Downscaling Satellite-derived Optical Trapezoid Model with Uncrewed Aerial Vehicle Data for Peatland Water Table Monitoring - Heikkinen, Saara; Räsänen, Aleksi; Kuzmin, Anton; Korpelainen, Pasi; Kumpula, Timo; Isoaho, Aleksi (Springer Nature, 2026)
Optical remote sensing, particularly satellite-derived optical trapezoid model (OPTRAM), can be used as a proxy to monitor peatland water table (WT), a key determinant for peatland condition. So far, OPTRAM has been used only in temporal monitoring of WT at coarse spatial resolution while it has not been tested to detect spatial patterns of WT in spatially heterogeneous northern peatlands. To address the abovementioned gap, we downscale four differently parameterized Sentinel‑2 OPTRAMs with the help of optical, thermal, and topographic uncrewed aerial vehicle (UAV) variables and random forest modeling in two open peatlands in northern Finland covered by spatially extensive field measurements of WT (n = 95). We (1) assess how parameterization of OPTRAM affects OPTRAM-WT correlation, (2) test whether downscaled OPTRAM correlates stronger with WT than the original OPTRAM, and (3) compare OPTRAM to other remote sensing variables calculated from Sentinel‑2 and UAV data. Our results showed that OPTRAM parameterization strongly affects OPTRAM-WT correlation, with Spearman correlation (rs) ranging between 0.23–0.53. Random forest-based downscaling models had a relatively high explained variance (45.6–72.4%). Downscaling increased rs by 0.09–0.16 units, up to 0.62 with the best-performing parameterization, and revealed the spatial patterns of WT more realistically than Sentinel‑2 OPTRAM. Other UAV and Sentinel-2 variables had differing correlations with WT, with greenness and water cover indices having stronger correlations with WT than OPTRAM (|rs| up to 0.67). Our results encourage the use of downscaling methods at high spatial resolutions and integrating multi-sensor and machine learning methods to generate high spatial and temporal resolution peatland WT monitoring approaches.
Keywords: drones; remote sensing; satellite imagery; soil moisture; wetland; uncrewed aerial systems