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Correction published on 15 July 2020, see Remote Sens. 2020, 12(14), 2263.
Article

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

1
ONERA/DOTA, Université de Toulouse, F-31055 Toulouse, France
2
Université Fédérale Toulouse Midi-Pyrénées, 41 Allées Jules Guesde, 31013 Toulouse, France
3
Dept. Land, Air and Water Resources, University of California, Davis, One Shield Avenue, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 28; https://doi.org/10.3390/rs12010028
Received: 28 October 2019 / Revised: 27 November 2019 / Accepted: 15 December 2019 / Published: 19 December 2019
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. View Full-Text
Keywords: AVIRIS; DART; leaf pigments; Mediterranean forest; tree–grass ecosystem AVIRIS; DART; leaf pigments; Mediterranean forest; tree–grass ecosystem
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MDPI and ACS Style

Miraglio, T.; Adeline, K.; Huesca, M.; Ustin, S.; Briottet, X. Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling. Remote Sens. 2020, 12, 28. https://doi.org/10.3390/rs12010028

AMA Style

Miraglio T, Adeline K, Huesca M, Ustin S, Briottet X. Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling. Remote Sensing. 2020; 12(1):28. https://doi.org/10.3390/rs12010028

Chicago/Turabian Style

Miraglio, Thomas, Karine Adeline, Margarita Huesca, Susan Ustin, and Xavier Briottet. 2020. "Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling" Remote Sensing 12, no. 1: 28. https://doi.org/10.3390/rs12010028

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