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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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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