Integrating Fourier-derived temporal features from multi-year vegetation and soil moisture time series enhances soil organic matter mapping

Abstract

Soil organic matter (SOM) is critical for soil health, ecosystem functioning, and climate regulation. Digital soil mapping (DSM) offers an efficient approach for estimating the spatial distribution of SOM, but its accuracy strongly depends on whether environmental covariates adequately represent soil spatial variability. In recent years, dynamic covariates that change over time, particularly remote sensing vegetation indices (VI) and soil moisture (SM), have been increasingly incorporated into DSM. However, many studies still rely on a static perspective (usually using temporally averaged representations) for establishing covariates, which leads to the fact that the intrinsic temporal dynamic features of those covariates cannot be well captured. In this study, we extracted temporal dynamic covariates from VI and SM time series data over multiple time windows (1-year, 3-year, 5-year, and 10-year) using a Fast Fourier Transform (FFT) approach and evaluated their performance in SOM mapping across paddy fields, drylands, and forests in Anhui Province, China, using a random forest model. The results indicate that the FFT-derived VI-FFT and SM-FFT covariates improved SOM mapping accuracy across all evaluated time windows, with the 1-year + 5-year combination achieving the best performance. Adding VI-FFT and SM-FFT covariates increased the metric of Lin’s concordance correlation coefficient (LCCC) by 13.9% and 12.6%, respectively. The combined use of VI-FFT and SM-FFT covariates achieved the highest predictive performance, with a model improvement of 35.0% on average. Our results underscore the importance of incorporating multi-year environmental dynamics for establishing a robust SOC-environment relationship. This suggests that future DSM efforts should move beyond static covariates by systematically extracting temporal features from historical observations of vegetation and soil moisture.