期刊
REMOTE SENSING
卷 2, 期 1, 页码 76-114出版社
MDPI
DOI: 10.3390/rs2010076
关键词
Composite multispectral modeling; SAR; classification; SatPhenClass algorithm; minimum dataset; cereal yield; phenology; LAI-bridge; CAP; IACS; FLPIS
类别
资金
- Finnish National Board of Agriculture from the Maatilatalouden kehittamisrahasto fund
- European Union [AOE-488]
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 multispectral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R-2 for cereal yield (y(b)) 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).
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