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

Remote Sensing

Jordi Inglada²  Marcela Arias²  Benjamin Tardy²  Olivier Hagolle²  Silvia Valero²  David Morin²  Gérard Dedieu²  Guadalupe Sepulcre⁴  Sophie Bontemps⁴  Pierre Defourny⁴  Benjamin Koetz³  Clement Atzberger¹ 

[1] CESBIO-UMR 5126, 18 avenue Edouard Belin, 31401 Toulouse CEDEX 9, France;;CESBIO-UMR 5126, 18 avenue Edouard Belin, 31401 Toulouse CEDEX 9, France; E-Mails:;European Space Agency-ESRIN D/EOP-SEP, Via Galileo Galilei, 00044 Frascati, Italy; E-Mail:;Earth and Life Institute, Université Catholique de Louvain, 2 Croix du Sud bte L7.05.24, 1348 Louvain-la-Neuve, Belgium; E-Mails:

关键词: crop type mapping;    land cover;    satellite image time series;    Sentinel-2;    SPOT4 (Take5);    Landsat 8;    random forests;    support vector machines;   

DOI  :  10.3390/rs70912356

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.