The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 39–44, 2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 39–44, 2015

  28 Apr 2015

28 Apr 2015

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

A. Kolotii1,2,3, N. Kussul1,3, A. Shelestov1,2,3, S. Skakun1, B. Yailymov1,2, R. Basarab1,2, M. Lavreniuk1, T. Oliinyk1, and V. Ostapenko1 A. Kolotii et al.
  • 1Space Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, Ukraine
  • 2National University of Life and Environmental Sciences of Ukraine , Kiev, Ukraine
  • 3National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kiev, Ukraine

Keywords: Crop yield, Forecasting, Biophysical parameters, Winter wheat, Ukraine, regression model, NDVI, FAPAR, LAI

Abstract. Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.