Next Article in Journal
Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach
Next Article in Special Issue
In-Orbit Radiometric Calibration and Stability Monitoring of the PROBA-V Instrument
Previous Article in Journal
Interseismic Deformation of the Altyn Tagh Fault Determined by Interferometric Synthetic Aperture Radar (InSAR) Measurements
Previous Article in Special Issue
Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 232; doi:10.3390/rs8030232

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

Earth and Life Institute, Université catholique de Louvain, 2 Croix du Sud, 1348 Louvain-la-Neuve, Belgium
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 6 January 2016 / Revised: 25 February 2016 / Accepted: 29 February 2016 / Published: 11 March 2016
View Full-Text   |   Download PDF [16197 KB, uploaded 11 March 2016]   |  


Early warning systems for food security require accurate and up-to-date information on the location of major crops in order to prevent hazards. A recent systematic analysis of existing cropland maps identified priority areas for cropland mapping and highlighted a major need for the Sahelian and Sudanian agrosystems. This paper proposes a knowledge-based approach to map cropland in the Sahelian and Sudanian agrosystems that benefits from the 100-m spatial resolution of the recent PROBA-V sensor. The methodology uses five temporal features characterizing crop development throughout the vegetative season to optimize cropland discrimination. A feature importance analysis validates the efficiency of using a diversity of temporal features. The fully-automated method offers the first cropland map at 100-m using the PROBA-V sensor with an overall accuracy of 84% and an F-score for the cropland class of 74%. The improvements observed compared to existing cropland products are related to the hectometric resolution, to the methodology and to the quality of the labeling layer from which reliable training samples were automatically extracted. Classification errors are mainly explained by data availability and landscape fragmentation. Further improvements are expected with the upcoming enhanced cloud screening of the PROBA-V sensor. View Full-Text
Keywords: cropland; Sahelian and Sudanian agrosystems; knowledge-based; PROBA-V; time series cropland; Sahelian and Sudanian agrosystems; knowledge-based; PROBA-V; time series

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lambert, M.-J.; Waldner, F.; Defourny, P. Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m. Remote Sens. 2016, 8, 232.

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



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