Soil moisture content of northern peatlands from close-range spectral data

Peatlands play a critical role in the global carbon cycle. Their carbon exchange functions are highly sensitive to moisture conditions and water table levels, which are increasingly threatened by climate change and land-use modifications. While satellite remote sensing enables large-scale monitoring of peatland moisture conditions, precise close-range measurements are essential for calibrating and validating these methods. Previous close-range studies have typically focused on data from a limited number of peatland sites, creating gaps in the understanding of soil surface moisture estimation across diverse peatland types and climatic zones. Our study addressed these gaps using close-range hyperspectral field measurements from 13 northern peatlands spanning hemiboreal to Arctic regions in Estonia and Finland. We evaluated multiple techniques, including spectral indices, continuum removal, full-spectrum analysis, and Continuous Wavelet Transform (CWT), with Kernel Partial Least Squares (KPLS) regression. Additionally, we compared hyperspectral methods with simulated multispectral data. Our results showed that hyperspectral data with CWT processing provided the most accurate soil moisture estimations across diverse peatland environments (R 2 = 0.65 and RMSE = 17.7 %). Additionally, the model based on multispectral bands, achieved moderate prediction accuracy (R 2 = 0.53 and RMSE = 20.4 %), which was competitive with full-spectrum analysis (R 2 = 0.58 and RMSE = 19.4 %). Separating peatlands into minerotrophic and ombrotrophic categories further improved prediction accuracy, particularly for the minerotrophic sites (with CWT: R 2 = 0.74 and RMSE = 15.9 %). In contrast, spectral indices performed poorly (R 2 ≤ 0.26 and RMSE ≥25.8 %), suggesting that they may be unsuitable for large-scale remote sensing applications. • Peatland moisture estimation using hyperspectral field data from 13 northern sites • Soil moisture predicted with four methods using kernel partial least squares • Continuous wavelet transform achieved the highest prediction accuracy • Sentinel-2 bands showed potential for moisture estimation • Separating peatlands by trophic type improved model accuracy