China - Guangdong/Leizhou

Project Overview

Crop identification and Crop Area Estimation

  • Object-based Image Analysis and The Support Vector Machine (SVM) classification method
  • The fusion of SAR and Optical satellite data
  • Statistical analysis
  • Determine to what level of accuracy Radarsat-2 can classify crops with different cropping system in China
  • Determine whether Radarsat-2 data alone can produce classification accuracies targeted by CAAE (overall and individual accuracies of 90%) at the early stages of the growing season
  • Develop comprehensive algorithms using Radarsat-2 in combination with other data resources in the operational crop monitoring system

Crop Condition/Stress

  • Normalized Difference Vegetation Index (NDVI)
  • The ground truth information

Yield Prediction and Forecasting

  • Artificial Neural networks (NN) method

Phenological Events and Estimation of Rice biophysical variables

  • Multiple regression analysis
  • Leaf Area Index (LAI) measured with Hemispherical lens 

Crop Biophysical Parameters Estimation

  • Leaf Area Index monitoring using Radarsat-2 images at regional scale
©2015 Joint Experiment for Crop Assessment and Monitoring
© HER MAJESTY THE QUEEN IN RIGHT OF CANADA SA MAJESTE LA REINE DU CHEF DU CANADA (2015)