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Calibration of CLAIR Model by Means of Sentinel-2 LAI Data for Analysing Wheat Crops Through Landsat-8 Surface Reflectance Data

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10964))

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Abstract

This study proposes a method to calibrate the semi-empirical CLAIR model, a simplified reflectance model used to estimate the Leaf Area Index (LAI) from optical data, using Landsat-8 Operational Land Imager Surface Reflectance (OLISR) data over wheat cultivation areas.

The procedure can be applied lacking both LAI field measurements and surface reflectance (SR) data by exploiting free of charge data, as the novel high-level Landsat8 OLISR and the Sentinel-2 LAI (S2 LAI) products. This last dataset was used as LAI reference at field size scale. Once calibrated, the model generates LAI information from OLISR data consistent with the S2 LAI. In this way it is possible merge the two products to obtain a finer temporal resolution LAI estimation during all the crop seasons.

The method was tested and statistically assessed on three different wheat test fields located in the Capitanata area (Apulia region, Italy).

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Authors and Affiliations

  1. DICATECh – Politecnico di Bari, Via Orabona 4, Bari, Italy

    Giuseppe Peschechera & Umberto Fratino

Authors
  1. Giuseppe Peschechera
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  2. Umberto Fratino
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Correspondence to Umberto Fratino .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Peschechera, G., Fratino, U. (2018). Calibration of CLAIR Model by Means of Sentinel-2 LAI Data for Analysing Wheat Crops Through Landsat-8 Surface Reflectance Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10964. Springer, Cham. https://doi.org/10.1007/978-3-319-95174-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-95174-4_24

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  • Online ISBN: 978-3-319-95174-4

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