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RESEARCH PRODUCT
Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy
Matthias WocherTobias HankA. HalabukKatja BergerJochem VerrelstG TagliabueMatej MojsesK. Gerhátovásubject
2. Zero hunger010504 meteorology & atmospheric sciencesData stream mining0211 other engineering and technologiesEnMAPHyperspectral imagingContext (language use)PRISMA02 engineering and technologyVegetationVegetation functional trait01 natural sciencesLigninImaging spectroscopyAtmospheric radiative transfer codesWorkflowHybrid approacheCHIMEKrigingEnvironmental scienceCelluloseGaussian process regression021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingdescription
Non-photosynthetic vegetation (NPV) has been identified as priority variable in the context of new spaceborne imaging spectroscopy missions. In this study we provide a first attempt to quantify NPV biomass from these unprecedented data streams to be provided by multiple recently launched or planned instruments. A hybrid workflow is proposed including Gaussian process regression (GPR) trained over radiative transfer model (RTM) simulations and applying active learning strategies. A soybean field data set including two dates with NPV measurements on yellow and senescent (brown) plant organs was used for model validation, resulting in relative errors of 13.4%. This prototype retrieval model was then applied over a resampled Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) scene, resulting in trustful estimates of NPV biomass for some areas with crop residue cover and senescent vegetation. In view of these results, the proposed workflow may show a promising path towards operational delivery of next-generation global NPV products.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-07-11 |