title: Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland.
abstract: Soil carbon sequestration in agroecosystems could play a key role in climate change mitigation but will require accurate predictions of soil organic carbon (SOC) stocks over spatial scales relevant to land management. Spatial variation in underlying drivers of SOC, such as plant productivity and soil mineralogy, complicates these predictions. Recent advances in the availability of remotely sensed data make it practical to generate multidecadal time series of vegetation indices with high spatial resolution and coverage. However, the utility of such data largely is unknown, only having been tested with shorter (e.g., 1-2 yr) data summaries. Across a 2,000 ha subtropical grassland, we found that a long time series (28 yr) of a vegetation index (Enhanced Vegetation Index; EVI) derived from the Landsat 5 satellite significantly enhanced prediction of spatially varying SOC pools, while a short summary (2 yr) was an ineffective predictor. EVI was the best predictor for surface SOC (0-5 cm depth) and total measured SOC stocks (0-15 cm). The optimum models for SOC in the upper soil layer combined EVI records with elevation and calcium concentration, while deeper SOC was more strongly associated with calcium availability. We demonstrate how data from the open access Landsat archive can predict SOC stocks, a key ecosystem metric, and illustrate the rich variety of analytical approaches that can be applied to long time series of remotely sensed greenness. Overall, our results showed that SOC pools were closely coupled to EVI in this ecosystem, demonstrating that maintenance of higher average green leaf area is correlated with higher SOC. The strong associations of vegetation greenness and calcium concentration with SOC suggest that the ability to sequester additional SOC likely will rely on strategic management of pasture vegetation and soil fertility.
authors.affiliation: School of Natural Resources and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, Florida, 32611, USA.
authors.forename: Chris H
authors.initials: CH
authors.lastname: Wilson
authors.affiliation: School of Forest Resources and Conservation, University of Florida, 136 Newins-Ziegler Hall, Gainesville, Florida, 32611, USA.
authors.forename: T Trevor
authors.initials: TT
authors.lastname: Caughlin
authors.affiliation: School of Forest Resources and Conservation, University of Florida, 136 Newins-Ziegler Hall, Gainesville, Florida, 32611, USA.
authors.forename: Sami W
authors.initials: SW
authors.lastname: Rifai
authors.affiliation: Macarthur Agroecology Research Center, 300 Buck Island Ranch Road, Lake Placid, Florida, 33852, USA.
authors.forename: Elizabeth H
authors.initials: EH
authors.lastname: Boughton
authors.affiliation: Center for Ecosystem Science and Society, Northern Arizona University, PO Box 5620, Flagstaff, Arizona, 86011, USA.
authors.forename: Michelle C
authors.initials: MC
authors.lastname: Mack
authors.affiliation: Agronomy Department, University of Florida, 3127 McCarty Hall B, Gainesville, Florida, 32611, USA.
authors.forename: S Luke
authors.initials: SL
authors.lastname: Flory
mesh_terms: D002244:Carbon / Q000032:analysis*; D057965:Carbon Sequestration; D005431:Florida; D065948:Grassland; D058998:Remote Sensing Technology / Q000379:methods*; D012987:Soil / Q000737:chemistry*; D013997:Time Factors
chemical_list: D012987:Soil; D002244:Carbon
keywords: Google Earth Engine; Landsat time series; enhanced vegetation index; remote sensing; soil carbon sequestration; soil organic carbon; subtropical grasslands