Document Type
Article
Publication Date
6-20-2022
Department
Department of Applied Computing
Abstract
This study investigates the best available methods for remote monitoring inland small-scale waterbodies, using remote sensing data from both Landsat-8 and Sentinel-2 satellites, utilizing a handheld hyperspectral device for ground truthing. Monitoring was conducted to evaluate water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and turbidity. Ground truthing was performed to select the most suitable atmospheric correction technique (ACT). Several ACT have been tested: dark spectrum fitting (DSF), dark object subtraction (DOS), atmospheric and topographic correction (ATCOR), and exponential extrapolation (EXP). Classical sampling was conducted first; then, the resulting concentrations were compared to those obtained using remote sensing analysis by the above-mentioned ACT. This research revealed that DOS and DSF achieved the best performance (an advantage ranging between 29% and 47%). Further, we demonstrated the appropriateness of the use of Sentinel-2 red and vegetation red edge reciprocal bands (1/(B4 X B6)) for estimating Chl-a (R2 = 0.82, RMSE = 14.52mg/m3). As for Landsat-8, red to near-infrared ratio (B4/B5) produced the best performing model (R2 = 0.71, RMSE = 39.88 mg/m3), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model (with (R2 =0.85, RMSE = 0.87 NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with R2 = 0.64, RMSE = 0.90 NTU) using Landsat-8 was performed by applying two bands (B1/B3). Mapping the water quality parameters using the best performance biooptical model showed the significant effect of the adjacent land on the boundary pixels compared to pixels of deeper water.
Publication Title
Journal of Sensors
Recommended Citation
Abdel, Q.,
Assaf, M. N.,
Al-Rawabdeh, A.,
Arabasi, S.,
&
Rawashdeh, N.
(2022).
Assessment of Sentinel-2 and Landsat-8 OLI for Small-Scale Inland Water Quality Modeling and Monitoring Based on Handheld Hyperspectral Ground Truthing.
Journal of Sensors,
2022.
http://doi.org/10.1155/2022/4643924
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16072
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF