Next Article in Journal
UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline
Next Article in Special Issue
Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping
Previous Article in Journal
Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data
Previous Article in Special Issue
Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(6), 911; https://doi.org/10.3390/rs10060911

Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy

1
Institute for Space Applications and Remote Sensing, National Observatory of Athens, I. Metaxa and Vas. Pavlou St, Penteli, 15236 Athens, Greece
2
INTIA Tecnologías e Infraestructuras Agroalimentarias, Av. Serapio Huici, 22, 31610 Villava, Spain
*
Author to whom correspondence should be addressed.
Received: 12 March 2018 / Revised: 6 June 2018 / Accepted: 7 June 2018 / Published: 8 June 2018
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
Full-Text   |   PDF [5667 KB, uploaded 9 June 2018]   |  

Abstract

This work investigates a Sentinel-2 based crop identification methodology for the monitoring of the Common Agricultural Policy’s (CAP) Cross Compliance (CC) and Greening obligations. In this regard, we implemented and evaluated a parcel-based supervised classification scheme to produce accurate crop type mapping in a smallholder agricultural zone in Navarra, Spain. The scheme makes use of supervised classifiers Support Vector Machines (SVMs) and Random Forest (RF) to discriminate among the various crop types, based on a large variable space of Sentinel-2 imagery and Vegetation Index (VI) time-series. The classifiers are separately applied at three different levels of crop nomenclature hierarchy, comparing their performance with respect to accuracy and execution time. SVM provides optimal performance and proves significantly superior to RF for the lowest level of the nomenclature, resulting in 0.87 Cohen’s kappa coefficient. Experiments were carried out to assess the importance of input variables, where top contributors are the Near Infrared (NIR), vegetation red-edge, and Short-Wave Infrared (SWIR) multispectral bands, and the Normalized Difference Vegetation (NDVI) and Plant Senescence Reflectance (PSRI) indices, sensed during advanced crop phenology stages. The scheme is finally applied to a Lansat-8 OLI based equivalent variable space, offering 0.70 Cohen’s kappa coefficient for the SVM classification, highlighting the superior performance of Sentinel-2 for this type of application. This is credited to Sentinel-2’s spatial, spectral, and temporal characteristics. View Full-Text
Keywords: crop identification; support vector machines; random forest; common agricultural policy; Sentinel-2 MSI; Landsat-8 OLI; feature importance; multispectral image time-series crop identification; support vector machines; random forest; common agricultural policy; Sentinel-2 MSI; Landsat-8 OLI; feature importance; multispectral image time-series
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Sitokonstantinou, V.; Papoutsis, I.; Kontoes, C.; Lafarga Arnal, A.; Armesto Andrés, A.P.; Garraza Zurbano, J.A. Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy. Remote Sens. 2018, 10, 911.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top