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Remote Sens. 2010, 2(1), 76-114; doi:10.3390/rs2010076

Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

1
Department of Applied Biology, Section of Crop Science, University of Helsinki, P.O. Box 27, FIN-00014 Helsinki, Finland
2
Remote Sensing and Photogrammetry Department, The Finnish Geodetic Institute, 02431 Masala, Finland
*
Author to whom correspondence should be addressed.
Received: 14 October 2009 / Revised: 11 November 2009 / Accepted: 11 December 2009 / Published: 29 December 2009
(This article belongs to the Special Issue Microwave Remote Sensing)
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Abstract

During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). View Full-Text
Keywords: Composite multispectral modeling; SAR; classification; SatPhenClass algorithm; minimum dataset; cereal yield; phenology; LAI-bridge; CAP; IACS; FLPIS Composite multispectral modeling; SAR; classification; SatPhenClass algorithm; minimum dataset; cereal yield; phenology; LAI-bridge; CAP; IACS; FLPIS
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Laurila, H.; Karjalainen, M.; Hyyppä, J.; Kleemola, J. Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I). Remote Sens. 2010, 2, 76-114.

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