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.

Berger, K., Hank, T., Halabuk, A., Rivera-Caicedo, J., Wocher, M., Mojses, M., et al. (2021). Assessing non-photosynthetic cropland biomass from spaceborne hyperspectral imagery. REMOTE SENSING, 13(22) [10.3390/rs13224711].

Assessing non-photosynthetic cropland biomass from spaceborne hyperspectral imagery

Tagliabue G.;
2021

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.
Articolo in rivista - Articolo scientifico
Active learning; CHIME; Gaussian process regression; Hybrid retrieval; NPV; PCA; PRISMA; PROSAIL-PRO;
English
Berger, K., Hank, T., Halabuk, A., Rivera-Caicedo, J., Wocher, M., Mojses, M., et al. (2021). Assessing non-photosynthetic cropland biomass from spaceborne hyperspectral imagery. REMOTE SENSING, 13(22) [10.3390/rs13224711].
Berger, K; Hank, T; Halabuk, A; Rivera-Caicedo, J; Wocher, M; Mojses, M; Gerhatova, K; Tagliabue, G; Dolz, M; Venteo, A; Verrelst, J
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/357864
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