| RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA |
| Paper ID : 1071-GEOSPATIAL (R2) |
| Authors: |
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Mohammad Hajeb *1, Saeid Hamzeh1, Seyed Kazem Alavipanah2, Jochem Verrelst3 1Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran 21 Dept. of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran- 3Image Processing Laboratory (IPL), Parc Científic, Universitat de Val`encia, Val`encia, Spain |
| Abstract: |
| The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m2/m2 for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI. |
| Keywords: |
| PRISMA, Leaf Area Index, Bayesian Regularized Artificial Neural Network, Hyperspectral, Sugarcane, Bootstrapping |
| Status : Paper Accepted (Oral Presentation) |
