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Open AccessArticle

Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series

1
Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada
2
Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, ON K1S 5B6, Canada
3
Space and ISR Applications Section, Defence Research and Development Canada, Ottawa, ON K2K 2Y7, Canada
4
Terradue SL, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(15), 2465; https://doi.org/10.3390/rs12152465
Received: 25 June 2020 / Revised: 27 July 2020 / Accepted: 29 July 2020 / Published: 31 July 2020
(This article belongs to the Collection Feature Papers for Section Environmental Remote Sensing)
Differences in topographic structure, vegetation structure, and surface wetness exist between peatland classes, making active remote sensing techniques such as SAR and LiDAR promising for peatland mapping. As the timing of green-up, senescence, and hydrologic conditions vary differently in peatland classes, and in comparison with upland classes, full growing-season time series SAR imagery was expected to produce higher accuracy classification results than using only a few select SAR images. Both interferometric coherence, amplitude and difference in amplitude time series datasets were assessed, as it was hypothesized that these may be able to capture subtle changes in phenology and hydrology, which in turn differentiate classes throughout a growing season. Groups of variables were compared for their effectiveness in Random Forest classification for both Sentinel-1 and Radarsat-2. The Shapley value was used to determine the contribution of each group of variables in thirty scenarios, and Mean Decrease in Accuracy was compared to evaluate its ability to rank variables by relative importance. Despite being dual-pol, the results of classifications using Sentinel-1 coherence (12-day repeat) were significantly better than using fully polarimetric RADARSAT-2 coherence (24-day repeat), likely owing to the difference in baseline and specific acquisition dates of the data in this study. Overall, full growing season Sentinel-1 coherence time series produced higher accuracy results than fully polarimetric quad pol RADARSAT-2 coherence amplitude, difference in amplitude and polarimetric decomposition time series. Using a full growing season of time-series imagery in classification resulted in higher accuracy than using a few dates over a growing season. Using mean decrease in accuracy to rank and reduce variables resulted in a weaker classification than if the entire time series is used. View Full-Text
Keywords: InSAR; coherence; time series; peatland; classification; random forest; Shapley value InSAR; coherence; time series; peatland; classification; random forest; Shapley value
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MDPI and ACS Style

Millard, K.; Kirby, P.; Nandlall, S.; Behnamian, A.; Banks, S.; Pacini, F. Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series. Remote Sens. 2020, 12, 2465.

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