Evaluation of remotely sensed imagery to monitor temporal changes in soil organic carbon at a long-term grazed pasture trial

Abstract

Temporal variation of soil organic carbon (SOC) is driven by land use/management practice, ecosystem conditions and climatic variation. Robust quantification of changes in SOC that is cost-effective and provides a statistical assessment of uncertainty is challenging, particularly in the face of large spatial variability and slow soil SOC changes. Remote-sensing indicators of above-ground vegetation provide some indication of the amount of fresh organic material being supplied to the soil. Although, because of the time taken for this organic material to decay and become incorporated into the soil, there will be a lag between the changes in the indicator of vegetation growth and the resulting changes in SOC. In this work, we investigate how a remotely sensed indicator of vegetation cover can be used with a lag period to predict or indicate changes in SOC for grazed pasture sites at a long-term monitoring study, which has been monitoring soil under different land uses for over forty years. We assessed how well this worked for indicating the SOC changes for different depths in the soil profile. Results suggested that a lagged remotely sensed vegetation cover—the average cover of the two preceding years—provides some indication of SOC changes for the 0–10 cm soil depth, but changes for deeper soil depths were not well predicted. Further, we investigated the potential of using soil data from a point-in-time spatial dataset (e.g. data from a baseline sampling round) to calibrate a relationship between the remotely sensed cover and SOC, which can then be applied to predict or indicate the temporal variation of SOC. Results showed this approach gave large prediction errors, likely because the temporal variation (at a fixed point in space) and spatial variation (for a fixed point in time) of SOC that is predictable by cover differences are not interchangeable.