0000000000423811

AUTHOR

L. Goudzwaard

showing 1 related works from this author

Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI

2020

The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of…

Leaf area index (LAI)010504 meteorology & atmospheric sciencesComputer scienceScienceMultispectral image0211 other engineering and technologiesFeature selection02 engineering and technology01 natural sciencesCropLaboratory of Geo-information Science and Remote SensingMachine learningRadiative transferBosecologie en BosbeheerLaboratorium voor Geo-informatiekunde en Remote SensingForestLeaf area indexDiscrete anisotropic radiative transfer (DART) model021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingQInversion (meteorology)Vegetation15. Life on landPE&RCForest Ecology and Forest ManagementVegetation radiative transfer modelNoiseFeature (computer vision)Thematic MapperGeological surveyGeneral Earth and Planetary SciencesSentinel-2Remote Sensing
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