ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 4, pp. 265-273

Wheat canopy biophysical and spectral features seasonality

R. Kancheva , G. Georgiev 
Institute for Space Research and Technologies - Bulgarian Academy of Sciences
Agricultural monitoring is an important and continuously spreading application of remote sensing observations. It
supplies valuable information on crop condition and growth processes. Much research has been carried out on vegetation
phenology issues. These issues are related to using remotely sensed data for phenology monitoring, assessment of
vegetation types distribution, predicting ecosystems, quantifying the carbon budget, evaluation of year-to-year spatial
and temporal variations of vegetation seasonality, and the dependence of these changes on environmental factors. In
agriculture, the timing of seasonal cycles of crop activity is important for species classification and evaluation of crop
development, growing conditions and potential yield. However, the correct interpretation of remote sensing data and
the increasing demand for its reliability require ground-truth study of the seasonal spectral behaviuor of different species
and their link to crop vigour. For this reason, we performed ground-based experiments to investigate the seasonal
response of various winter wheat vegetation indices (VIs) to crop growth patterns. The utility of spectral vegetation
indices for monitoring crop seasonal dynamics, health condition, and yield potential was examined. The goal was to
quantify crop seasonality by establishing empirical relationships between plant biophysical and spectral properties
in different ontogenetic periods. Suchlike phenologically-specific relationships allow to assess crop condition during
different portions of the growth cycle and thus effectively track plant development and predict yield.
Keywords: winter wheat, spectral features, vegetation indices, seasonal dynamics, phenology, growth variables, yield prediction
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