Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986
Sugar beet biomass estimation using spectral data
derived from colour infrared slides
Robert R.De Wulf & Roland E.Goossens
State University Gent, Belgium
ABSTRACT: Sugar beet spectral reflectance data were extracted from multitemporal ground -
based colour infrared photography. Measured agronomic parameters include green leaf area
index (GLAI), cover percentage, fresh root weight and sugar content.
Statistical relationships between spectral reflectance and biomass were determined for
one growing season and applied for prediction of biomass in a subsequent growing season.
1 INTRODUCTION
Crop-weather relationships numerically expressed in
prediction models are being applied for yield fore
casting in a number of countries.
However no crop growth model has been able to
perfectly simulate the synergistic effects of
environmental conditions, meteorological factors and
agro-cultural factors (Colwell 1977 et al).
Moreover , a number of abiotic and biotic hazards
which cannot be predicted by agromet models or
visually assessed in the field, can have conside
rable negative effects on crop yield.
Remote sensing may serve to complement traditional
methods for crop growth monitoring and production fo
recasting on local and on global scale.
Ground-based radiometry has established empirical
relations between spectral and agronomic parameters
for a range of economically important crops. Research
efforts have established the basics for operational
large scale applications as global wheat yield fore
casting in the LACIE project (MacDonald and Hall
1978) and biomass estimation in arid zones using
multitemporal NOAA AVHRR data (Tucker et al 1985).
Crop production can be viewed as a product of
acreage and yield (Colwell et al 1977), A colour
infrared (CIR) photograph allows extraction of both
factors. Using visual interpretation methods or
interactive image classification preceded by
digitizing, parcels of land covered by a particular
crop can be measured fairly precisely. This aspect
is not elaborated here.
The numerical assessment of the yield factor requires
a statistically-based relationship between remotely
sensed data and agronomic features. This relationship
has been studied for sugar beets during the 1984 and
1985 growing seasons.
The choice of a 35mm colour infrared emulsion is
tied to its planned use in an automatic SLR camera on
board a remotely piloted aircraft (RPA).
This delta-winged RPA, custom-built for the Centre
for Remote Sensing of Vegetation (CEVA) , made
successful test flights in March 1986 and is
scheduled to be fully operational for low altitude
crop monitoring in the second half of the 1986
growing season.
The procedure for extraction of spectral parameters
from 35mm CIR slides, in a preparatory phase taken
from a tripod, became a priority research issue at
CEVA.
The selection of sugar beet as crop of interest is
determined by its importance in Belgian agriculture.
In 1984 sugar beets covered 117000 ha or 8.4 % of the
country's agricultural surface (N.I.S. 1985).
2 REMOTE SENSING OF SUGAR BEET BIOMASS : PRINCIPLES
According to Thorne (1971) arable crops can be
regarded as machines for converting C02 and water
into carbohydrate, using the sun's energy.
Photosynthate can be transferred to physiological
sinks which consitute the harvestable parts of a
number of economically important crops : potato
tubers, wheat grain kernels and sugar beet roots.
White sugar is the most important part of the sugar
beet from the economical point of view. However in
terms of biomass quantity (15%) it is clearly sur
passed by a sizeable amount of processed by-products
including pulp and brown molasse, both used as
livestock feed.
Remote sensing of sugar beet biomass requires
efficient and economical data aquisition and know
ledge of yield-related crucial moments on the
physiological time scale.
Sugar beet productivity is strongly favoured by long
growing seasons provided that meteorological and soil
conditions are not limiting (Analogides 1979). For’
sugar beets (Steven et al 1982), potatoes (Allen and)
Scott 1980) as for cereals ( Gossse et al 1986) it
has been shown that total dry matter accumulation is
proportional to intercepted solar radiation.
The amount of photosynthetically active biomass,
usually expressed as Green Leaf Area Index (GLAI) and
its time of duration (Green Leaf Area Duration,GLAD )
have become critical parameters in the majority of
plant growth analysis. The LAD concept is illustrated
by Evans (1972), who compared dry matter at harvest
with GLAD for potatoes and sugar beets, and found
both factors to be 57% higher for sugar beets.
From a prediction point of view radiation alone
may not be a particular useful variable. In the case
of sugar beet, and specifically when its root sugar
content is considered meteorological factors are
known to play a decisive role which varies with the
physiological time scale.
Soil moisture deficit at the 4-leaf stage can have
disastrous consequences for harvest, whereas too much
rains in July and August favour leaf development at
the expense of root weight and sugar content (Leblon
1983). Cool days and frosty nights in late September
and October check leaf and root growth but increase
photosynthetic activity and sugar storage (Whyte
1960).
Scammel's general yield prediction model for
Belgium is based on sugar beet physiology and
meteorological parameters but is said to perform
poorly on local basis (Leblon 1983). This is mainly
due to varying climatological conditions and soil
