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

A Framework for Integrating A Framework for Integrating Remote Sensing, Soil Remote Sensing, Soil

Sampling, and Models for Sampling, and Models for Monitoring Soil Carbon Monitoring Soil Carbon

SequestrationSequestrationJ. 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 Africa

throughthrough• 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)


• 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

ResolutionMultispectral - 2.4 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


DDATAATA

Biomass

Soil C

WeatherManagement

Soil Properties

Parameters

BiomassMeasured

Soil CMeasured

Soil CSimulated

Optimized

BiomassEstimation

Optimized Parameter Estimation

MMODELODEL DDATA ATA AASSIMILATIONSSIMILATION

ENSEMBLE

KALMAN

FILTERBiomassSimulated

Crop/Soil Model

Optimized

Soil CarbonEstimation

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

zsstt dcZ

z))(,0(~ tz ZVarN

zsstt dcZ [ 1 ] w h e r e Z t = M a s s o f s o i l C m e a s u r e d o n d a y t i n a

s p e c i f i c f i e l d , k g [ C ] c t = c o n c e n t r a t i o n o f c a r b o n i n s o i l m e a s u r e d o n d a y t , k g [ C ] k g [ s o i l ] - 1

s = b u l k d e n s i t y o f s o i l , k g [ s o i l ] m - 3

d s = s a m p l i n g d e p t h , m = a r e a o f f i e l d , m 2

z = e r r o r , f i e l d s c a l e s o i l C m e a s u r e , k g [ C ] a n d ))(,0(~ tz ZVarN


Variance of Soil C Mass, Field Scale

Total Field Soil C Standard Error

0.0

500.01000.0

1500.02000.0

2500.0

3000.03500.0

4000.0

0 0.02 0.04 0.06 0.08 0.1

Soil C Measurement Error, %C

Fie

ld C

Err

or,

kg

Error in soil C measurement

Errors in C measurement, field size, bulk density

)()()()()()()( 222 VardcVardccVardZVar sstssttsst


Simple Soil Carbon Model

zsstt dcZ

z))(,0(~ tz ZVarN

zsstt dcZ ),,(0

),,(

2

1

tRXfdt

Rd

tRXfUbXRdt

Xd

t

ttttT

tTt

Xt = Vector of soil C, field elements, kg[C]

Rt = Vector of decomposition rate parameters, yr-1

Ut = Vector of C in crop biomass in field, kg[C]

b = Vector of fractions of biomass not removed

t = time, yrFramework for Monitoring Soil Carbon Sequestration

Measurements, Measurement Errors

tititi Xm ,,,

),0(~ 2mt N

Wheremi,t = measurement in field i, year t (kg[C])Xi,t = Actual soil C in field i, year t (kg[C]) = vector of measurement errors, year t = variance of measurement error

2m

t


Combining measurements and model predictions using EnKF

)](ˆ[)(ˆ)(ˆ ttttt XmKXX

tX̂ = Vector of estimates of state variables and/or parameters

tm = measurement vector (soil C & remote sensing biomass)

tK = 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


Ensemble Kalman FilterEstimates of Soil C, Single

Field (Jones et al., 2004)

-1500

-1000

-500

0

500

1000

1500

2000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Year

Annu

al S

oil C

Cha

nge,

kg/

haMeasured EnKF Estimates True ValuesEstimates of Annual Changes in Soil

C,EnKF, Measurements, and True

Values

Increases in Soil C vs. Years.

Comparing EnKF Estimates with Measurements


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).


• 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

Yearly Carbon Sequestration12 plots (1800 m2) in Nakor, Wa, Ghana

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

2003 2013 2023 2033

(ton

[C]/

regi

on)/

yr

TrueObservedFiltered


Comparing estimates based on measurements alone (x) vs. those based on the EnKF (red line)

Tradeoff between accuracy and costs

Year 20121: Carbon mesured every year

1/3: Carbon measured every third year

21.5

22.0

22.5

23.0

23.5

2 3 4 5 6 7 8 9 10 11 12 13

No. measured plots / 12 plots

A.

Soi

l C S

td.

Err

or,

(kg)

0

20

40

60

80

100

120

140

B. N

o. soil samples

A. 1A. 1/3B. 1B. 1/3


150 ha, Near Madiama, Mali


Example of Contiguous Grazing Area (150 ha)

Uncertainty in Aggregate Soil C Estimate Decreases with Time

2

2.5

3

3.5

4

4.5

5

5.5

6

0 5 10 15 20

year

C e

stim

atio

n er

ror

(%)

# = 25

# = 55

# = 85


Results from 150 ha grazing land near Madiama, MaliM. 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


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

Agricultural management practices can be implemented that– Remove CO2 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



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


Documents

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