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)
