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Remote Sens. 2015, 7(10), 12859-12886; doi:10.3390/rs71012859

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

Institute for Electromagnetic Sensing of the Environment, National Research Council (IREA-CNR), via Bassini 15, Milan 20133, Italy
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Author to whom correspondence should be addressed.
Academic Editors: Tao Cheng, Zhengwei Yang, Yoshio Inoue, Yan Zhu, Weixing Cao, Clement Atzberger and Prasad S. Thenkabail
Received: 28 May 2015 / Revised: 22 September 2015 / Accepted: 24 September 2015 / Published: 30 September 2015
(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)
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Abstract

The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features. View Full-Text
Keywords: agriculture; summer crops; Landsat 8 OLI; COSMO-SkyMed; rule-based classification; Random Forest; Enhanced Vegetation Index (EVI); Red Green Ratio Index (RGRI); Normalized Difference Flood Index (NDFI); multi-temporal agriculture; summer crops; Landsat 8 OLI; COSMO-SkyMed; rule-based classification; Random Forest; Enhanced Vegetation Index (EVI); Red Green Ratio Index (RGRI); Normalized Difference Flood Index (NDFI); multi-temporal
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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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MDPI and ACS Style

Villa, P.; Stroppiana, D.; Fontanelli, G.; Azar, R.; Brivio, P.A. In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features. Remote Sens. 2015, 7, 12859-12886.

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