It has been found that a close relationship exists between the spectral reflectance and the health as well as the productivity of soybean canopies. Basing on this property, a remote sensing method was used to assess the plant productivity and health, which was able to cover a large area simultaneously. In this project, aerial images and reflectance measurements were obtained from two soybean fields located in Story Country, Iowa during summer 2000 and 2001. Aerial image were taken on both infrared band and color band. An eight-band radiometer was used to obtain the reflectance reading at ground level, which ranged from 460-nm to 810-nm. A study on aerial-radiometer data relation for two continuous years was carried out. The reflectance measurement from radiometer served as ground truth. Also, the calibration procedure for aerial image was applied. Because the atmospheric distortion became more pronounced when the aerial image was taken at higher altitude. Scattering and absorption will affect the image intensity which indirectly affect our analysis. The red, green and blue ground cloths were put beside the fields when the aerial image was taken. They are used as references to evaluate color distortion in the red, blue and green bands respectively.
This project seeks to assess plant productivity and health in time and space by measuring spectral reflectance from soybean canopies using remote sensing images that do not require ground assessment. Aerial images and reflectance measurements from a multi-spectral radiometer were obtained simultaneously from a soybean field located in Story County, Iowa. The multi-spectral radiometer has eight wavelength bands, ranging from 460-nm to 810-nm and was used as a ground reference for the data analysis. Aerial images were obtained from altitudes ranging from 152 to 427 meters from the ground during summer 2000. Aerial images were analyzed using Matlab, ArcView and Imagine. Difficulties in image analysis and interpretation may occur as the sensing equipment increases in altitude because atmospheric influences become more pronounced. Scattering and absorption of electromagnetic waves in the atmosphere change the spectrum of the reflected wave emitting from the plants as it propagates from the plants to the sensors. Color calibration procedures were used with red, green and blue ground cloths to correct aerial images in the respective red, blue and green bands. Regression analysis was carried out to quantify the relationships between multi-spectral radiometer data and aerial image data.
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