300
JULIAN DATE
line) and
x) and 1985
of GLAD in
ï
§
14000 16 000 °
1 MASS KG/HA)
line),
PVIC
1984 (x)
abd
ÏENNESSC
are
but are
not
[high daily
ghts) caused
), ND (NDC),
ire computed,
it sugar mass
ng season is
iteorological
phenomena cause the less regular pattern for 1985. An
attempt to predict sugar yield for 1985 from 1984
relationships by remote sensing methods only would be
senseless.
5 CONCLUSIONS
Remote sensing of sugar beet biomass with the purpose
of sugar yield estimation implies essentially an
indirect measurement of subterranean plant parts. Due
to meteorological factors experimental results did
not indicate simple relationships between crop canopy
and root biomass. Cover percentage could be estimated
rather precisely although further experiments using
more data are necessary to confirm these results.
Estimated GLAI is likely to be used in prediction
models which should also include meteorological
factors. Without doubt sugar beet yield prediction
with remote sensing methods only are prone to produce
results as inaccurate as most agromet models. The
ultimate solution appears to be a procedure
incorporating both meteo and remote sensing
parameters.
It was also concluded that spectral reflectance data
derived from CIR film yield results comparable to
those obtained by using more widely applied
radiometers.
AKNOVLEDGEMENTS
This research is part of IWONL contract No. 4555.
Field equipment and computer hardware of the Centre
for Remote Sensing of Vegetation (CEVA) were used
throughout the experiments.
The authors gratefully aknowledge EUROSENSE NV for
development of the CIR films, Luc Vandekerckove wrote
customized software on the PDP 11/34. Peter Haelvoet
and Dirk Tietens cheerfully assisted for densito
métrie measurements, GLAI calculations and drawings.
REFERENCES
Allen, E.J. and SCOTT, R.K. 1980. An analysis of
growth of the potato crop. J. Agr. Sci. Cambridge
94:583-606.
Analogides, D.A. 1979. Simulating the effect of the
starting date of harvest on sugar beet productivi
ty. Proc. 42nd Winter Congress I.J.R.B. plll-127.
Andrieu B. 1984. Utilisation de données photographi
ques et radiométriques pour la mise en évidence de
la rhizomanie de la betterave. Proc. 15th Int. Symp
on Rem. Sens, of Environment, p.813-823.
Badhwar, G.D., MacDonald, R.B. and Mehta, N.C. 1986.
Satellite-derived leaf area index and vegetation
maps as input to global carbon cycle models - a
hierarchical approach. Int. J. Rem. Sens. 7<2):
265-281.
Boehnel, H.J., Fischer, W. and Knoll, G. 1983. In si
tu measurements for the determination of the spec
tral characteristics of diseased and healthy sugar
beets. In P. Reichert and G. Hildebrandt (eds.),
Detection of sugar beet disease using remote sen
sing techniques. Final Report. Publ. Comm. of the
European Communities.
Colwell, J.E., Rice D.P. and Nalepka R.F. 1977. Wheat
yield forecasts using Landsat data. Proc. 12th Int.
Symp. on Remote Sensing of Environment p.1245-
1254.
De Wulf, R.R. and Goossens, R.E. 1986. Multispectral
indicators of seasonal crop development derived
from CIR slides. Rem. Sens, of Env. (under review)
Evans, G.C. 1972. Quantitative analysis of plant
growth. Oxford: Blackwell Scientific Publications.
Gosse, G., Varlet-Grancher, C., Bonhomme, R.,
Chartier, M., Allirand, J.M., and Lemaire, G. 1986.
Production maximale de matière sèche et rayonnement
soliare intercepté par un couvert végétal. Agronom.
6(1):47-56.
Hinzman, L.D. , Bauer, M.E. and Daughtry, C.S.T. 1986.
Effects of nitrogen fertilization on growth and re
flectance characteristics of winter wheat. Remote
Sens, of Env. 19:47-61.
Jackson, R.D. 1982. Spectral indices in n-space. Rem.
Sens, of Env. 13:409-429.
Leblon, B. 1983. Variations du rendement du blé
d'hiver et de la betterave sucrière en Belgique.
Effets du climat. Thesis. Fac. des sciences agrono
miques. U.C.L.
MacDonald, R.B., and Hall, F.G. 1976. Lacie: a look
to the future. Proc. 11th Symp. on Remote Sensing
of Environment p. 429-465.
N.I.S. 1985. Landbouwstatistieken No. 1-2-3. Min. Ec.
Zaken.
Reichert, P. 1983. Evaluation of airborne multispec
tral scanner data for the discrimination of healthy
and diseased sugar beets. In Reichert, P. and Hil
debrandt G. (eds.), Detection of sugar beet disease
using remote sensing techniques. Final Report.Publ.
Comm, of the European Communities.
Richardson, A.J. and Wiegand C.L. 1977. Distingui
shing vegetation from soil background information.
Photogramm, Eng. and Rem. Sens. 43(12): 1541-1552.
Steven, M.D., Biscoe, P.V. and Jaggard, K.W. 1981.
Estimation of sugar beet productivity from reflec
tion in the red and infrared spectral bands. In
The Remote Sensing Society (publ.), Matching Remo
te Sensing technologies and their application,
p. 259-279.
Thorne, G.N. 1971. Physiological factors limiting the
yield of arable crops. In P.F. Wareing and J.P.
Cooper(eds,),Potential Crop production p.131-157 ,
Heinemann Books.
Tucker, C.J. 1977, Asymptotic nature of grass cano
py spectral reflectance. Appl. Opt.16(5): 1151-1156.
Tucker, C.J., Van Praet, C.L., Sharman, M.J. and Van
Ittersum, G. 1985. Satellite Remote Sensing of to
tal herbaceous biomass production in the Senegalese
Sahel: 1980-1984. Remote Sensing of Environment 17:
233-249.
Whyte, R.O. 1960. Crop production and environment.
London: Faber and Faber.
