Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries

W. Kowalik, K. Dabrowska-Zielinska, M. Meroni, T.U. Raczka, A.J.W. de Wit

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Scopus)

Abstract

In the period 1999-2009 ten-day SPOT-VEGETATION products of the Normalized Difference Vegetation Index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 1 km spatial resolution were used in order to estimate and forecast the wheat yield over Europe. The products were used together with official wheat yield statistics to fine-tune a statistical model for each NUTS2 region, based on the Partial Least Squares Regression (PLSR) method. This method has been chosen to construct the model in the presence of many correlated predictor variables (10-day values of remote sensing indicators) and a limited number of wheat yield observations. The model was run in two different modalities: the "monitoring mode", which allows for an overall yield assessment at the end of the growing season, and the "forecasting mode", which provides early and timely yield estimates when the growing season is on-going. Performances of yield estimation at the regional and national level were evaluated using a cross-validation technique against yield statistics and the estimations were compared with those of a reference crop growth model. Models based on either NDVI or FAPAR normalized indicators achieved similar results with a minimal advantage of the model based on the FAPAR product. Best modelling results were obtained for the countries in Central Europe (Poland, North-Eastern Germany) and also Great Britain. By contrast, poor model performances characterize countries as follows: Sweden, Finland, Ireland, Portugal, Romania and Hungary. Country level yield estimates using the PLSR model in the monitoring mode, and those of a reference crop growth model that do not make use of remote sensing information showed comparable accuracies. The largest estimation errors were observed in Portugal, Spain and Finland for both approaches. This convergence may indicate poor reliability of the official yield statistics in these countries. (C) 2014 Elsevier B.V. All rights reserved,
Original languageEnglish
Pages (from-to)228-239
JournalInternational Journal of applied Earth Observation and Geoinformation
Volume32
DOIs
Publication statusPublished - 2014

Fingerprint

SPOT
wheat
photosynthetically active radiation
Statistics
Radiation
Crops
Remote sensing
NDVI
growing season
remote sensing
product
Monitoring
crop
Error analysis
monitoring
spatial resolution

Keywords

  • ndvi time-series
  • crop yield
  • modis data
  • grain yields
  • area index
  • leaf-area
  • model
  • prediction
  • networks
  • drought

Cite this

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abstract = "In the period 1999-2009 ten-day SPOT-VEGETATION products of the Normalized Difference Vegetation Index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 1 km spatial resolution were used in order to estimate and forecast the wheat yield over Europe. The products were used together with official wheat yield statistics to fine-tune a statistical model for each NUTS2 region, based on the Partial Least Squares Regression (PLSR) method. This method has been chosen to construct the model in the presence of many correlated predictor variables (10-day values of remote sensing indicators) and a limited number of wheat yield observations. The model was run in two different modalities: the {"}monitoring mode{"}, which allows for an overall yield assessment at the end of the growing season, and the {"}forecasting mode{"}, which provides early and timely yield estimates when the growing season is on-going. Performances of yield estimation at the regional and national level were evaluated using a cross-validation technique against yield statistics and the estimations were compared with those of a reference crop growth model. Models based on either NDVI or FAPAR normalized indicators achieved similar results with a minimal advantage of the model based on the FAPAR product. Best modelling results were obtained for the countries in Central Europe (Poland, North-Eastern Germany) and also Great Britain. By contrast, poor model performances characterize countries as follows: Sweden, Finland, Ireland, Portugal, Romania and Hungary. Country level yield estimates using the PLSR model in the monitoring mode, and those of a reference crop growth model that do not make use of remote sensing information showed comparable accuracies. The largest estimation errors were observed in Portugal, Spain and Finland for both approaches. This convergence may indicate poor reliability of the official yield statistics in these countries. (C) 2014 Elsevier B.V. All rights reserved,",
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Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries. / Kowalik, W.; Dabrowska-Zielinska, K.; Meroni, M.; Raczka, T.U.; de Wit, A.J.W.

In: International Journal of applied Earth Observation and Geoinformation, Vol. 32, 2014, p. 228-239.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries

AU - Kowalik, W.

AU - Dabrowska-Zielinska, K.

AU - Meroni, M.

AU - Raczka, T.U.

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AB - In the period 1999-2009 ten-day SPOT-VEGETATION products of the Normalized Difference Vegetation Index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 1 km spatial resolution were used in order to estimate and forecast the wheat yield over Europe. The products were used together with official wheat yield statistics to fine-tune a statistical model for each NUTS2 region, based on the Partial Least Squares Regression (PLSR) method. This method has been chosen to construct the model in the presence of many correlated predictor variables (10-day values of remote sensing indicators) and a limited number of wheat yield observations. The model was run in two different modalities: the "monitoring mode", which allows for an overall yield assessment at the end of the growing season, and the "forecasting mode", which provides early and timely yield estimates when the growing season is on-going. Performances of yield estimation at the regional and national level were evaluated using a cross-validation technique against yield statistics and the estimations were compared with those of a reference crop growth model. Models based on either NDVI or FAPAR normalized indicators achieved similar results with a minimal advantage of the model based on the FAPAR product. Best modelling results were obtained for the countries in Central Europe (Poland, North-Eastern Germany) and also Great Britain. By contrast, poor model performances characterize countries as follows: Sweden, Finland, Ireland, Portugal, Romania and Hungary. Country level yield estimates using the PLSR model in the monitoring mode, and those of a reference crop growth model that do not make use of remote sensing information showed comparable accuracies. The largest estimation errors were observed in Portugal, Spain and Finland for both approaches. This convergence may indicate poor reliability of the official yield statistics in these countries. (C) 2014 Elsevier B.V. All rights reserved,

KW - ndvi time-series

KW - crop yield

KW - modis data

KW - grain yields

KW - area index

KW - leaf-area

KW - model

KW - prediction

KW - networks

KW - drought

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DO - 10.1016/j.jag.2014.03.011

M3 - Article

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EP - 239

JO - International Journal of applied Earth Observation and Geoinformation

JF - International Journal of applied Earth Observation and Geoinformation

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