Preliminary machine‑learning study of minimal hyperspectral bands for heat and drought stress in the Rzecin peatland

Peatlands are significant carbon sinks, heat waves and drought threatens their function as long-term carbon sinks. Detecting stress in Sphagnum-dominated peatlands before damage occurs is critical for conservation and carbon accounting. Here, we present a trial framework combining high-resolution hyperspectral remote sensing with machine learning to identify minimal spectral band sets and develop peatland-specific indices for early stress detection.

This study was at the Rzecin peatland in Poland (52°45'N, 16°18'E), a poor fen. Hyperspectral measurements (350–1000nm) were acquired across 13 plots over 22 measurement campaigns, using a Piccolo Doppio dual-field-of-view spectrometer. environmental monitoring included water table depth (WTD) from plots nine using TD divers and meteorological variables (air temperature, vapour pressure deficit, precipitation) recorded at half-hourly intervals from 2020–2024.

We defined heat stress events using a compound threshold approach requiring exceedance of the 90th percentile for both daily maximum air temperature (Tair > 29.1°C) and vapour pressure deficit (VPD > 2.90 kPa) for a minimum of three consecutive days. Drought stress was characterised by plot-specific 10th percentile WTD thresholds (site median: −25.4 cm) sustained for at least five consecutive days. Bootstrap resampling (n = 1,000) quantified threshold uncertainty, yielding 95% confidence intervals of 28.6–29.6°C for temperature and 2.71–2.99 kPa for VPD thresholds.

To address the hyperspectral multicollinearity, we applied correlation-based filtering (ρ > 0.98), reducing the original 921 spectral bands to 9 representative wavelengths while preserving spectral diversity. Recursive Feature Elimination with Random Forest, validated through leave-one-plot-out cross-validation to ensure spatial independence, identified an optimal subset of eight features: Water Index (WI), Photochemical Reflectance Index (PRI), Peatland Stress-Water Index (PSWI), Normalised Difference Red-Edge Index (NDRE), Peatland Drought Index (PDI), Normalised Difference Vegetation Index (NDVI), reflectance at 800 nm, and the MERIS Terrestrial Chlorophyll Index (MTCI).

We are trying to build a peatland-specific spectral indices. The Peatland Drought Index (PDI), calculated as (R705 − R750)/(R705 + R750), exploits the red-edge region's sensitivity to both chlorophyll content and leaf water status. The Peatland Stress-Water Index (PSWI), formulated as (R860 − R550)/(R750 − R670), combines NIR water sensitivity with red-edge slope normalisation. Permutation tests (n = 1,000) demonstrated that PDI significantly outperformed NDVI in detecting VPD-related stress (Δρ = 0.054, p = 0.009), supporting the development of ecosystem-specific rather than generic vegetation indices.

Random Forest and XGBoost classifiers achieved strong discrimination between stressed and non-stressed conditions, with areas under the receiver operating characteristic curve (AUC) of 0.836 and 0.851, respectively. The water-related indices (WI, PSWI, PDI) among top-ranked features underscores the primacy of hydrological stress in peatland ecosystems. Sensitivity analysis across varying threshold percentiles (85–95%) and duration requirements (2–7 days) revealed that stress classification varied up to 10-fold, emphasising the critical importance of transparent methodological reporting in peatland remote sensing studies.

Our findings demonstrate that reliable stress detection in our peatlands can be achieved with eight spectral features, enabling potential deployment on multispectral sensor platforms. This framework could offer a transferable approach for early-warning systems in peatland conservation, supporting climate adaptation strategies for these critical ecosystem.

 

Acknowledgement: Acknowledgement: Funded by NCN (2020/39/O/ST10/00775), NAWA (BPN/PRE/2022/1/00102), DDSA (2025‑5687), and PANGEOS (CA22136‑80fe26e2).