COMBINING DIRECTIONAL AND HIGH SPECTRAL RESOLUTION INFORMATION
FROM OPTICAL REMOTE SENSING DATA FOR CROP GROWTH MONITORING
J.G.P.W. CLEVERS and H.J.C. van LEEUWEN
Wageningen Agricultural University
Dept, of Landsurveying and Remote Sensing
P.O. Box 339, 6700 AH Wageningen (The Netherlands)
ABSTRACT
Knowledge about land cover and crop production is required for an agricultural policy at national and EC level.
For monitoring agricultural crop production, growth of crops has to be studied, e.g. by using crop growth models.
Estimates of crop growth often are inaccurate for practical field conditions. Therefore, model simulations must
be improved by incorporating information on the actual growth and development of field crops, e.g. by using
remote sensing data. Relevant crop parameters are: (1) leaf area index (LAI), (2) leaf angle distribution (LAD)
and (3) leaf optical properties in the PAR region. The methodology of integrating optical remote sensing data
from various sources with crop growth models is illustrated with a case study for sugar beet using airborne data
obtained during the MAC Europe 1991 campaign over the Dutch test site Flevoland. Results showed that, by
combining high resolution spectral measurements with bidirectional broad-band measurements, information on
LAI, LAD and leaf chlorophyll content (related to leaf optical properties) may be obtained, which is important
for an accurate monitoring of crop growth and prediction of crop yield. On the average, the simulation error
of beet yield decreased from 13.4 tons/ha without using remote sensing to 4.2 tons/ha using remote sensing data.
KEY WORDS: Directional Reflectance, High Spectral Resolution, Crop Growth Monitoring
1 - INTRODUCTION
In the past decades, a lot of research has been devoted to land cover classification and acreage estimation using
remote sensing techniques with considerable success. Another very important field of interest in agriculture is
yield estimation. Such information is essential for an agricultural policy at national and EC level. However, remote
sensing alone is generally not capable to produce accurate yield estimations. This has prompted scientists to look
for other techniques that can be combined with remote sensing data to give better results. One of such techniques
is crop growth modelling (Deldcolle et al., 1992).
In this paper, some views on linking optical remote sensing data with crop growth models are presented.
Emphasis is on using information from both directional and spectral measurements. Some concepts will be illustrated
with data from the MAC Europe 1991 campain over the Dutch test site Flevoland.
1.1 Crop Growth Models
Crop growth models describe the relationship between physiological processes in plants and environmental factors
such as solar irradiation, temperature and water and nutrient availability. These models compute the daily growth
and development rate of a crop, simulating the dry matter production from emergence till maturity. Finally, a
simulation of yield at harvest time is obtained. The basis for the calculations of dry matter production is the rate
of gross C0 2 assimilation of the canopy.
The main driving force for crop growth in these models is absorbed solar radiation, and a lot of emphasis
is given to the modelling of the solar radiation budget in the canopy. Incoming photosynthetically active radiation
(PAR * 400-700 nm) is first partly reflected by the top layer of the canopy. The complementary fraction is potentially
available for absorption by the canopy. The product of the amount of incoming photosynthetically active radiation
(PAR) and the absorptance yields the amount of absorbed photosynthetically active radiation (APAR). The rate
°f C0 2 assimilation (photosynthesis) is calculated from the APAR and the photosynthesis-light response of individual
leaves. The maximum rate of photosynthesis at light saturation is highly correlated to the leaf nitrogen content.
Hie assimilated C0 2 is then reduced to carbohydrates which can be used by the plant for growth.
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