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
Project Objectives
Estimating Crop Conditions
Operational Implementation Plan
Crop Conditions
- Biological Stress
Measuring Phenological Events
Phenological Events
- Seeding
- Seedling
- Vegetative Growth
- Flowering
- Fruit Development
- Maturity
- Harvest
Estimation of Biophysical Variables
Operational Implementation Plan
Biophysical Variables
- LAI (Leaf Area Index)
Site Description
Landscape Topography | flat area, and low hills around |
---|---|
Typical Field Size | 0.1 to 10.0 ha |
Climatic Zone | Tropics, warm |
Major Crops and Calendars | Rice (Normal): Rice (Late): Sugar cane (Normal): Peanut (Normal): |
Soil Type & Texture | |
Soil Drainage Class | ["Well drained"] |
Irrigation Infrastructure | ["Surface irrigation"] |
Other Site Details |
In Situ Observations
Planting pattern, growing stage, seeding/transplanting date, plant height, Leaf area index(LAI), weather condition and digital photos during key rice growing stages concurrently with the satellite pass
- Crop Type(s):
- Collection Protocol:
25 sample plots
- Frequency:
Crop height
- Crop Type(s):
- Collection Protocol:
- Frequency: About 2 times each crop year
Accuracy of rice identification
- Crop Type(s):
- Collection Protocol:
Hundreds of points for land cover type were collected once per season
- Frequency:
Crop type
- Crop Type(s):
- Collection Protocol:
GPS
- Frequency: 2 times each crop year
LAI
- Crop Type(s):
- Collection Protocol:
LAI2000
- Frequency: About 2 times each crop year
EO Data
Optical Data Requirements
SAR Data Requirements
Passive Microwave Data Requirements
Thermal Data Requirements
Results
Documents and Files
Links to paper
Project Reports
Study Team
Team Leader
Other Team Members