Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards

Min Gao; Junsheng Li; Fangfang Zhang; Shenglei Wang; Ya Xie; Ziyao Yin; Bing Zhang

doi:10.3390/s20226580

Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards

Sensors (Basel). 2020 Nov 18;20(22):6580. doi: 10.3390/s20226580.

Authors

Min Gao^{1

2}, Junsheng Li^{1

3}, Fangfang Zhang¹, Shenglei Wang¹, Ya Xie^{1

4}, Ziyao Yin^{1

2}, Bing Zhang^{1

2}

Affiliations

¹ Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
² College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
³ School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
⁴ School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China.

Abstract

With the development of citizen science, digital cameras and smartphones are increasingly utilized in water quality monitoring. The smartphone application HydroColor quantitatively retrieves water quality parameters from digital images. HydroColor assumes a linear relationship between the digital pixel number (DN) and incident radiance and applies a grey reference card to derive water leaving reflectance. However, image DNs change with incident light brightness non-linearly, according to a power function. We developed an improved method for observing and calculating water leaving reflectance from digital images based on multiple reflectance reference cards. The method was applied to acquire water, sky, and reflectance reference card images using a Cannon 50D digital camera at 31 sampling stations; the results were validated using synchronously measured water leaving reflectance using a field spectrometer. The R² for the red, green, and blue color bands were 0.94, 0.95, 0.94, and the mean relative errors were 27.6%, 29.8%, 31.8%, respectively. The validation results confirm that this method can derive accurate water leaving reflectance, especially when compared with the results derived by HydroColor, which systematically overestimates water leaving reflectance. Our results provide a more accurate theoretical foundation for quantitative water quality monitoring using digital and smartphone cameras.

Keywords: citizen science; digital camera; remote sensing reflectance; water leaving reflectance.

Abstract

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