the WDVI to satellite data is described: (1) offset correction, (2) calculating the slope of the soil line 
and (3) calculating the WDVI for satellite data (WDVI sat ). The WDVI sat is a two-dimensional analogue of 
the Kauth-Thomas Greenness. The results of an analysis of a TM scene of the Flevoland agricultural 
area in the Netherlands, recorded 22 August 1984, shows little difference between images of the WDVI 
(based on TM bands 3 and 4) and the Kauth-Thomas Greenness. A training set yielded a linear correlation 
coefficient of 0.997 between WDVI and Greenness. Application of the WDVI concept induces a considerable 
simplification of data analysis. 
Atmosheric correction is indispensable for a multitemporal analysis of optical satellite data. The above 
procedure is extended in order to derive the WDVI in a multitemporal analysis based on surface information 
only (e.g. water, bare soil and urban areas). 
KEY WORDS: Weighted Difference Vegetation Index, Satellite data, Greenness. 
1. INTRODUCTION 
Application of remote sensing techniques has 
the potential to provide information about 
agricultural crops quantitatively, 
instantaneously and, above all, non- 
destructively. During the past decades 
knowledge about remote sensing techniques and 
their application to fields such as 
agriculture has improved considerably. Bunnik 
(1978) demonstrated the possibilities of 
applying remote sensing in agriculture, 
particularly with regard to crop charac 
teristics such as soil cover and leaf area 
index (LAI). LAI is defined as the total 
one-sided green leaf area per unit soil area 
and it is regarded as a very important plant 
characteristic because photosynthesis takes 
place in the green plant parts. The LAI is 
also a main driving variable in many crop 
growth models, designed for yield prediction 
(Penning de Vries & Laar, 1982). Crop growth 
models describe the relation between physio 
logical processes in plants and environmental 
factors such as solar radiation, temperature 
and water and nutrient availability. 
Estimates of crop growth often are inaccurate 
for sub-optimal growing conditions. Remote 
sensing may yield information about the 
actual status of a crop (e.g., in terms of 
LAI), resulting in an improvement of crop 
growth modelling. 
At the beginning of the growing season, 
soil reflectance influences the relation 
between measured infrared reflectance and 
LAI. At low soil cover, soil reflectance 
contributes strongly to the composite 
canopy-soil reflectance in the different 
spectral bands. Soil moisture content is not 
constant during the growing season and 
differences in soil moisture content greatly 
influence soil reflectance. 
At the end of the growing season, annual 
agricultural plants will show signs of 
senescence. Leaves turn from green to yellow. 
This phenomenon starts when the LAI is at its 
maximum value. In cereals all the leaves have 
appeared by that moment and the ears are 
about to appear. Subsequently, both LAI and 
photosynthetic activity decrease, because 
only the green parts will be photosynthe- 
tically active. During this stage it is 
important to gain an impression of the speed 
of senescence and to estimate LAI. Yellow 
leaves will also influence the relation 
between measured infrared reflectance and 
(green) LAI. 
If a multitemporal analysis of remote 
sensing data is required, a correction has to 
be made for background when ascertaining the 
relation between infrared reflectance factors 
and LAI. First of all, attention will be 
focussed on correction for soil background 
(Clevers 1988a, 1989). Clevers (1990) has