Using multispectral imagery and linear spectral unmixing techniques for estimating crop yield variability

Vegetation indices derived from multispectral imagery are commonly used to extract crop growth and yield information. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each image pixel and have the potential for mapping crop yield variability. The objective of this study was to apply linear spectral unmixing techniques to airborne multispectral imagery for estimating grain sorghum yield variability. Five time-sequential airborne multispectral images and yield monitor data collected from a grain sorghum field were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the images to generate crop plant and soil abundances for each image and for all 26 multi-image combinations of the five images. Yield was related to unconstrained and constrained plant and soil abundances as well as to the normalized difference vegetation index (NDVI) and the green NDVI (GNDVI). Results showed that unconstrained plant abundance had better correlations with yield than NDVI for all five images, but GNDVI had better correlations with yield for the first three images. Unconstrained plant abundance derived from the fourth image provided the best overall correlation with yield (r = 0.88). Moreover, multi-image combinations generally improved the correlations with yield over single images, and the best three-image combination resulted in the highest overall correlation (r = 0.90) between yield and unconstrained plant abundance. These results indicate that linear spectral unmixing techniques can be a useful tool for quantifying crop canopy abundance and mapping crop yield.

[1]  A. J. Richardson,et al.  Vegetation indices in crop assessments , 1991 .

[2]  B. R. Roberts,et al.  RELATIONSHIPS BETWEEN REMOTELY SENSED REFLECTANCE DATA AND COTTON GROWTH AND YIELD , 2000 .

[3]  C. Yang,et al.  Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn , 2001 .

[4]  Brian Curtiss,et al.  A method for manual endmember selection and spectral unmixing , 1996 .

[5]  John B. Adams,et al.  Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon , 1995 .

[6]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[7]  Chenghai Yang,et al.  Airborne Videography to Identify Spatial Plant Growth Variability for Grain Sorghum , 2004, Precision Agriculture.

[8]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[9]  Alan R. Gillespie,et al.  Vegetation in deserts. I - A regional measure of abundance from multispectral images. II - Environmental influences on regional abundance , 1990 .

[10]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[11]  James E. McMurtrey,et al.  Relationship of spectral data to grain yield variation , 1980 .

[12]  Chenghai Yang,et al.  Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum , 2002, Precision Agriculture.

[13]  Chenghai Yang,et al.  OPTIMUM TIME LAG DETERMINATION FOR YIELD MONITORING WITH REMOTELY SENSED IMAGERY , 2002 .

[14]  Prasad S. Thenkabail,et al.  Landsat-5 Thematic Mapper models of soybean and corn crop characteristics , 1994 .

[15]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[16]  Chenghai Yang,et al.  Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery. , 2000 .

[17]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .