in-season mapping of crop type with optical and x-band sar data: a classification tree approach using synoptic seasonal features

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ID: 176810
2015
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Ranked #266 of 499 articles by views in Journal of pharmacological sciences

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Abstrak
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.
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Penulis ;Paolo Villa;Daniela Stroppiana;Giacomo Fontanelli;Ramin Azar;Pietro Alessandro Brivio
Jurnal Journal of pharmacological sciences
Tahun 2015
DOI
10.3390/rs71012859
URL
http://www.mdpi.com/2072-4292/7/10/12859
Kata Kunci
agriculture random forest rule-based classification

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