Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #375309

Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

Title: Hyperspectral imaging from a multipurpose floating platform to estimate chlorophyll-a concentrations in irrigation pond water

item KIM, GEONWOO - Orise Fellow
item BAEK, INSUCK - Orise Fellow
item STOCKER, MATTHEW - Orise Fellow
item SMITH, JACLYN - Orise Fellow
item VAN TESSEL, ANDREW - Iowa State University
item Qin, Jianwei - Tony Qin
item Chan, Diane
item Pachepsky, Yakov
item Kim, Moon

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/25/2020
Publication Date: 6/27/2020
Citation: Kim, G., Baek, I., Stocker, M., Smith, J., Van Tessel, A., Qin, J., Chan, D.E., Pachepsky, Y.A., Kim, M.S. 2020. Hyperspectral imaging from a multipurpose floating platform to estimate chlorophyll-a concentrations in irrigation pond water. Remote Sensing. 13(12):2070.

Interpretive Summary: Foodborne illnesses spread by the consumption of fresh produce can be caused by the use of contaminated agricultural irrigation water. Monitoring and assessing irrigation water quality is important for preventing contamination in field production, but conventional methods to do so can be time-consuming and laborious. In this pilot study, a line-scan hyperspectral imaging camera system mounted on the bow of a GPS-equipped multipurpose floating platform (MFP) was tested on an irrigation pond, acquiring about 80,000 near-infrared/red spectral line-scan images of the water for correlation to measurements of chlorophyll-a content, a water quality indicator, for water samples collected during imaging. From this data, models were developed to predicted chlorophyll-a concentrations and the results were highly correlated to measured concentrations. This work shows that low-altitude hyperspectral imaging via MFP can provide valuable information about water quality through spatial mapping for data visualization. The hyperspectral imaging method for water quality can be further improved by additional research addressing variation arising from floating debris, aquatic organisms, and changes in sunlight intensity and cloud movement, as well as by considering other indicator measurements such as suspended solids, colored dissolved organic matter, bacteria, nutrient concentrations, turbidity, and so on. This research will help researchers develop and optimize models and methods that can be used in field production by the fresh produce industry to help meet federal mandates for irrigation water quality.

Technical Abstract: This study provides detailed information about the use of hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by line-scan method. Image preprocessing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two NIR-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low altitude can provide valuable information about water quality in a fresh water source.