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Potential use of AVHRR NDVI in estimating crop aboveground biomass and carbon inputs from crop residues

Erandi Lokupitiya1,2, Michael Lefsky3, and Keith Paustian1,2

1 Natural Resource Ecology Laboratory, 2 Department of Soil and Crop Sciences, 3 Department of Forest, Rangeland and Watershed Stewardship,

Colorado State University, Fort Collins, CO

• The current study was carried out as part of a project that assesses potential carbon sequestration in agricultural soils in the conterminous U.S.

• Crop yields can be used to estimate crop aboveground biomass and carbon inputs from crop residues; one limitation of using the available national crop yield statistics for biomass (and carbon inputs) estimation is missing data in certain counties and certain years.

• This study was designed to evaluate the potential use of remote sensing in estimating crop aboveground biomass when no yield information is available; model relationships between biweekly NDVI and biomass of the main crops (estimated from the crop yields) in Iowa were developed using the available data from 1992 and 1997.

Introduction

A BF

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Data analysis and imputation procedure

Methods

• Biweekly NDVI images of Iowa in 1992 and 1997 were concatenated separately, and non-crop areas were excluded using the recoded NLCD coverage for Iowa; average biweekly NDVI pixel values for each county were calculated.

• Crop area within each county, and the crop area occupied by each major crop (i.e. corn, soybean and oats), and crop aboveground biomass in the two years were estimated using the Crop yields and acreages reported by NASS; county level yield data reported by NASS were converted to aboveground biomass using equations derived from past studies (Paustian and Williams, submitted).

Analyses to find NDVI- biomass relationships

Best subset multiple regression analyses

Independent variables- avg. pixel values from biweekly NDVI images during the crop growth period in Iowa (altogether 15 in 1992, and 16 those in 1997)

Dependent variables : a. Above ground biomass kg ha-1 for each crop b.      Area fraction (area of the particular crop as a fraction of the total crop area) for

each crop• Area weighted biomass kg ha-1; the sum of the aboveground biomass of each crop

weighted by their area fraction)

• Each dependent variable was regressed against the best subset of the biweekly NDVI pixel bands

Canonical correlation analyses

• Since the simple best subsets multiple regression analyses yielded some un-interpretable negative coefficients (as shown in the Results section) in the models, which can be attributed to autocorrelation among biweekly NDVI values, canonical correlation analyses (CANCORR, SAS 9.0) were performed using the above independent and dependent variables for the data in 1992 and 1997.

• Canonical correlation creates new canonical variables from the original independent and dependent variables, and maximizes the correlation between each set of canonical variables.(Highly correlated variables in the original data are combined together, thus reducing the number of variables, but still retaining the information from the original data.)

Iowa Composite NDVI image

- 1992

NDVI image for crops- after being masked using the NLCD image

- 1992

Some results from the simple best subsets multiple regression analyses- models between biweekly NDVI and corn aboveground biomass

____________________________________________________ Year regression model R-sq ___________________________________________________________________________________________

1992 Corn AGBM kg/ha = - 15426 + 316 (Apr03-16) - 166 (May01-14) 78%

- 59.6 (May29-Jun11)+ 71.6 (Jun12-25) + 241 (Aug21-Sep03) - 163 (Sep18-Oct01)

1997 Corn AGBM kg/ha = 15755 - 89.5 (Mar28-Apr10) + 85.2 (Jun06-19) 74%

- 76.1 (Jul04-17)+ 117 (Jul18-31) + 44.1 (Sep12-25) - 91.4 (Sep26-Oct09) - 32.7 (Oct10-23)

_____________________________________________________________ Simple best subsets multiple regression analyses yielded certain

un-interpretable negative coefficients in the models

Results

100110120130140150160170180

94-107

108-121

122-135

136-149

150-163

164-177

178-191

192-205

206-219

220-233

234-247

248-261

262-275

276-289

290-303

time (day of the year)

NDVI

pixe

l valu

es

92

97

Change of NDVI over time- Iowa

Best subset multiple regression models between

canonical variables (cv) from NDVI and corn aboveground biomass-

________________________________________________ Year Model with NDVI canonical variables R-sq

____________________________________________________________________________________

1992 =16653 + 1105 cv1 - 266 cv2 - 283 cv3 - 281 cv5 + 192 cv6 78% 1997 =15723 + 873 cv1 + 234 cv2 + 311 cv4 82%

__________________________________________________________________

Correlations between aboveground biomass and NDVI pixel values in 1992

-1

-0.5

0

0.5

1

Apr03-16

May15-28

Jun26-Jul09

Aug07-20

Sep18-Oct01

Biweekly NDVI pixel bands

Cor

r. C

oeffi

cien

t w

ith b

iom

ass

soycornoats

Loadings on canonical variables from original NDVI- 1992

-1.2

-0.8

-0.4

0

0.4

0.8

1.2

Apr03-16

May01-14

May29-Jun11

Jun26-Jul09

Jul24-Aug06

Aug21-Sep03

Sep18-Oct01

Oct16-29

Biweekly NDVI band

Can

onic

al load

ing

cv1

cv2

scatter plot predicted vs observed corn biomass - 1992

13000

15000

17000

19000

13000 15000 17000 19000

CAGBM_obs

CAG

BM

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d 92 Model97 Model

Image and data processing

Simple best subsets multiple regression analyses

Canonical correlation analyses (CCA)

Corn aboveground biomass observed (CAGBM_obs) in 1992 vs predicted (CAGBM_pred) using the above models for 1992 and 1997.

• Our results confirmed earlier studies that NDVI can be used to predict crop aboveground biomass, making its use feasible in estimating crop residue carbon inputs. • Both simple best subset multiple regression analyses and CCA yielded good model relationships between NDVI and biomass. But CCA improved the interpretability of the results, and gave better predictability in the models, as they better dealt with the autocorrelation associated with biweekly NDVI. CCA found that NDVI and crop biomass are well correlated during the middle of the crop growth from mid June to end August, and using the canonical variables from original biweekly NDVI pixel values in subsequent best subset multiple regression analyses was important in determining model relationships with biomass of individual crops.

Discussion and conclusion Acknowledgements

Sunil Kumar and Kay Dudek from Colorado State University. This work was financially supported by the Consortium for Agricultural Soil Mitigation of Greenhouse Gases.

ReferencesDoraiswamy, P.C., Hartfield, J.L., Jackson, T.J., Akhmedov, B., Prueger, J., & Stern, A. 2004. Crop condition and yield simulations using Landsat and MODIS. Remote sensing of environment 92: 548-559Doraiswamy, P. C., T. R. Sinclair, S. Hollinger, B. Akhmedov, A. Stem, and J. Prueger. 2005. Application of MODIS derived parameters for regional crop yield assessment. Remote Sensing of Environment 92:192-202.Timm, N.H. 2002. Applied multivariate Analysis. Springer texts in statistics. Springer. New York.

• CCA produced 8 canonical variables from various combinations of raw independent variables (biweekly NDVI), with most highly correlated variables forming the first canonical variable (cv1). Similarly, canonical variables were formed from the dependent variables (biomass & area fraction), too. • Correlations between the raw dependent and independent variables, each canonical variable and the raw (both independent and dependent) variables were produced.

CCA contd.