Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies

Brown, Luke A.; Morris, Harry; MacLachlan, Andrew; D’Adamo, Francesco; Adams, Jennifer; Lopez-Baeza, Ernesto; Albero, Erika; Martínez, Beatriz; Sánchez-Ruiz, Sergio; Campos-Taberner, Manuel; Lidón, Antonio; Lull, Cristina; Bautista, Inmaculada; Clewley, Daniel; Llewellyn, Gary; Xie, Qiaoyun; Camacho, Fernando; Pastor-Guzman, Julio; Morrone, Rosalinda; Sinclair, Morven; Williams, Owen; Hunt, Merryn; Hueni, Andreas; Boccia, Valentina; Dransfeld, Steffen; Dash, Jadunandan

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Jun 4, 2024
• Online Publication Date: Jun 7, 2024
• Publication Date: Jul 7, 2024
• Deposit Date: Jun 27, 2024
• Publicly Available Date: Jun 27, 2024
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 12
• Pages: 2066
• DOI: https://doi.org/10.3390/rs16122066