Precision Agriculture, 4, 359±384, 2003
2003 Kluwer Academic Publishers. Manufactured in The Netherlands.
Utility of Remote Sensing in Predicting Crop and
CHRIS T. LEON*
DAVID R. SHAW firstname.lastname@example.org
MICHAEL S. COX
MELINDA J. ABSHIRE
BRIAN WARD AND
MILTON C. WARDLAW III
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University,
Mississippi State, MS 39762
Experimental Statistics, 151 Dorman Hall, Box 9653, Mississippi State University, Mississippi State,
Abstract. Remote sensing during the production season can provide visual indications ofcrop growth along
with the geographic locations ofthose areas. A grid coordinate system was used to sample cotton and soybean
fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield.
During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield
generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green±red ratio and
a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands.
After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI,
and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87.
Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from
0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720 nm). The most
consistent correlation coefficients for soybean yield were obtained 89±93 DAP, corresponding to peak
vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally
ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal
component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters
captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop
growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the
largest amount of variability for cotton growth occurred during the period from first bloom to first open boll,
with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on
the soybean fields, R
values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78,
55±130 DAP. The use ofindividual bands located in the green, red, and NIR, ratios such as RVI and DVI,
indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields
performed well during the cotton and soybean production season, though none of these single bands, ratios, or
indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within
a production season. More research needs to be conducted to determine whether a certain image analysis method
will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for
each imagery date in order to provide reliable estimates ofcrop and soil characteristics.
*Current address: Louisiana State University, Department ofAgronomy and Environmental Management,
104 Sturgis Hall, Baton Rouge, LA 70803.