At the 2017 CSI Meeting in Cali, Murali Gumma from ICRISAT presented an impressive progress on the mapping of super high resolution (30 meter) croplands in South Asia using Landsat 8 imagery (16-day, 8-band). More than 2,000 field samples were collected to train a random forest classification algorithm and used to generate five types of classified data products:
- Cropland vs. non-croplands
- Irrigated vs, rainfed (including water bodies)
- Cropping intensities: single, double, or continuous
- Major crop types
- Cropland change
This method was successfully applied in more than 446 million ha area spanning across five countries in South Asia with the accuracy levels ranging from 80% (for rainfed vs. irrigated classification) to 90% (for cropland vs non-cropland classification). This approach is now being used to generate global-scale datasets.
Another welcoming update from Murali’s presentation was on the development of iCrops mobile app, being tested for streamlining groundtruthing data collection. We discussed the exciting potential of CGIAR-wide adoption of this tool to generate a large groundtruthing database in collaboration, which will be an invaluable global public good.
Learn more about this study and ICRISAT’s geospatial analysis work from Murali’s presentation: Murali Gumma (ICRISAT) 2017 – Center Update – CGIAR-CSI in Cali (Sep 22)