Monitoring Soil Moisture To Support Risk Reduction For The Agriculture Sector Using

RADARSAT-2

Heather McNairn, Amine Merzouki and Anna Pacheco

IGARSS 2011, Vancouver (Canada)July 28, 2011

Soil Moisture Requirements for Agriculture

• the agriculture community continually contends with risk, often related to the availability of appropriate levels of soil moisture

• too much or too little available soil moisture contributes to a range of risks

– Runoff and erosion– Flooding– Ability to seed– Pest infestations– Crop productivity

• Agriculture and Agri-Food Canada (AAFC) is the Canadian federal agriculture ministry which among other tasks, delivers programs and develops policies to support the Canadian agriculture community

• early assessment of emerging risk, and identifying the extent and severity of extreme events, will assist in the effective and efficient delivery of programs to those most affected

Moisture Issues for Canadian Prairies (2010)

• according to the 2008-2009 Annual Report from the Manitoba Agricultural Services Corporation drought and excessive heat have historically (1960-2007) accounted for 37% of reported crop losses, while excessive moisture was responsible for 36% of losses.

Prairies in 2010• cropland moisture was rated as

surplus on ~50 per cent of the fields• only ~75% of cropland was seeded in

Saskatchewan• the federal and provincial

governments are providing $450 million in aid to farmers under the AgriRecovery Program with Saskatchewan will get most of the funds (~ $360 million)

Moisture Issues for Canadian Prairies (2011)

Canadian Wheat Board: • saturated soil moisture conditions and

above-normal snow pack for 2011

• six million acres unseeded in 2011 • second-largest loss of cropland due to

excessive soil moisture in the past 50 years. The largest loss was in 2010 when large areas of northern and central Saskatchewan were left unplanted

• wheat acres will be the second-smallest since 1971 at 20.3 million acres

• seeding was much later than normal, raising serious concerns about the potential for frost damage this fall from a late harvest

worst hit areas - southwestern Manitoba and southeastern Saskatchewan, where large areas of farmland have been abandoned

Monitoring Soil Moisture

• early assessment of soil moisture reserves, and monitoring of changes in available soil moisture, could assist in risk reduction strategies for the agriculture sector and effective delivery of government programs including those delivered by AAFC

• soil moisture is inherently highly variable in space and time

• In situ soil moisture networks contribute valuable information on temporal changes in soil moisture, but their sparse spatial coverage cannot provide the level of information required at local and field scales.

• due to the sensitivity of microwave scattering to the dielectric properties of targets, SAR sensors represent a valuable data source for estimating soil moisture with improved spatial detail.

• however, conflicting and inconsistent results in the accuracies of soil moisture estimates have impeded operational implementation, where reliability of data products is paramount

• Methods must work under “real world” conditions if they are to be widely adopted by the agriculture end users

Research Objectives

!

!

!

!

!

!Portage la Prairie

Brandon

Selkirk

Winnipeg

SteinbachRed River Basin

M A N I T O B A

U. S. A.

0Km

Legend

Brunkild Site

PFRA_Edited watersheds

Water Survey of Canada Sub-sub Watersheds

Water Survey of Canada Sub Watersheds

Brunkild – implementation site - Casselman

– development site -

• develop, test and evaluate methods to quantify surface soil moisture using SAR satellite sensors

• assist with transitioning methods to implementation within AAFC

• have been acquiring data since 2008 • 9 RADARSAT-2 quad-pol (FQ5, FQ11, FQ16, FQ19) images over Casselman • 7 quad-pol (FQ2, FQ11, FQ15, FQ16) images over Brunkild• spring (April-May) of 2008 and 2010 as well as during the fall (September-November) of 2009.

• soil moisture measurements acquired at +40 sites • sites were 120 x 120 m or 150 x 150 m • site uniformity: roughness, residue cover, tillage implementation, soil type, slope• 16 soil moisture sub-sites with 3-4 replicates (total of 48-64 readings per site)• > 2000 soil moisture measurements per acquisition • roughness measured using 1-metre pin board at 5 locations at each site

Soil Moisture - Data Collection

Needle Profiler

Theta Probes

Semi-empirical Models

• Oh and Dubois – semi-empirical models, developed with inputs of empirical data

– limited ranges of validity - an issue in terms of moisture and roughness

– soil moisture was estimated by explicitly solving the two backscatter equations of the Dubois model, and using a Look-Up Table (LUT) approach applied to the Oh model

– validation using 2008 Eastern Ontario data

– the Oh model in a cross-polarization (HH-HV) and Dubois in a co-polarization (HH-VV) inversion scheme provided the best estimates. Soil moisture root mean square errors were found to be 6.21% for the Dubois model and 7.56% for the HH-HV version of the Oh model.

Integral Equation Model (IEM)

• a physically based model applicable to a wide range of conditions present on agricultural fields (from smooth to rough surfaces; from very dry to very wet)

• three parameters describe roughness in IEM: the correlation function, correlation length and root mean square (rms) height

• calibrated IEM (Baghdadi et al., 2006) uses an optimum roughness correlation length ℓopt2 obtained by forcing the IEM until a good agreement is reached between simulations and SAR image data

• Baghdadi, N., Holah, N. and Zribi, M. (2006) “Calibration of the Integral Equation Model for SAR data in C-band and HH and VV polarizations”, International Journal of Remote Sensing, 27:4, 805-816)

• calibrated IEM inversion was implemented using a LUT approach where LUTs were generated by simulating HH and VV backscatter coefficients using ℓopt2 formulation.

• a direct search algorithm minimizes a scalar value representing the difference between measured and simulated backscatter coefficients.

Soil Moisture Error Assessment

• The errors in soil moisture estimation using the calibrated IEM were determined for the entire AAFC RADARSAT-2 data set. The performance of the IEM was evaluated several ways:

1. The difference between measured and estimated soil moisture on a site-by-site (field) basis;

2. The difference between regional averages of measured and estimated soil moisture; and

3. The ability of the model to estimate relative changes in soil moisture.

0

20

40

60

80

100

0 20 40 60 80 100

Measured volumetric soil moisture (%)

Re

trie

ved

vo

lum

etr

ic s

oil

mo

istu

re (

%)

1. Site Specific Error Assessment

• when all data are included, mean average error in soil moisture estimation was 7.71%

• correlation is weak (coefficient of 0.54) due to significant scatter in results

• need to understand sources of error

• contribution to errors from ground validation• instrument precision ~1% error• dielectric to volumetric conversion ~4%• optimum correlation length not adjusted to these sites

• hypothesis that largest sources of error include• inability to adequately capture spatial variance in soil moisture (even with 48+ measurements over small site)• site specific variance of at least 6%• confounding yet “real world” factors including variance introduced by tillage, post harvest residue etc.

• yet these are the realities of implementation and adoption of these methods by user communities

1. Spatial Variance in Soil Moisture

Date (2007) Field - Site Moisture Under Corn Residue (%)

Moisture of Bare Soil (%)

Moisture Difference (Residue-No Residue)

Oct31 F1-S1 29.2 19.8 9.4

Oct31 F2-S1 20.7 17.8 2.9

Nov16 F1-S1 30.7 25.5 5.2

Nov16 F2-S1 27.6 23.2 4.4

0

10

20

30

40

50

0 132

Acquisition period

Soil

moi

stur

e co

nten

t (%

)

5 May 2008 16 May 2008 23 May 2008

Casselman site

Variance in soil moisture measured at each site (2008)(N=48 for each site)

Effect of tillage structure on soil moisture

Effect of residue on soil moisture

2. Regional Error Assessment

• mean measured soil moisture for all sites and for any given date was compared to the mean estimated moisture for all sites.

• results for Casselman and Brunkild were pooled providing 16 points for statistical analysis

• mean average error for these 16 points was 3.23%

• correlation coefficient (R) between estimated and measured values was 0.92

0

20

40

60

80

100

0 20 40 60 80 100

Measured volumetric soil moisture (%)

Re

trie

ved

vo

lum

etr

ic s

oil

mo

istu

re (

%) Mean_Rsat-2

Mean_Rsat-2

Shallow angles

Steep angles

3. Monitoring Soil Moisture Change - Brunkild

26 April 2010 20 May 2010

3. Monitoring Soil Moisture Change - Casselman

15 September (FQ16) 18 September (FQ5) 9 October (FQ16)

12 October (FQ5) 2 November (FQ16) 5 November (FQ5)

Volumetric Soil Moisture

0

55%

3. Relative Error AssessmentChanges in Soil Moisture for Eastern Ontario

0

5

10

15

20

25

30

35

40

2008May 5

2008May 16

2008May 23

2009Sep. 18

2009Oct. 12

2009Oct. 15

2009Nov. 08

2010Apr. 19

2010May 13

Dates

Vo

lum

etri

c S

oil

Mo

istu

re (

%)

Average SM (%) (Ground Truth)

Average SM (%) (RADARSAT-2)

• assessment of relative change is preliminary• the calibrated IEM model is able to detect wetting and drying of the soil

• differences in the magnitude of changes partially attributable to discrepancy between the time of field measurements and SAR overpasses. The IEM overestimated soil moisture (and underestimate change in soil moisture) for AM satellite orbit (overpass occurs from 6:30 – 7:00)

Conclusions and Next steps

• Calibrated IEM does well in estimating soil moisture on a regional basis and in identifying change in soil moisture over time

• Site specific errors require further investigation

• Focus on assessing where errors are coming from and try to reduce these errors

• More closely examine ability to determine relative change– Use in situ data to assist with assessing “true” ability to quantify change and to

benchmark the absolute soil moisture

• Also investigating use of multi-angle RADARSAT-2 data

• Plan is to pilot at least regional moisture estimates by end of current project (March 2013)

Acknowledgements

• Canadian Space Agency (Government Related Initiatives Program)

• Agri-Environmental Services Branch of AAFC

Additional Slides

1.2 (r) ^0.5

1.2 (r) ^0.5

k2sl

C-Band< 30°or

> 40°Sahebi et al., 2002NBRI

< 6.0> 8.0

21,24-26, 35,39,40,45,47°

(HH)23-24° (VV)

> 5.0< 3.0Baghdadi et

al., 2004Calibrated

IEM

< 6.0> 8.0

> 5.0< 3.0Fung et al.,1992/94

IEM

Gaussian

1.5 to 9.5> 9%

< 31%> 20°

> 2.6 < 19.7

> 0.1< 6.0

Oh et al.,1992Oh

C-Band> 14< 32

≥30°NAallAngles et al., 2001

Modified Dubois

1.5 to 11.0≤35%≥30°NA≤2.5Dubois et al., 1995

Standard Dubois

f (GHz)Mv (%)°klksAuthorsRadar

Models

Validity Range

k = 2/

1.2 (r) ^0.5

1.2 (r) ^0.5

k2sl

C-Band< 30°or

> 40°Sahebi et al., 2002NBRI

< 6.0> 8.0

21,24-26, 35,39,40,45,47°

(HH)23-24° (VV)

> 5.0< 3.0Baghdadi et

al., 2004Calibrated

IEM

< 6.0> 8.0

> 5.0< 3.0Fung et al.,1992/94

IEM

Gaussian

1.5 to 9.5> 9%

< 31%> 20°

> 2.6 < 19.7

> 0.1< 6.0

Oh et al.,1992Oh

C-Band> 14< 32

≥30°NAallAngles et al., 2001

Modified Dubois

1.5 to 11.0≤35%≥30°NA≤2.5Dubois et al., 1995

Standard Dubois

f (GHz)Mv (%)°klksAuthorsRadar

Models

Validity Range

k = 2/

Calibrated IEM

• Baghdadi et al. have proposed a calibration of the IEM (Baghdadi, N., Holah, N. and Zribi, M. (2006) “Calibration of the Integral Equation Model for SAR data in C-band and HH and VV polarizations”, International Journal of Remote Sensing, 27:4, 805-816)

• calibrated IEM uses an optimum roughness correlation length ℓopt2 obtained by forcing the IEM until a good agreement is reached between simulations and SAR image data. This optimum correlation length is expressed as:

• ℓopt2 depends on roughness, incidence angle, polarization and frequency• validation was undertaken by comparing measured and simulated backscatter coefficients for the calibrated IEM, where

ℓ values were replaced by the optimum values ℓopt2, using Baghdadi’s equation (i.e. no site specific calibration)• results were assessed on site-by-site basis, and averaged according to broad soil texture classes (heavy clay, sandy

soils and silty and clayey loams)

rmsθδ sinopt2026.4HH δ

289.3VV δ

744.1VVHH μμ

0025.0VVHH ηη

551.1HH ξ

222.1VV ξ

026.4HH δ

289.3VV δ

744.1VVHH μμ

0025.0VVHH ηη

551.1HH ξ

222.1VV ξ