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Remote Sens. 2015, 7(8), 10400-10424; doi:10.3390/rs70810400

Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

Earth and Life Institute, Université catholique de Louvain, 2 Croix du Sud, 1348 Louvain-la-Neuve, Belgium
INRA-EMMAH UMR 1114, 84914 Avignon, France
CESBIO, UMR CNES-CNRS-IRD-UPS, 18 avenue Edouard Belin, 31401 Toulouse Cedex 4, France
Author to whom correspondence should be addressed.
Academic Editors: Sylvia Sylvander, Olivier Arino, Benjamin Koetz, Clement Atzberger and Prasad S. Thenkabail
Received: 31 May 2015 / Revised: 30 July 2015 / Accepted: 4 August 2015 / Published: 13 August 2015
View Full-Text   |   Download PDF [1785 KB, uploaded 13 August 2015]   |  


With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0.83, 0.85 and 0.79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables’ time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI. Six random forest classifiers were trained and applied along the season with different inputs: spectral bands, NDVI, as well as FAPAR, LAI and FCOVER, separately and jointly. Classifications with structural biophysical variables reached end-of-season overall accuracies ranging from 73%–76% when used alone and 77% when used jointly. This corresponds to 90% and 95% of the accuracy level achieved with the spectral bands and NDVI. FCOVER appears to be the most promising biophysical variable for classification. When assuming that the cropland extent is known, crop type classification reaches 89% with spectral information, 87% with the NDVI and 81%–84% with biophysical variables. View Full-Text
Keywords: classification; biophysical variables; along the season; land cover; crop types classification; biophysical variables; along the season; land cover; crop types

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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).

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Waldner, F.; Lambert, M.-J.; Li, W.; Weiss, M.; Demarez, V.; Morin, D.; Marais-Sicre, C.; Hagolle, O.; Baret, F.; Defourny, P. Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series. Remote Sens. 2015, 7, 10400-10424.

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