DOI: 10.5061/dryad.266m0
title: Data from: Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland

description: 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 year) data
 summaries. Across a 2000 ha subtropical grassland, we found that a long
 time series (28 years) 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 years)
 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.
description: EVI Matrices and Julian
 Days, Buck Island RanchThis RData file contains
 a list called 'EVImatlist' which contains 4 components. The
 first two are matrices with LEDAPS filtered surface reflectance calculated
 EVI from Landsat 5 platform for 57 soil sampling points across pastures at
 Buck Island Ranch (Lake Placid, FL, USA). Matrix one contains a column for
 every day in the date range accessed (n = 426), whereas matrix two
 summarizes the EVI per year. NA's represent cells where the LEDAPS
 algorithm filtered out cloud and/or shadow contamination. Associated with
 the daily matrix, there is a vector called 'jd' that contains
 the julian day for that acquisition, and associated with the yearly
 matrix, there is a vector called 'yr' that represents the year
 summarized.EVImat_BIR.rdataSoil carbon stock (0-15cm)This data file contains a data frame called 'soil_fert1a' which contains soil carbon, various fertility parameters, LiDAR derived elevation, and mean vegetation greenness (from EVI across 28 years) for each of 57 soil samples collected from across Buck Island Ranch (Lake Placid, FL). The bulk-density corrected estimate of soil carbon stock is in the column called 'c_stock'. These values are based on a single sample from a hammer core taken to 15cm depth. Samples were oven-dried, weighed to assess bulk density, and analyzed for SOC/SON via EA at the University of Florida. Fertility parameters were assessed at the University of Florida extension soil testing laboratory using the Mehlich III extraction. The first appearance of each parameter represents raw data on original scale, while the second appearance, denoted by _std label, represents value after standardization onto a N(0,0.5) scale.soil_fert1a.csvSoil carbon concentration (0-5cm)This data file contains a data frame called 'soil_fert2a' which contains soil carbon concentration in the 0-5cm depth fraction, various fertility parameters, LiDAR derived elevation, and mean vegetation greenness (from EVI across 28 years) for each of 57 soil samples collected from across Buck Island Ranch (Lake Placid, FL). The soil carbon concentration is in column called ‘C_05’. These values are based on a composite sample from 12 subsamples representing the top 5 cm fraction at each sampled point. Samples were oven-dried and analyzed for SOC/SON via EA at the University of Florida. Fertility parameters were assessed at the University of Florida extension soil testing laboratory using the Mehlich III extraction. The first appearance of each parameter represents raw data on original scale, while the second appearance, denoted by _std label, represents value after standardization onto a N(0,0.5) scale.soil_fert2a.csvSoil carbon concentration (5-15cm)This data file contains a data frame called 'soil_fert3a' which contains soil carbon concentration in the 5-15cm depth fraction, various fertility parameters, LiDAR derived elevation, and mean vegetation greenness (from EVI across 28 years) for each of 57 soil samples collected from across Buck Island Ranch (Lake Placid, FL). The soil carbon concentration is in column called ‘C_515’. These values are based on a composite sample from 12 subsamples representing the 5-15 cm fraction at each sampled point. Samples were oven-dried and analyzed for SOC/SON via EA at the University of Florida. Fertility parameters were assessed at the University of Florida extension soil testing laboratory using the Mehlich III extraction. The first appearance of each parameter represents raw data on original scale, while the second appearance, denoted by _std label, represents value after standardization onto a N(0,0.5) scale.soil_fert3a.csv