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
Full textReferences:
- Darvishzadeh R., Skidmore A., Schlerf M., Atzberger C., Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland, Remote Sensing of Environment, 2008, Vol. 112 (5), pp. 2592-2604.
- Broge N. H., Mortensen J. V., Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data, Remote Sensing of Environment, 2002, Vol. 81 (1), pp. 45-57.
- McNairn H., Deriving percent crop cover over agriculture canopies using hyperspectral remote sensing, Canadian Journal of Remote Sensing, 2008, Vol. 34 (1).
- González-Sanpedro M.C., Le Toan T. , Moreno J., Kergoat L., Rubio E., Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data, Remote Sensing of Environment, 2008, Vol. 112 (3), pp. 810-824.
- Doraiswamy P.C., Hatfield J.L., Jackson T.J., Akhmedov B., Prueger J., Stern A., Crop condition and yield simulations using Landsat and MODIS, Remote Sensing of Environment, 2004, Vol. 92, pp. 548–559.
- Serrano L., Filella I., Penuelas J., Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies, Crop Science, 2000, Vol. 40, pp. 723–731.
- Anup K. Prasad A.K., Chai L., Singh R.P., Kafatos M., Crop yield estimation model for Iowa using remote sensing and surface parameters, International Journal of Applied Earth Observation and Geoinformation, 2006, Vol. 8, pp. 26–33.
- Jolly, W. M., Nemani, R., Running, S. W., A generalized, bioclimatic index to predict foliar phenology in response to climate, Global Change Biology, 2005, Vol. 11, pp. 619−632.
- Schwartz M. D., Reed B., & White M. A., Assessing satellite-derived start-of-season (SOS) measures in the conterminous USA, International Journal of Climatology, 2002, Vol. 22, pp. 1793−1805.
- Zhang X.Y., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., Reed B.C., Huete A., Monitoring vegetation phenology using MODIS, Remote Sensing of Environment, 2003, Vol. 84, pp. 471−475.
- Ferreira L.G., Huete.A. R., Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices, Int. J. Remote Sensing, 2004, Vol. 25, No. 10, pp. 1837–1860..
- Ferreira L.G., Yoshioka H., Huete A., Sano E.E., Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado: An analysis within the Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA), Remote Sensing of Environment, 2003, Vol. 87, pp. 534–550.
- White, M. A., Nemani, R.R., Soil water forecasting in the continental United States: Relative forcing by meteorology versus leaf area index and the effects of meteorological forecast errors, Canadian Journal of Remote Sensing, 2004, Vol. 30, pp. 717−730.
- Stockli R., Rutishauser T., Dragoni D., O’Keefe J., Thornton P. E., Jolly M., Lu L., Denning A.S., Remote sensing data assimilation for a prognostic phenology model, Jounal of Geophysical Research, 113, G04021, doi:10.1029/2008JG000781, 2008.
- White M. A., Brunsell N., Schwartz M.D., Vegetation phenology in global change studies (from Phenology: An integrative environmental science, New York: Kluwer Academic Publishers, 2003, pp. 453-466.
- White M. A., Hoffman F., Hargrove W.W., Nemani R. R., A global framework for monitoring phonological responses to climate change, Jounal of Geophysical Research, Vol. 32, L04705, doi:10.1029/2004GL021961, 2005.
- Jenkins J.P., Braswell B.H., Frolking S.E., Aber J.D., Detecting and predicting spatial and interannual patterns of temperate forest springtime phenology in the eastern U.S., Geophysical Research Letters, Vol. 29 (24), 2201, doi:10.1029/2001GL014008, 2002.