Remote Sensing for Organic and Conventional Corn Assessment

The aim of this study is to evaluate the effectiveness of remote sensing in distinguishing organic and conventional corn. The hypothesis of the research is that the difference between organic and conventional corn can be detected based on the dissimilarities in their vigor and maturity, which are commonly altered by different agricultural management and nutrient application to soil. Hyperspectral in situ measurements as well as multispectral reflectance along with narrow and wide band vegetation indices were assessed. Two available cloud-free Landsat 7 and Landsat 8 data sets were used in the analysis, one for the mid- growing season and another for the pre-harvest (maturity) season. Overall, the organic corn demonstrates higher values of chlorophyll- and nitrogen- related narrow band indices at the mid-season and at maturity stage, based on the in situ measurements. The results indicate a significant difference between two types of corn, particularly at 410, 545, 710, and 760 nm. A 750/550 ratio and MSR705 index, calculated from reflectances at 445, 705, and 740 nm, are the most effective indices for the corn separation. The wide band indices, calculated from the satellite data in the blue, red, NIR, and MIR regions, are effective in corn type determination. Landsat 8 color composite images with indices 1/4, 6/2, and 5/7 (Coastal Aerosol/red, SWIR1/blue, NIR/SWIR2) as well as 1/4, 6/2, 5/4 (Coastal Aerosol/red, SWIR1/blue, NIR/red) indices are able to separate the organic and conventional parcels in the mid-season and pre-harvest time in the study. Landsat 8 has more potential than Landsat 7 to discriminate corn type (organic vs. conventional) in both the mid-season and pre-harvest time. More research should be conducted in order to understand the factors that cause the differences between the two types of corn. Chlorophyll measurements, soil chemistry data, soil textural analysis, and soil moisture records were not available for this study.

Authors: Natalia Balashova

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