This paper presents a prototype crop production monitoring pipeline which identifies agricultural fields planted with small grains over 19 countries in the Middle East and North Africa (MENA) and monitors those crops over the growing season. The technical approach employs an boundary-based image segmentation algorithm to define units of consistent land use, and clusters Sentinel-2 normalized difference vegetation index (NDVI) time series within the fields to identify small grains, without requiring labeled examples. The small grain fields are then monitored over the growing season on a monthly basis using time-integrated NDVI beginning at an interval from the planting date to the end of the target month. Classification accuracy is estimated at 82% for the test case, and crop deviations from the mean and/or reference year(s) have been detected within 1-2 months of planting, and are reliably detected several months before harvest.

Date Published: 21 October 2019
PDF: 11 pages
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 1114907 (21 October 2019); doi: 10.1117/12.2532840

Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)