6533b85cfe1ef96bd12bc0b9
RESEARCH PRODUCT
PHYSICS-based retrieval of scattering albedo and vegetation optical depth using multi-sensor data integration
Jaan PraksMartin BaurCarsten MontzkaMaria PilesD. EntekhabiOleg AntropovJaakko SeppänenA. LowMoritz LinkThomas Jagdhubersubject
010504 meteorology & atmospheric sciencesScattering albedo0208 environmental biotechnologyradiometry02 engineering and technologyretrieval methodologycomputer.software_genre01 natural scienceslaw.inventionlawremote sensing by radarRadaractive-passive microwavesPhysics::Atmospheric and Oceanic PhysicsIndexespassive microwave remote sensingRemote sensingremote sensing by laser beamGeographyLidaroptical radarcrucial parametersmedicine.symptomvegetation scattering coefficientData integrationBackscattervegetation mappingta1171τ-ω modelsoilPhysics::GeophysicsICESat lidar vegetation heightsvegetationmedicineVegetation optical depthbackscatter0105 earth and related environmental sciencesRemote sensingsensor fusionRadiometerScatteringnovel multisensor approachSMAPAlbedoMulti-sensor020801 environmental engineeringradiometer dataVegetation (pathology)multisensor data integration approachcomputerICESatalbedodescription
Vegetation optical depth and scattering albedo are crucial parameters within the widely used τ-ω model for passive microwave remote sensing of vegetation and soil. A multi-sensor data integration approach using ICESat lidar vegetation heights and SMAP radar as well as radiometer data enables a direct retrieval of the two parameters on a physics-derived basis. The crucial step within the retrieval methodology is the calculus of the vegetation scattering coefficient KS, where one exact and three approximated solutions are provided. It is shown that, when using the assumption of a randomly oriented volume, the backscatter measurements of the radar provide a sufficient first order estimate and subsequently lead to effective estimates of vegetation optical depth and scattering albedo acquired with the novel multi-sensor approach.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-12-01 |