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  Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

Berger [Richter], K., Hank, T., Halabuk, A., Rivera-Caicedo, J. P., Wocher, M., Mojses, M., Gerhátová, K., Tagliabue, G., Dolz, M. M., Venteo, A. B. P., Verrelst, J. (2021): Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery. - Remote Sensing, 13, 22, 4711.
https://doi.org/10.3390/rs13224711

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 Creators:
Berger [Richter], Katja1, Author              
Hank, Tobias2, Author
Halabuk, Andrej2, Author
Rivera-Caicedo, Juan Pablo2, Author
Wocher, Matthias2, Author
Mojses, Matej2, Author
Gerhátová, Katarina2, Author
Tagliabue, Giulia2, Author
Dolz, Miguel Morata2, Author
Venteo, Ana Belen Pascual2, Author
Verrelst, Jochem2, Author
Affiliations:
10 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146023              
2External Organizations, ou_persistent22              

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Free keywords: PRISMA; CHIME; NPV; Gaussian process regression; hybrid retrieval; active learning; PCA; PROSAIL-PRO
 Abstract: Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.

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Language(s): eng - English
 Dates: 2021-11-212021
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3390/rs13224711
 Degree: -

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Title: Remote Sensing
Source Genre: Journal, SCI, Scopus, OA
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Pages: - Volume / Issue: 13 (22) Sequence Number: 4711 Start / End Page: - Identifier: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals426