Modelling water table depth at rewetted peatlands with Sentinel-1 and Sentinel-2
Water table depth is the primary consideration during peatland rewetting, as a post-industrial peatland transitions from a degraded system with bare peat surfaces to a natural one. For rewetting to be successful, water table depth should be maintained in the upper 0.2 m of the soil to promote carbon...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000446 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Water table depth is the primary consideration during peatland rewetting, as a post-industrial peatland transitions from a degraded system with bare peat surfaces to a natural one. For rewetting to be successful, water table depth should be maintained in the upper 0.2 m of the soil to promote carbon sequestration while minimising net greenhouse gas emissions. There is evidence that satellite remote sensing techniques may be effective tools at monitoring water table depth. However, these techniques have been seldom used on degraded bare peat bogs, despite their excellent potential as monitoring tools during the restoration process. The aims of this paper are to (1) systematically test the relationship between radar backscatter and water table depth (2) compare decision tree regression algorithms to evaluate the potential of multi-sensor remote sensing in peatland management, and (3) make novel estimations of site-wide water table depth using a multi-sensor approach. This paper applies multi-sensor machine learning techniques to two post-industrial harvesting degraded peatlands, which are currently undergoing rewetting. Combined, these peatlands have nearly three years (2021–2023) of water table measurements, from over 50 piezometers. These data were used to train machine learning models, resulting in R2 values ranging from 0.72 to 0.78, and RMSE values of 0.14 m and 0.12 m. Significant variation in water level throughout the year was observed, suggesting that the ability for a peatland to successfully sequester carbon may be temporally variable. With this study, we provide a timely assessment of restoration efforts at anthropologically degraded bare peat peatlands. This work proves the utility of remote sensing techniques in tracking restoration progress, and may inform future strategies in peatland restoration, rewetting, and monitoring. |
|---|---|
| ISSN: | 2666-0172 |