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Journal Article

Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

Authors
/persons/resource/kberger

Berger [Richter],  Katja
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Hank,  Tobias
External Organizations;

Halabuk,  Andrej
External Organizations;

Rivera-Caicedo,  Juan Pablo
External Organizations;

Wocher,  Matthias
External Organizations;

Mojses,  Matej
External Organizations;

Gerhátová,  Katarina
External Organizations;

Tagliabue,  Giulia
External Organizations;

Dolz,  Miguel Morata
External Organizations;

Venteo,  Ana Belen Pascual
External Organizations;

Verrelst,  Jochem
External Organizations;

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Citation

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


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026380
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.