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Multi Crop Estimation of LAI from Sentinel-2 VIs with Parametric Regression Approach: Comparison of Performances and VIs Sensitivity

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Book cover Geomatics for Green and Digital Transition (ASITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1651))

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Abstract

Leaf Area Index (LAI) is a key variable for spatiotemporal modelling and analysis of several land surface processes. LAI can be successfully estimate by means of Vegetation Indices (VIs), retrieved from multispectral satellite images, however the different VIs show variable estimation uncertainty in relation to vegetation characteristics and soil background condition. In particular, VIs can show saturation behaviour at medium/high vegetation density. Thus, in this study we aimed at implementing parametric approach considering VIs belonging to three different classes computed on visible, red-edge and short-wave infrared spectral band combination provided by (multi spectral instrument) MSI sensor onboard Sentinel-2 satellites constellation. Results show that all VIs are generally well correlated to ground LAI, among the 11 tested ones EVI, NDI45 and NBR shows best results for the three considered categories.

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Acknowledgement

The authors wish to thank the staff from Scuola Superiore Sant’Anna for the filed trial management and ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale) for their valuable support in the pre-processing stage of Sentinel-2 data. The study was part of the project E-Crops “TECNOLOGIE PER L’AGRICOLTURA DIGITALE SOSTENIBILE “(PON Ricerca e Innovazione 2014–2020 - Agrifood) and SOS-AP “SOluzioni Sostenibili per l’Agricoltura di Precisione in Lombardia” (FEASR funded by Lombardy PSR 2014–2021).

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Authors and Affiliations

  1. Italian National Research Council, Institute for Electromagnetic Sensing of the Environment, Via Bassini 15, 20133, Milan, Italy

    Margherita De Peppo, Francesco Nutini, Gabriele Candiani & Mirco Boschetti

  2. Dipartimento di Scienze Agrarie e Ambientali-Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133, Milano, Italy

    Giorgio Ragaglini

  3. Institute for Advanced Study of Pavia (IUSS), Palazzo del Broletto, Piazza della Vittoria 15, 27100, Pavia, Italy

    Andrea Taramelli

  4. Institute for Environmental Protection and Research (ISPRA), via Vitaliano Brancati 48, 00144, Rome, Italy

    Andrea Taramelli & Federico Filipponi

Authors
  1. Margherita De Peppo
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  2. Francesco Nutini
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  3. Gabriele Candiani
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Correspondence to Margherita De Peppo .

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Editors and Affiliations

  1. University of Torino, Grugliasco, Italy

    Enrico Borgogno-Mondino

  2. University of Pisa, Pisa, Italy

    Paola Zamperlin

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De Peppo, M. et al. (2022). Multi Crop Estimation of LAI from Sentinel-2 VIs with Parametric Regression Approach: Comparison of Performances and VIs Sensitivity. In: Borgogno-Mondino, E., Zamperlin, P. (eds) Geomatics for Green and Digital Transition. ASITA 2022. Communications in Computer and Information Science, vol 1651. Springer, Cham. https://doi.org/10.1007/978-3-031-17439-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-17439-1_16

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