| ESTIMATING CANOLA’S BIOPHYSICAL PARAMETERS FROM TEMPORAL, SPECTRAL, AND POLARIMETRIC IMAGERY USING MACHIN LEARNING APPROACHES |
| Paper ID : 1069-SMPR |
| Authors: |
|
Omid Reisi-Gahrouei *1, Saeid Homayouni2, Abdolreza Safari1 1School of Surveying and Geospatial Engineering, College of Engineering, U. of Tehran, Tehran, Iran 2Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, Canada |
| Abstract: |
| The objective of this study was to investigate the application of multi-temporal optical and polarimetric synthetic aperture radar (PolSAR) Earth observations for crop monitoring and mapping. Crop dry biomass, Leaf Area Index (LAI), and Plant Water Content (PWC) were estimated and assessed using Machin learning approaches. An accurate estimation of crop parameters provides essential information to increased food production and plays a crucial role in management of agricultural lands. Multispectral and PolSAR data provide valuable observations of spectral and structural properties which are essential for crops parameter modelling. The Earth observations used in this paper were collected by RapidEye satellites and Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system in the summer of 2012, over an agriculture area in Winnipeg, Manitoba, Canada. The RapidEye vegetation indices and UAVSAR polarimetric parameters were used as inputs in artificial neural network (ANN) and support vector regression (SVR) models for canola biophysical parameters estimation. The best models were provided. |
| Keywords: |
| Satellite Earth Observations, Crop parameters, Support Vector Regression (SVR), Artificial Neural Network (ANN) |
| Status : Conditional Accept (Oral Presentation) |
