Evaluating the potential of GeoEye data in retrieving LAI at watershed scale

LAI is defined as one sided green leaf area per unit ground area in broadleaf canopies and is an important input parameter to monitor crop growth conditions and to improve the performance of crop yield models. Because direct measurements of LAI are usually time-consuming and require continuous updates, remote sensing is an alternative to estimate this attribute over large areas as watershed scale. The primary objective of this work was to derive a reliable LAI estimation model from VHR satellite data to be compared with moderate resolution satellite products in order to improve LAI estimation performance for next validation activities. Due to lack of contemporaneous satellite and on-site sensor data acquisitions and intrinsic complexity of physical models, in our study case the semi-empirical approach with the CLAIR model was applied. It is based on an inverse exponential relationship between LAI and the WDVI (Weighted Difference Vegetation Index) related to different land covers. LAI values were generated from multispectral GeoEye-1 sensor data covering a time space of 5 years (2009-2013) to study crop phenological stages on the study area of the Carapelle watershed located in the North of Puglia region (Southern Italy). Data were preliminarily pre-processed (geometric and radiometric correction), classified (ISODATA method) and texture based analyzed in order to extract the vegetated areas (mainly cereal crops). Finally, the resulted maps were compared with moderate resolution satellite data by reaching a possible correspondence.

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