0ad8dd96dd989d1c1f48688a4903752d RemSen_a2024v16n12p2066.pdf bfa7b4487f4eb7a8671553de637d3e030ad7fc98 RemSen_a2024v16n12p2066.pdf 32221c420b5d6e086d6d64fce200b42e7ea0ffffea2b74bc676b6d52e95df6ea RemSen_a2024v16n12p2066.pdf Title: Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies Subject: 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-2, NRMSD = 64–84%, bias = -0.17–0.89 g m-2). 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-2, NRMSD = 52–73%, bias = 0.03–0.42 g m-2) 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. Keywords: CCC; CHIME; EnMAP; INFORM; LAI; PRISMA; SAIL; SBG Author: Luke A. Brown, Harry Morris, Andrew MacLachlan, Francesco D’Adamo, Jennifer Adams, Ernesto Lopez-Baeza, Erika Albero, Beatriz Martínez, Sergio Sánchez-Ruiz, Manuel Campos-Taberner, Antonio Lidón, Cristina Lull, Inmaculada Bautista, Daniel Clewley, Gary Llewellyn, Qiaoyun Xie, Fernando Camacho, Julio Pastor-Guzman, Rosalinda Morrone, Morven Sinclair, Owen Williams, Merryn Hunt, Andreas Hueni, Valentina Boccia, Steffen Dransfeld and Jadunandan Dash Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Wed Jun 26 04:41:59 2024 CEST ModDate: Wed Jun 26 04:45:04 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 19 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3304081 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- PRJKGQ+VnURWPalladioL-Bold Type 1 Custom yes 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EBCNDC+PalatinoLinotype,Bold CID TrueType Identity-H yes yes yes 254 0 EBCNEC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 260 0 EBCNFD+PalatinoLinotype CID TrueType Identity-H yes yes yes 263 0 EBCNFE+PalatinoLinotype TrueType WinAnsi yes yes no 269 0 EBCNFG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 272 0 HYYHEA+CMBSY10 Type 1 Builtin yes yes yes 283 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-01-31 02:46:59 CET RepresentationInformation: RemSen_a2024v16n12p2066.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-01-30 19:08:52 CET Size: 3304081 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 593 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Airborne Hyperspectral Data Acquisition Destination: subsection.2.1 Item: Title: Airborne Hyperspectral Data Pre-Processing Destination: subsection.2.2 Item: Title: Airborne Hyperspectral LAI and CCC Retrieval Destination: subsection.2.3 Item: Title: Validation of LAI and CCC Retrievals against In Situ Measurements Destination: subsection.2.4 Item: Title: Results Destination: section.3 Children: Item: Title: Consistency of Atmospheric Correction Approaches Destination: subsection.3.1 Item: Title: Characteristics of Airborne Hyperspectral and In Situ LAI, LCC, and CCC Data Destination: subsection.3.2 Item: Title: Overall Performance of Turbid Medium and Hybrid Radiative Transfer Models Destination: subsection.3.3 Item: Title: Performance of Turbid Medium and Hybrid Radiative Transfer Models by Campaign Destination: subsection.3.4 Item: Title: Discussion Destination: section.4 Children: Item: Title: Suitability of SAIL-Based Retrieval Algorithms over Heterogeneous Canopies Destination: subsection.4.1 Item: Title: Utility of Hybrid Radiative Transfer Models over Heterogeneous Canopies Destination: subsection.4.2 Item: Title: Limitations and Perspectives for Future Work Destination: subsection.4.3 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: section.6 Info: Title: Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies Author: Luke A. Brown, Harry Morris, Andrew MacLachlan, Francesco D’Adamo, Jennifer Adams, Ernesto Lopez-Baeza, Erika Albero, Beatriz Martínez, Sergio Sánchez-Ruiz, Manuel Campos-Taberner, Antonio Lidón, Cristina Lull, Inmaculada Bautista, Daniel Clewley, Gary Llewellyn, Qiaoyun Xie, Fernando Camacho, Julio Pastor-Guzman, Rosalinda Morrone, Morven Sinclair, Owen Williams, Merryn Hunt, Andreas Hueni, Valentina Boccia, Steffen Dransfeld and Jadunandan Dash Subject: 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-2, NRMSD = 64 84%, bias = -0.17 0.89 g m-2). 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-2, NRMSD = 52 73%, bias = 0.03 0.42 g m-2) 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. 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