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Remote Sens. 2017, 9(3), 248; doi:10.3390/rs9030248

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

1
Department of Earth Physics and Thermodynamics, Faculty of Physics, Universitat de València, Dr. Moliner 50, 46100 Burjassot, València, Spain
2
Image Processing Laboratory (IPL), Universitat de València, Catedrático A. Escardino, 46980 Paterna, València, Spain
3
Institute for Electromagnetic Sensing of the Environment, Italian National Research Council, Via Bassini 15, 20133 Milan, Italy
4
Hellenic Agricultural Organization—Demeter, Institute of Plant Breeding and Genetic Resources, 57001 Thermi, Thessaloniki, Greece
5
Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
6
Sarmap, Cascine di Barico 10, 6989 Purasca, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editors: Agnes Begue, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 12 January 2017 / Accepted: 4 March 2017 / Published: 7 March 2017
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Abstract

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R2 > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring. View Full-Text
Keywords: rice map; leaf area index (LAI); Sentinel-1A; Sentinel-2A; Gaussian process regression rice map; leaf area index (LAI); Sentinel-1A; Sentinel-2A; Gaussian process regression
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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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Campos-Taberner, M.; García-Haro, F.J.; Camps-Valls, G.; Grau-Muedra, G.; Nutini, F.; Busetto, L.; Katsantonis, D.; Stavrakoudis, D.; Minakou, C.; Gatti, L.; Barbieri, M.; Holecz, F.; Stroppiana, D.; Boschetti, M. Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index. Remote Sens. 2017, 9, 248.

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