Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 7; no. 9; pp. 12356 - 12379
• Main Authors: Inglada, Jordi, Arias, Marcela, Tardy, Benjamin, Hagolle, Olivier, Valero, Silvia, Morin, David, Dedieu, Gerard, Sepulcre, Guadalupe, Bontemps, Sophie, Defourny, Pierre
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2015
• Subjects: Assessments; Automation; crop type mapping; Crops; Data collection; land cover; Landsat; Landsat 8; Landscapes; random forests; Remote sensing; satellite image time series; Satellite imagery; Sentinel-2; Spatial resolution; SPOT4 (Take5); support vector machines; Temporal logic
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs70912356

Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.