Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-<i>a</i> Concentrations in Irrigation Pond Water

• Main Authors: Geonwoo Kim, Insuck Baek, Matthew D. Stocker, Jaclyn E. Smith, Andrew L. Van Tassell, Jianwei Qin, Diane E. Chan, Yakov Pachepsky, Moon S. Kim
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Remote Sensing
• Subjects: Irrigation pond water; principal component analysis; chlorophyll-<i>a</i> concentration; hyperspectral images; NIR-red model; remote sensing
• Online Access: https://www.mdpi.com/2072-4292/12/13/2070

This study provides detailed information about the use of a 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-<i>a</i> 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-<i>a</i> data. About 80,000 sample images were acquired by the line-scan method. Image processing 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-<i>a</i> in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (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 heights can provide valuable information about water quality in a fresh water source.