1175 
SYNERGY BETWEEN OPTICAL AND MICROWAVE REMOTE SENSING 
FOR CROP GROWTH MONITORING 
H.J.C. van LEEUWEN and J.G.P.W. CLEVERS 
Wageningen Agricultural University 
Dept, of Landsurveying and Remote Sensing 
P.O. Box 339, 6700 AH Wageningen, The Netherlands 
ABSTRACT 
An approach for the combined use of optical and microwave data for crop growth monitoring is investigated. 
In this approach a simple reflectance model is used for estimating LAI from optical data and a simple backscatter 
model is used for estimating LAI from radar data. Subsequently, the synergetic effect of using both optical and 
radar data for estimating LAI is analysed by studying different scenarios for data acquisition. Finally, the LAI 
estimates during the growing season are used for calibrating a crop growth model. This concept for crop growth 
monitoring will be elucidated and illustrated with examples using groundbased and airborne data obtained during 
the MAC Europe 1991 campaign and previous campaigns. 
KEYWORDS: Crop Growth Monitoring, Direct and Inverse modelling, Synergy. 
1 - INTRODUCTION 
In European agriculture knowledge of crop production at an early stage is very important at both national and 
regional level. The two constituents of crop production are crop acreage and crop yield. In order to estimate or 
predict crop yield, best results are obtained if the growth of the crops is being monitored during the growing 
season. The crop variable leaf area index (LAI) is important as a measure for crop growth. 
Crop growth can be monitored by using crop growth models. However, estimates of crop growth often are 
inaccurate for non-optimal growing conditions. Remote sensing can provide information on the actual status of 
agricultural crops, thus calibrating the growth model for actual growing conditions. Best results are obtained by 
using (reflective) optical remote sensing data (e.g. some vegetation index) in estimating the LAI regularly during 
the growing season and subsequently calibrating the growth model on time-series of estimated LAIs (Clevers 
& van Leeuwen, 1994). However, at national and regional scale in Europe the regular acquisition of optical remote 
sensing data is hampered by frequent cloud cover. Radar remote sensing data offer a solution in acquiring remote 
sensing information with a high temporal resolution due to its all-weather capability. Moreover, data from both 
windows provide complementary information and the combined use, either contemporary or at different times 
during the growing season, can improve the estimation of crop variables. 
In this paper we combine optical and radar remote sensing techniques with crop growth models through the 
LAI as the essential link. The LAI is estimated with the derived inverse remote sensing models and brought in 
the calibration process of the crop growth model with the appropriate weight factor. This concept is illustrated 
with data from the MAC Europe 1991 campaign over the Dutch test site Flevoland. 
2 - DIRECT AND INVERSE MODELLING IN REMOTE SENSING 
2.1 LAI Estimation with the CLAIR Model 
A simplified, semi-empirical reflectance model for estimating LAI of a green canopy (vegetative stage) was introduced 
by Clevers (1988, 1989). It is called the CLAIR model. In this model, first, the WDVI (= weighted difference 
vegetation index) was ascertained as a weighted difference between the measured NIR and red reflectances, assuming 
that the ratio of NIR and red reflectances of bare soil is constant (the weight factor). In this way a correction 
for the influence of soil background is performed. Subsequently, this WDVI was used for estimating LAI according 
to the inverse of an exponential function: 
LAI = -1/a * ln(l - WDVI/WDVL) (1)