A Framework for Integrating Remote Sensing, Soil Sampling, and Models for Monitoring Soil Carbon Sequestration J. W. Jones, S. Traore, J. Koo, M. Bostick, M. Doumbia, and J. Naab SANREM CRSP

Develop methods that can assist in operationalizing a carbon trading system in West Africathrough Land management systems that increase soil C and meet farmer needs System for monitoring soil C changes over time and space Carbon from Communities: A Satellite View Goal

Oumarbougou Quickbird image (2003) Challenges Complex landscapes Interactions among soil, climate, mgt Spatial, temporal variability Magnitude of soil C needed for trading vs. land area managed by individual landowners

Monitoring Soil C: In-Situ Measurements Spatial variations in soil C showing measurement points and kriged estimates (Omarbougou, Mali) Carbon from Communities: A Satellite View Collect soil samples from fields –Spatial and temporal resolution –Errors (sampling, measuring) –Costs Spatial Aggregation Using Geostatistics From R. Yost and M. Doumbia

Monitoring Soil C: Remote Sensing Resolution Multispectral m Panchromatic – 0.6 Quickbird Image, Omarabougou, Mali (Oct, 2002) Land management identification Crop identification Measure field areas Crop growth, biomass, residue estimation Sensing landscape changes Monitor compliance (mgt.) Errors in each process Spatial aggregation Can not measure soil carbon Framework for Monitoring Soil Carbon Sequestration

Monitoring Soil C: Modeling Predict soil C and crop productivity over time and space Soil Weather Management Field scale Link with GIS to scale up from field to community scale Errors in predictions, imperfect models, uncertainty in inputs Carbon from Communities: A Satellite View

D ATA Biomass Soil C Weather Management Soil Properties Parameters Biomass Measured Soil C Measured Soil C Simulated Optimized Biomass Estimation Optimized Parameter Estimation M ODEL D ATA A SSIMILATION ENSEMBLE KALMAN FILTER Biomass Simulated Crop/Soil Model Optimized Soil Carbon Estimation Soil Sampling R/S or Measurements Integrating Remote Sensing and in-situ Soil C Data with Crop Model using an Ensemble Kalman Filter Schematic of data assimilation process for estimation of soil C sequestration using remote sensing and ground observation and a biophysical model (eg. DSSAT-CENTURY).

Measuring Soil C Mass – Field Scale Framework for Monitoring Soil Carbon Sequestration

Variance of Soil C Mass, Field Scale Framework for Monitoring Soil Carbon Sequestration

Simple Soil Carbon Model X t = Vector of soil C, field elements, kg[C] R t = Vector of decomposition rate parameters, yr -1 U t = Vector of C in crop biomass in field, kg[C] b = Vector of fractions of biomass not removed t = time, yr Framework for Monitoring Soil Carbon Sequestration

Measurements, Measurement Errors Where m i,t = measurement in field i, year t (kg[C]) X i,t = Actual soil C in field i, year t (kg[C]) = vector of measurement errors, year t = variance of measurement error Framework for Monitoring Soil Carbon Sequestration

Combining measurements and model predictions using EnKF = Vector of estimates of state variables and/or parameters = measurement vector (soil C & remote sensing biomass) = Kalman gain matrix at time t The (-) and (+) indicate estimates before and after the Kalman update step, respectively

Implementation of EnKF Generate ensemble of random samples of soil C, parameters for each field i “Measure” biomass, all i fields, year t Use model to predict soil C at year t+1, each field Assimilate measurements (soil C, biomass) if available to update estimates of soil C and its variance/covariance matrix Compute aggregate soil C, its variance using the ensemble members Framework for Monitoring Soil Carbon Sequestration

Ensemble Kalman Filter Estimates of Soil C, Single Field (Jones et al., 2004) Estimates of Annual Changes in Soil C, EnKF, Measurements, and True Values Increases in Soil C vs. Years. Comparing EnKF Estimates with Measurements Carbon from Communities: A Satellite View

Spatial Example, Wa, Ghana Figure 4. Twelve fields in Wa, Ghana in which each field was sampled intensely for accurate estimation of soil C via kriging. The fields boundaries are superimposed on a Landsat 7 ETM+ image taken Sept. 9, 2002 (source: J. Naab, SARI, Wa Ghana). Framework for Monitoring Soil Carbon Sequestration DSSAT-Century Crop- Soil Model 12 fields, 0.54 ha 20 samples per field Krig to estimate initial soil C, each field Maize-peanut rotation Measurements varied (soil C and biomass) Estimate aggregate soil C, its variance

Aggregate Soil C Change per Year Framework for Monitoring Soil Carbon Sequestration Comparing estimates based on measurements alone (x) vs. those based on the EnKF (red line)

Tradeoff between accuracy and costs Framework for Monitoring Soil Carbon Sequestration

150 ha, Near Madiama, Mali Framework for Monitoring Soil Carbon Sequestration Example of Contiguous Grazing Area (150 ha)

Uncertainty in Aggregate Soil C Estimate Decreases with Time Framework for Monitoring Soil Carbon Sequestration Results from 150 ha grazing land near Madiama, Mali M. Bostick et al.

Requirements for Ensemble Kalman filter Data Assimilation Stochastic model of soil C changes vs. time, space, mgt Parameter estimates for region, knowledge of model errors Fields in program, mapped, areas determined Initial soil C & spatial correlation, all fields (sampling, geostatistics) Knowledge of measurement errors Sampling, soil – carbon Remote sensing measure of crop biomass added each year EnKF implemented to scale up measurements over space, time Framework for Monitoring Soil Carbon Sequestration

Integrating Remote Sensing, Models and in- situ Measurements for Soil Carbon Monitoring

Agricultural management practices can be implemented that –Remove CO 2 from the atmosphere and store it in soil in quantities that would allow land managers to participate in carbon emissions trading –Reduce land degradation and increase productivity Hypotheses Framework for Monitoring Soil Carbon Sequestration

How Does the Ensemble Kalman Filter Work?

Scaled Up Estimates of Carbon Sequestration Modeling Predict Carbon Sequestration and Agricultural Productivity Resulting from Improved Land Use Practices Improved Land Use Practices Remote Sensing In Situ Measurements Remote Sensing Scale of End User Individual/Local: Farmers/Herders Community: Community-Level Natural Resource Decision-Makers Sub-National: Researchers, Extensionists, Commodity Cooperatives National/Supra-National: National Ministry of Environment, West African Supra-National Organizations (CILSS/INSAH) Integrated Framework Carbon from Communities: A Satellite View