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

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...

Full description

Bibliographic Details
Main Authors: A. Kolotii, N. Kussul, A. Shelestov, S. Skakun, B. Yailymov, R. Basarab, M. Lavreniuk, T. Oliinyk, V. Ostapenko
Format: Article
Language:English
Published: Copernicus Publications 2015-04-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/39/2015/isprsarchives-XL-7-W3-39-2015.pdf
id doaj-d06f016dec304afc8fa990aba3de349a
record_format Article
spelling doaj-d06f016dec304afc8fa990aba3de349a2020-11-25T01:14:58ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342015-04-01XL-7/W3394410.5194/isprsarchives-XL-7-W3-39-2015Comparison of biophysical and satellite predictors for wheat yield forecasting in UkraineA. Kolotii0N. Kussul1A. Shelestov2S. Skakun3B. Yailymov4R. Basarab5M. Lavreniuk6T. Oliinyk7V. Ostapenko8Space Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineSpace Research Institute NASU-SSAU, Department of Space Information Technologies and Systems, Kyiv, UkraineWinter 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.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/39/2015/isprsarchives-XL-7-W3-39-2015.pdf
collection DOAJ
language English
format Article
sources DOAJ
author A. Kolotii
N. Kussul
A. Shelestov
S. Skakun
B. Yailymov
R. Basarab
M. Lavreniuk
T. Oliinyk
V. Ostapenko
spellingShingle A. Kolotii
N. Kussul
A. Shelestov
S. Skakun
B. Yailymov
R. Basarab
M. Lavreniuk
T. Oliinyk
V. Ostapenko
Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet A. Kolotii
N. Kussul
A. Shelestov
S. Skakun
B. Yailymov
R. Basarab
M. Lavreniuk
T. Oliinyk
V. Ostapenko
author_sort A. Kolotii
title Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
title_short Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
title_full Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
title_fullStr Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
title_full_unstemmed Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
title_sort comparison of biophysical and satellite predictors for wheat yield forecasting in ukraine
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2015-04-01
description 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.
url http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/39/2015/isprsarchives-XL-7-W3-39-2015.pdf
work_keys_str_mv AT akolotii comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT nkussul comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT ashelestov comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT sskakun comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT byailymov comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT rbasarab comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT mlavreniuk comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT toliinyk comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
AT vostapenko comparisonofbiophysicalandsatellitepredictorsforwheatyieldforecastinginukraine
_version_ 1725155180855427072