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.

Berger, K., Halabuk, A., Verrelst, J., Mojses, M., Gerhatova, K., Tagliabue, G., et al. (2021). Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp.5822-5825). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS47720.2021.9553212].

Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy

Tagliabue, G.;
2021

Abstract

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.
Si
paper
Cellulose; CHIME; EnMAP; Gaussian process regression; Hybrid approaches; Lignin; PRISMA; Vegetation functional traits;
English
2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
978-1-6654-0369-6
Berger, K., Halabuk, A., Verrelst, J., Mojses, M., Gerhatova, K., Tagliabue, G., et al. (2021). Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp.5822-5825). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS47720.2021.9553212].
Berger, K; Halabuk, A; Verrelst, J; Mojses, M; Gerhatova, K; Tagliabue, G; Wocher, M; Hank, T
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/357870
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