Spatial patterns of water quality and remote sensing indices from UAV-based multispectral imagery across an irrigation pond

Authors: HONG, SEOKMIN - Oak Ridge Institute For Science And Education (ORISE); Morgan, Billie; Stocker, Matthew; SMITH, JACLYN - Oak Ridge Institute For Science And Education (ORISE); Pachepsky, Yakov

Submitted to: Heliyon
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/10/2025
Publication Date: 2/11/2025
Citation: Hong, S., Morgan, B.J., Stocker, M.D., Smith, J.E., Pachepsky, Y.A. 2025. Estimating E. coli concentrations in an irrigation pond from multispectral imagery and/or water quality parameters. Heliyon. 11(4). Article e42622. https://doi.org/10.1016/j.heliyon.2025.e42622.
DOI: https://doi.org/10.1016/j.heliyon.2025.e42622

Interpretive Summary: Water quality in irrigation water sources often is an essential factor of public health and farm sustainability. Chemical and microbial indicators of water quality in irrigation ponds may substantially vary in space and time. Drone imagery can provide high-resolution data about pond surface reflectance. Finding how to convert patterns in drone-based images to patterns of water quality can save time and resources. Working at a Maryland farm during the summer, we measured water quality in the same locations several times and simultaneously obtained maps of the light reflectance from the pond surface at five wavelengths using the camera mounted on a drone. Then we applied the artificial intelligence techniques to find the conversion between patterns of light reflectance and patterns of water quality. Results of this work can be beneficial for water quality professionals and managers in that they indicate how to use relatively inexpensive drone-based imaging technology to find the best sampling locations to characterize water quality across the pond, and especially the area where the irrigation water is taken.

Technical Abstract: Water quality of irrigation water is an essential factor for public safety and farm sustainability. Imaging surface water sources from unmanned aerial vehicles (UAV) has become an important source of water quality information. WQVs in irrigation ponds have been shown to have persistent spatial patterns. The objective of this work was to test the hypothesis that (a) persistent spatial patterns can be found in reflectance and remote sensing indices from UAV-based multispectral imagery of irrigation ponds, and (b) these patterns can significantly correlate with patterns of WQVs. We utilized data from sampling, in-situ sensing, and UAV-based imaging of a commercial 40-ha farm pond in Maryland. Seventeen water quality variables were measured on a permanent grid during the irrigation season concurrently with the imaging of the pond with the MicaSense Red Edge camera at five wavelengths. Twenty-four remote sensing indices were computed. Spatial patterns were determined using the mean relative difference method. The water quality patterns appeared to reflect differences in distances from banks, closeness to the creek meeting the pond, the degree of water stagnancy, dominant wind directions, and a geese congregation site. High (>0.8) Spearman correlation coefficients were found for turbidity, photosynthetic pigments, and organic carbon in water. These variables' patterns had similarities with patterns of remote sensing indices AFAI, TCARI, TCI, and MCARI. Patterns of E. coli strongly correlated with the pattern of reflectance at the red wavelength. Given the high spatiotemporal variability of WQVs in the irrigation ponds, determining patterns of remote sensing indices can be useful for the design of surveys or monitoring important aspects of water quality.