Estimating Cotton Defoliation with Remote Sensing Glen Ritchie 1 and Craig Bednarz 2 1 UGA Coastal Plain Experiment Station, Tifton, GA 2 Texas Tech, Lubbock, TX 1 UGA Coastal Plain Experiment Station, Tifton, GA 2 Texas Tech, Lubbock, TX Funding provided by

Cotton Defoliants Facilitate machine harvest Reduce weather-induced losses Cost $$$ May be unnecessary in places Facilitate machine harvest Reduce weather-induced losses Cost $$$ May be unnecessary in places

Estimating Defoliation Visual Estimates –Inexpensive –Quick –Accurate??? Remote Sensing –Can cover broad areas –Estimates vegetation well –Relatively untested on defoliation Visual Estimates –Inexpensive –Quick –Accurate??? Remote Sensing –Can cover broad areas –Estimates vegetation well –Relatively untested on defoliation

Vegetation Indices Plant vs. soil reflectance –Ratios –Differences –Derivatives Separate plant and soil Plant vs. soil reflectance –Ratios –Differences –Derivatives Separate plant and soil BlueGreenRed Edge NIR Visible Soil Plant

Chlorophyll Leaf Structure Blue Green Red Edge Red Edge NIR Soil Plant

Normalized Difference Vegetation Index (NDVI) (NIR-R)/(NIR+R) Rouse et al., 1973 ( ) ( ) = 0.85 ( ) ( ) = (0.5 - ) (0.5+ ) = (0.5 - ) (0.5+ ) = ( - ) ( + ) = ( - ) ( + ) = 11 11 22 22 or (  2 -  1 )/(  2 +  1 )

Which wavelengths? –Red and NIR –Green and red edge variants –Higher order models Which wavelengths? –Red and NIR –Green and red edge variants –Higher order models NDVI

Potential Confounding Factors Atmospheric effects Green leaves on ground Desiccated leaves on plant Plant height, orientation Leaf structure Atmospheric effects Green leaves on ground Desiccated leaves on plant Plant height, orientation Leaf structure

Materials and Methods Four locations in Tifton: DP 555 and Stoneville 4892 Reflectance on 0.91 m of row Visual estimates Leaves removed by hand LAI from leaf area meter NDVI regressed against LAI Four locations in Tifton: DP 555 and Stoneville 4892 Reflectance on 0.91 m of row Visual estimates Leaves removed by hand LAI from leaf area meter NDVI regressed against LAI

Materials nm range 1.5 nm resolution nm range 1.5 nm resolution 2 m fiber optic cable Photos courtesy Apogee Instruments, Inc. LI-COR LI-3100 leaf area meter LI-COR LI-3100 leaf area meter Apogee PAR/NIR Spectrometer

Regression Analysis 22 22 820 nm (  2 -  1 )/(  2 +  1 ) If  1 and  2 are arbitrary, 250,000 NDVI wavelength combinations possible If  2 is fixed, 500 NDVI possibilities If  1 and  2 are arbitrary, 250,000 NDVI wavelength combinations possible If  2 is fixed, 500 NDVI possibilities 11 11

Regression Analysis LAI estimates were compared using the coefficient of determination (r 2 ). r 2 = 1 : perfect relationship between x and y r 2 = 0 : no relationship between x and y LAI estimates were compared using the coefficient of determination (r 2 ). r 2 = 1 : perfect relationship between x and y r 2 = 0 : no relationship between x and y r 2 = 1.0 r 2 = 0.0

Results Linear (y =  0 +  1 x) Quadratic (y =  0 +  1 x +  2 x 2 ) All dates, all locations

High quadratic correlation: flattening of NDVI at high LAI levels. Red NDVI did not increase above LAI of 1.2. Red edge NDVI continued to trend upward with LAI. High quadratic correlation: flattening of NDVI at high LAI levels. Red NDVI did not increase above LAI of 1.2. Red edge NDVI continued to trend upward with LAI. Results

Results: Quadratic Model Maximum Estimated LAI Minimum Estimated LAI Crop Reflectance

Results: All Combinations Comparison of all  1 and  2 Highest correlations: Combinations of red edge and near- infrared reflectance bands Comparison of all  1 and  2 Highest correlations: Combinations of red edge and near- infrared reflectance bands

Results: Visual Estimates Reviewer 1 Reviewer 2 Reviewer 3

Results: Visual Estimates Reviewer r 2 (all LAI) r 2 (LAI<0.5) Slope (LAI<0.5) r 2 (LAI>0.5) Slope (LAI>0.5) NDVI 710 nm  NS  : Not significant at 0.05 level

Conclusions Red edge: NIR wavelength combinations most consistently estimate LAI Individual reviewers are generally very good at estimating changes in LAI Estimates vary between reviewers Precision defoliant application… Red edge: NIR wavelength combinations most consistently estimate LAI Individual reviewers are generally very good at estimating changes in LAI Estimates vary between reviewers Precision defoliant application…

Acknowledgments Georgia Cotton Commission Cotton physiology technical staff Steve Brown and Stanley Culpepper Georgia Cotton Commission Cotton physiology technical staff Steve Brown and Stanley Culpepper