Google Scholar: cites
Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies
Brown, Luke (University of Southampton)
Morris, Harry (National Physical Laboratory)
MacLachlan, Andrew (University College London)
D'Adamo, Fracesco (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Adams, Jennifer (University of Zürich)
Lopez-Baeza, Ernesto (Universitat de València)
Albero, Erika (Universitat de València. Departament de Física de la Terra i Termodinàmica)
Martínez, Beatriz (Universitat de València. Departament de Física de la Terra i Termodinàmica)
Sánchez-Ruiz, Sergio (Universitat de València. Departament de Física de la Terra i Termodinàmica)
Campos-Taberner, Manuel (Universitat de València. Departament de Física de la Terra i Termodinàmica)
Lidón, Antonio (Universitat Politècnica de València)
Lull, Cristina (Universitat Politècnica de València)
Bautista, Inmaculada (Universitat Politècnica de València)
Clewley, Daniel (Natural Environment Research Council Earth Observation Data Acquisition and Analysis Service Plymouth Marine Laboratory)
Llewellyn, Gary (British Antarctic Survey)
Xie, Qiaoyun (University of Western Australia)
Camacho, Fernando (Earth Observation Laboratory)
Pastor-Guzman, Julio (Tecnológico Nacional de México)
Morrone, Rosalinda (National Physical Laboratory)
Sinclair, Morven (National Physical Laboratory)
Williams, Owen (University of Southampton)
Hunt, Merryn (University of Southampton)
Hueni, Andreas (University of Zürich. Department of Geography)
Boccia, Valentina (European Space Research Institute)
Dransfeld, Steffen (European Space Research Institute)
Dash, Jadunandan (University of Southampton)

Data: 2024
Resum: As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0. 92-2. 15, NRMSD = 40-67%, bias = -0. 64-0. 96) and CCC (RMSD = 0. 27-1. 27 g m, NRMSD = 64-84%, bias = -0. 17-0. 89 g m). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0. 88-1. 64, NRMSD = 27-64%, bias = -0. 78--0. 13) and CCC (RMSD = 0. 30-0. 87 g m, NRMSD = 52-73%, bias = 0. 03-0. 42 g m) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: CCC ; CHIME ; EnMAP ; INFORM ; LAI ; PRISMA ; SAIL ; SBG
Publicat a: Remote sensing (Basel), Vol. 16, Issue 12 (June 2024) , art. 2066, ISSN 2072-4292

DOI: 10.3390/rs16122066


19 p, 3.2 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2024-12-11, darrera modificació el 2025-10-12



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