Environmental Science & Technology · 2024 · 24 citations · 57 references
AgricultureEarth SciencesSoil SciencesSoil EcologySoil Organic MatterSoil ScienceGeospatial Machine LearningGlobal Soil DistributionSoil Carbon SensingSoil MappingSoil-landform RelationshipsSoil-landscape RelationshipsConcentration DistributionDecision-support ToolsPredictive Soil ModelingSoil Organic CarbonSoil Organic Carbon DistributionSoil Organic Carbon MappingSoil Spatial PredictionSpatial Variability
Soil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for a comprehensive understanding of its distribution and controls. This study explores the importance of various covariates on SOC spatial distribution at both local (up to 1.25 km) and continental (USA) scales using a deep learning approach. Our findings highlight the significant role of terrain attributes in predicting SOC concentration distribution with terrain, contributing approximately one-third of the overall prediction at the local scale. At the continental scale, climate is only 1.2 times more important than terrain in predicting SOC distribution, whereas at the local scale, the structural pattern of terrain is 14 and 2 times more important than climate and vegetation, respectively. We underscore that terrain attributes, while being integral to the SOC distribution at all scales, are stronger predictors at the local scale with explicit spatial arrangement information. While this observational study does not assess causal mechanisms, our analysis nonetheless presents a nuanced perspective about SOC spatial distribution, which suggests disparate predictors of SOC at local and continental scales. The insights gained from this study have implications for improved SOC mapping, decision support tools, and land management strategies, aiding in the development of effective carbon sequestration initiatives and enhancing climate mitigation efforts.
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