Relationship between modified normalized difference vegetation index and leaf area index for processing tomatoes
2005
Koller, M. | Upadhyaya, S.K.
Remote sensing has the potential for obtaining large amounts of soil and plant data inexpensively and rapidly. Vegetation growth monitored using normalized difference vegetation index (NDVI) tends to be affected by the background soil reflectance. Modified normalized difference vegetation index, which is related to the soil adjusted vegetation index (SAVI), has the capability to overcome this effect. Modified NDVI is related to the slope of vegetation isolines and is based on the assumption that all vegetation isolines and soil line intersect at a single point. Leaf area index obtained through field and laboratory measurements for a processing tomato crop correlated well with the slope of the vegetation isolines with a R2 value of 0.92. A neural network model was developed and trained to predict daily LAI values based on six sets of Modified NDVI values derived from biweekly aerial images obtained during the tomato growing season. The trained network was able to predict LAI values with R2 values of 0.96 or higher. The cumulative leaf area index (CLAID) predicted by the neural network model correlated well with the measured CLAID values (R2 = 0.83).
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