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Open AccessArticle

Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping

Microwaves and Radar Institute, German Aerospace Center (DLR), Münchener Straße 20, 82234 Weßling, Germany
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Remote Sens. 2020, 12(5), 847; https://doi.org/10.3390/rs12050847
Received: 4 February 2020 / Revised: 28 February 2020 / Accepted: 2 March 2020 / Published: 6 March 2020
(This article belongs to the Special Issue SAR for Forest Mapping)
This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85 % , when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions. View Full-Text
Keywords: forest mapping; Sentinel-1; short-time-series; synthetic aperture radar; interferometric coherence; temporal decorrelation; Random Forests; spatial texture forest mapping; Sentinel-1; short-time-series; synthetic aperture radar; interferometric coherence; temporal decorrelation; Random Forests; spatial texture
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MDPI and ACS Style

Pulella, A.; Aragão Santos, R.; Sica, F.; Posovszky, P.; Rizzoli, P. Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping. Remote Sens. 2020, 12, 847. https://doi.org/10.3390/rs12050847

AMA Style

Pulella A, Aragão Santos R, Sica F, Posovszky P, Rizzoli P. Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping. Remote Sensing. 2020; 12(5):847. https://doi.org/10.3390/rs12050847

Chicago/Turabian Style

Pulella, Andrea; Aragão Santos, Rodrigo; Sica, Francescopaolo; Posovszky, Philipp; Rizzoli, Paola. 2020. "Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping" Remote Sens. 12, no. 5: 847. https://doi.org/10.3390/rs12050847

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