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

Abstract

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-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 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-a 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.

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

“…By employing suitable techniques, hyperspectral data can capture the discriminative optical features of PC pigments in detail and be used for quantitative mapping of cyanobacteria during bloom conditions [30]. Hyperspectral data, with the detailed spatial and spectral resolutions, and frequent temporal coverage, can complement conventional remote sensing observations [40]. Extending the use of hyperspectral datasets in monitoring different variables requires employing techniques that are able to address their collinearity and data redundancy [41].…”

Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South

“…As in Gholizadeh, Melesse and Reddi [7], the results of this study illustrated that most of the studies that utilised earth observation data sought to characterise water quality more than water quantity (Figure 3). The widely researched water quality parameters included conductivity [24,25], pH [25,26], Cl − [24], dissolved oxygen [27], total suspended solids (TSS) [28,29], chlorophyll [30][31][32][33], turbidity [34][35][36], K + , ammonium nitrogen (NH 4 -N), sodium (Na + ), BOD, magnesium (Mg), total phosphorous, orthophosphate (PO 4 -P), temperature and total nitrogen, iron (Fe), COD, zinc (Zn), calcium (Ca), manganese (Mn), salinity, copper (Cu), bicarbonate HCO 3− , sodium-absorbed ratio (SAR), coliform, cadmium (Cd), chromium (Cr), Ca 2+ , HCO 3− , and total hardness in order of frequency, as illustrated in Figure 4b. These parameters were mostly characterised using satellite remotely sensed data.…”

Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

“…The dark reference image was acquired by deactivating the illumination and was obtained to enable correction for noise from the EMCCD camera. The spectral calibration reflectance values were acquired according to the following equation [14,30]:…”

“…Hyperspectral imaging (HIS) can simultaneously acquire spatial and spectral data from a target by combining image processing and spectroscopy techniques, and is a powerful tool for agricultural analyses [13][14][15]. Rapid improvements in HSI technology have facilitated nondestructive evaluation of soil nutrition, fertilizer quality, plant productivity, and so on.…”