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

• Tác giả: Laxman Bokati, Saurav Kumar, Rahul Perepi, Javad Robatjazi, Reshmi Sarkar, Anil Somenahally, Rocky Talchabadel
• Ngôn ngữ: eng
• Ký hiệu phân loại: 346.02 Contracts and agency
• Thông tin xuất bản: England : Scientific reports , 2025
• Bộ sưu tập: NCBI
• ID: 581604

Tóm tắt

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, SOC