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Remote Sens. 2010, 2(9), 2185-2239; doi:10.3390/rs2092185

Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

1
Department of Applied Biology Section of Crop Science, University of Helsinki, P.O. Box 27, FIN-00014 University of Helsinki, Finland
2
Remote Sensing and Photogrammetry Department, The Finnish Geodetic Institute, 02431 Masala, Finland
*
Author to whom correspondence should be addressed.
Received: 25 July 2010 / Revised: 3 September 2010 / Accepted: 3 September 2010 / Published: 16 September 2010
(This article belongs to the Special Issue Global Croplands)
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Abstract

During 1996–2006, the Ministry of Agriculture and Forestry in Finland (MAFF), MTT Agrifood Research and the Finnish Geodetic Institute performed a joint remote sensing satellite research project. It evaluated the applicability of optical satellite (Landsat, SPOT) data for cereal yield estimations in the annual crop inventory program. Four Optical Vegetation Indices models (I: Infrared polynomial, II: NDVI, III: GEMI, IV: PARND/FAPAR) were validated to estimate cereal baseline yield levels (yb) using solely optical harmonized satellite data (Optical Minimum Dataset). The optimized Model II (NDVI) yb level was 4,240 kg/ha (R2 0.73, RMSE 297 kg/ha) for wheat and 4390 kg/ha (R2 0.61, RMSE 449 kg/ha) for barley and Model I yb was 3,480 kg/ha for oats (R2 0.76, RMSE 258 kg/ha). Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R2 0.71, RMSE 436 kg/ha) and with composite SAR/ASAR and NDVI models (mean R2 0.61, RMSE 402 kg/ha) using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats. View Full-Text
Keywords: optical vegetation Indices models; classification; NDVI; GEMI; FAPAR; PARND; SAR/ASAR; CropWatN; LAI-bridge; Finland; CAP; Kalman Filter; data fusion; harmonized data optical vegetation Indices models; classification; NDVI; GEMI; FAPAR; PARND; SAR/ASAR; CropWatN; LAI-bridge; Finland; CAP; Kalman Filter; data fusion; harmonized data
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.; Kleemola, J.; Hyyppä, J. Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing. Remote Sens. 2010, 2, 2185-2239.

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