Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries |
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| Institution: | 1. Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland;2. Joint Research Center, Institute for Environment and Sustainability (IES), Via E. Fermi 2749, I-21027 Ispra (VA), Italy;3. Alterra Wageningen UR, P.O. Box 47, 6700 AA Wageningen, The Netherlands;1. Hybrid Rapeseed Research Center of Shaanxi Province, Yangling, Shaanxi 712100, PR China;2. College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, PR China;1. School of Information Engineering, China University of Geosciences, 29 Xueyuan Road, Beijing 100083, China;2. Institute of Remote Sensing and GIS, Peking University, 5 Yiheyuan Road, Beijing 100871, China;1. Sustainable Intensification Innovation Lab, Kansas State University, 108 Waters Hall, 1603 Old Claflin Place, Manhattan, KS 66506, USA;2. Department of Agronomy, 2004 Throckmorton Plant Science Center, Kansas State University, 1712 Claflin Road, Manhattan, KS 66506, USA;3. USDA – ARS Southeast Area, 141 Experiment Station Road, Stoneville, MS 38776, USA;4. Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USA;1. KU Leuven University, Department of Earth and Environmental Sciences, Celestijnenlaan 200E, Post Box 02411, B-3001 Leuven, Belgium;2. FAO, Land and Water Division, VialedelleTerme di Caracalla, 00153 Rome, Italy;3. University of California, Davis, One Shields Avenue, Davis, CA 95616, USA;4. University of Cordoba (UCO), Medina Azahara Avenue, 5, 14071 Cordoba, Spain;5. International Atomic Energy Agency (IAEA), Vienna International Centre, PO Box 100, A-1400 Vienna, Austria;1. Università degli Studi di Milano, DEMM, Cassandra lab, via Celoria 2, 20133 Milano, Italy;2. Università degli Studi di Milano, DiSAA, Cassandra lab, via Celoria 2, 20133 Milan, Italy;3. European Commission, Joint Research Centre, Institute for Environment and Sustainability, Monitoring Agricultural Resources Unit—H04, Via Fermi 2749, TP 263, I-21027 Ispra, VA, Italy;4. Universidad de Córdoba, Departamento de Agronomía, Apartado 3048, 14080 Córdoba, Spain;5. Agroscope Institute for Sustainability Sciences ISS, Reckenholzstrasse 191, P.O. Box 8046, Zürich, Switzerland;6. UMR Ecosystème Prairial, INRA, VetAgroSup, 63000 Clermont-Ferrand, France;7. Instituto de Agricultura Sostenible, CSIC. Apdo. 4084, 14080 Cordoba, Spain;8. Munich Re, Agro & Weather, Königinstr. 107, 80802 Munich, Germany;1. European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability (IES), Monitoring Agricultural Resources Unit (MARS), Via Enrico Fermi 2749, 20127 Ispra, Italy;2. European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability (IES), Climate Risk Management Unit, Via Enrico Fermi 2749, 20127 Ispra, Italy |
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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. |
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| Keywords: | Yield forecasting Wheat Remote sensing Crop simulations models: European scale |
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