Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling

Abstract

Leaf pigment contents, such as chlorophylls a and b content (C a b ) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI < 1 m 2 /m 2 ), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C a b and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C a b , and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m 2 /m 2 , R 2 = 0.81); the band ratio ρ 0.750 μ m ρ 0.550 μ m retrieved C a b more accurately than other chlorophylls indices (RMSE = 5.21 μ g/cm 2 , R 2 = 0.73); RMSE over the 0.5–0.55 μ m interval showed encouraging results for Car estimations.

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

“…However, strong limitations of RTM inversion are also implied, and a reliable LAI spatialization in heterogeneous canopies needs to be based, in the future, on more detailed parameterization of the traits which are co-varying with LAI. RTM models show evident intrinsic limitations in their capacity to simulate heterogeneous canopies [76,77], and do not take fully into account some parameters, e.g., the presence of non-photosynthetic material in the canopy [58]. Furthermore, the GSA showed the impact of many different input parameters on LAI retrieval, and only very preliminary information on the spectral impact of some RTM parameters (e.g., canopy brown pigments and anthocyanins) is available in the literature [76,78].…”

Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente

“…La combinación del RTM 3-D FLIGHT y el RTM 1-D PROSAIL permitió simular los factores de reflectividad de un ecosistema mixto tipo tree-grass en función de los parámetros de los dos estratos de vegetación presentes en el ecosistema: pasto y arbolado, durante las principales etapas fenológicas del ecosistema. Otros autores han aplicado RTMs 3D a la simulación de este tipo de ecosistemas (Hernández-Clemente et al, 2017;Miraglio et al, 2020). Sin embargo, la mayoría de estos trabajos se centraron en la representación de los factores de reflectividad en función de las propiedades del dosel arbóreo, considerando como elemento subyacente, un suelo desnudo o una cubierta vegetal estática representada a partir de espectros medidos o simulados.…”

Phenological Dynamics Characterization of Alignment Trees with Sentinel-2 Imagery: A Vegetation Indices Time Series Reconstruction Methodology Adapted to Urban Areas

“…Finally, this methodology is generic and appears as applicable on any large-enough group of mono-species urban vegetation, the main limitation being the resolution of the satellite used in the study. The phenology of mono-species grouped trees located in non-urban environments where mixed pixel problems can appear, such as in orchards [75,76] or woodlands/savannas [77][78][79], might be also studied with the proposed methodology.…”