Abstract

Recent advancements in low-cost remote sensing equipment and techniques have proven that crop parameterclassification can be practical in agricultural situations. One problem still remaining is how to account for in-field variabilityof mixed ground-cover areas. This study focused on finding a non-invasive method of screening low ground-cover areasbefore assessing crop nitrogen (N) status. Four years of plot data were used where intense ground sampling was coupled withnadir-viewing remotely sensed data from a ground-based remote sensing system. Data were collected over irrigated corn(Zea mays L.) with both light and dark soil backgrounds to develop a relationship between leaf area index (LAI) and remotelysensed data. An index based on subtracting the red from the green reflectance (green - red) was shown to be strongly relatedto LAI. A concurrent assessment of the N reflectance index (NRI) over the four-year study indicated that the corn N statusclassification improved with increasing LAI. Using the (green - red) index and a screening value corresponding to an LAIof 2.5, the data from the four-year study were screened and the NRI was reassessed. The initial screening removed all bare-soiland many of the measurements collected before the V12 growth stage. In this example, 94.0% of the data associated with LAIbelow 2.5 was correctly filtered out, and 95.4% of the data collected over areas with LAI at or above 2.5 correctly passedthe filter. This method of filtering was shown to be highly effective in screening low ground-cover and bare-soil areas beforeclassification. Future incorporation of this technique into on-the-go filtering algorithms could improve crop parameterclassification from remotely sensed measurements in early-season and mixed vegetative cover situations.

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