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

Abstract

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.

Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

“…When selecting a SAR sensor for peatland mapping, considerations such as wavelength (e.g., L-band, X-band, and C-band), polarization options (with fully polarimetric sensors providing more information than dual-or single-polarisation sensors), and spatial resolution are crucial. L-band has demonstrated the ability to provide distinctive information about peatland surfaces and is relatively insensitive to vegetation variability [69]. Nonetheless, given the intricate nature of peatland ecosystems, acquiring sub-metre resolution data is imperative for accurately mapping the sporadic patterns of peatland vegetation.…”

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

“…After preprocessing the Sentinel-1 images, we calculated several statistical metrics for each pixel in the time-series stack, for each polarization (VV and VH), and for both coherence and intensity. The statistical descriptors chosen largely followed earlier studies that performed similar analysis [30,31,67]. Multi-temporal, pixel-based statistical descriptors included mean, standard deviation, coefficient of variation, median, maximum, and minimum.…”

“…Fortunately, there are some recent options designed to address the underutilization of InSAR products-one of which is the European Space Agency's Geohazards Thematic Exploitation Platform (GEP), an R&D activity designed for large scale Earth observation data processing. Millard et al [67] used this platform for Sentinel-1 InSAR processing and peatland mapping, although they noted that processing options were limited in comparison to a dedicated InSAR processing software (e.g., SNAP). Piter et al [106] discuss other cloud-based platforms including CODE-DE and the Alaska Satellite Facility's OpenSARLab and present their advantages and limitations.…”

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

“…2 However, that might not fully utilize or miss the time series change information. Using the long-term observation data directly can retain complete and rich wetland time information, and the results are more accurate [11,32].…”