Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery
| Autor: | Jochem Verrelst, Juan Pablo Rivera-Caicedo, K. Gerhátová, A. Halabuk, Miguel Morata Dolz, Tobias Hank, Katja Berger, Matej Mojses, Ana Belen Pascual Venteo, Matthias Wocher, G Tagliabue |
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| Přispěvatelé: | Berger, K, Hank, T, Halabuk, A, Rivera-Caicedo, J, Wocher, M, Mojses, M, Gerhatova, K, Tagliabue, G, Dolz, M, Venteo, A, Verrelst, J |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
PCA
Coefficient of determination Dimensionality reduction Science Biomass Hyperspectral imaging hybrid retrieval PRISMA PROSAIL-PRO Vegetation NPV Imaging spectroscopy CHIME Kriging active learning General Earth and Planetary Sciences Environmental science Leaf area index Gaussian process regression Remote sensing |
| Zdroj: | Remote Sensing, Vol 13, Iss 4711, p 4711 (2021) Remote Sensing Remote Sensing; Volume 13; Issue 22; Pages: 4711 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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