0000000000555646

AUTHOR

Fred Baret

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Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

2021

Abstract Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to re…

010504 meteorology & atmospheric sciencesMean squared errorArtificial neural networkCalibration (statistics)0208 environmental biotechnologyEmpirical modellingSoil ScienceGeology02 engineering and technology01 natural sciencesNormalized Difference Vegetation Index020801 environmental engineeringSupport vector machineData pointKrigingComputers in Earth SciencesAlgorithm0105 earth and related environmental sciencesRemote sensingMathematicsRemote Sensing of Environment
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