Integrating stochastic models and in situ sampling for monitoring soil carbon sequestration

Participation in carbon (C) markets could provide farmers in developing countries incentives for improving soil fertility. However carbon traders need assurances that contract levels of C are being achieved. Thus, methods are needed to monitor and verify soil C changes over time and space to determine whether target levels of C storage are being met. Because direct measurement over the large areas needed to sequester contract amounts of C in soil is not practical, other approaches are necessary. An integrated approach is described in which an Ensemble Kalman Filter (EnKF) is used to assimilate in situ soil carbon measurements into a stochastic soil C model to estimate soil C changes over time and space. The approach takes into account errors in in situ measurements and uncertainties in the model to estimate mean and variance of soil C for each land unit within a larger land area. The approach requires initial estimates of soil C over space along with uncertainties in these estimates. Model predictions are made to estimate soil C for the next year, in situ soil C measurements update these predictions using maximum likelihood methods, and the spatial pattern of soil C mean, variance, and covariance thus evolve over time.