Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 3; p. 438
• Main Authors: Kakhani, Nafiseh, Alamdar, Setareh, Kebonye, Ndiye Michael, Amani, Meisam, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Accuracy; Algorithms; Carbon; Carbon content; conformal prediction; data collection; digital soil mapping; ecosystems; Emissions; Estimation; Europe; Greenhouse gases; Machine learning; Methods; Model accuracy; model uncertainty; Monte Carlo simulation; Neural networks; Nutrient cycles; Organic carbon; Organic soils; prediction; Predictions; Remote sensing; Sensors; soil organic carbon; soil quality; Soil sciences; Soils; Statistical analysis; Uncertainty; uncertainty quantification; Vegetation; Germany; Europe

Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revea...