Temporal adjustment approach for high-resolution continental scale modeling of soil organic carbon.

ABSTRACT: Open-source legacy data available for training soil organic carbon (SOC) models are limited and not uniformly distributed in space or time. While some process-based models predict SOC changes, most of the large-scale data-driven SOC modeling efforts overlook temporal shifts. Accounting for the expected temporal drift allows us to increase the accuracy of dataset available for machine learning models. Here we present an approach for creating proximity-based distance matrices using the legacy data available in contiguous US (CONUS) and generating spatially resolved temporal shift projections that adjust observations to the target date. The approach was evaluated by comparing SOC observations projected to two reference years, SOC₁₉₈₀ and SOC₂₀₂₀ and without temporal adju

SUBMITTER: Bokati L 

PROVIDER: S-EPMC11846914 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature