B43A-0522
Validation of Landsat 8 satellite-derived LAI from field measurements in a durum-wheat cropped area in south-eastern Italy

Thursday, 17 December 2015
Poster Hall (Moscone South)
Gabriele Buttafuoco, CNR National Research Council, Rome, Italy, Annamaria Castrignano, CRA SCA, Bari, Italy, Piero Toscano, CNR IBIMET, Firenze, Italy and Michele Rinaldi, Cereal Research Center - CREA CER, Foggia, Italy
Abstract:
Leaf area index (LAI) is one of the key biophysical variables to describe land surface processes of fundamental importance for vegetation, such as photosynthesis, transpiration and energy balance. Current spatial missions allow to estimate LAI at global scale, however product validation is needed for its reliable use.

In this paper, a methodology is described to derive reference LAI maps from ground-based measurements at the JECAM site of Capitanata using Landsat 8 imagery as auxiliary information and to estimate prediction uncertainty.

LAI was measured with LAI-2000 Plant Canopy Analyzer (LI-COR) in 30 experimental sampling units (ESU) of 10 m by 10 m size within a polygon of about 3 km by 3 km size. Two surveys were carried out in 2014: on March 18-20 and on May 9-13.

To integrate secondary finer-resolution information in primary sparse variable modelling, multicollocated cokriging was used, where the secondary variable for cokriging estimate is used at the target location and also at all the locations where the primary variable is defined within the neighbourhood of interpolation. To assess prediction uncertainty, Confidence Interval (CI) was calculated and in order to make the values comparable the relative CI was calculated by dividing by the corresponding LAI estimation and assumed as a quality index of LAI product.

LAI maps were produced for the two selected dates and from a comparison between radiometric and estimated LAI maps, it results a general high consistency with Landsat 8 imagery.

The quality maps derived from LAI estimator for the two dates with overlaid the locations of the sample data points show that to the pixels located near the samples are attributed a quality index of about 40-50% but, as the distance increases, the uncertainty becomes too large. The poor results are probably due to the too coarse sampling, with large areas without any ground-truth data of LAI.