Assessment of Sentinel-2 and Landsat-8 OLI for Small-Scale Inland Water Quality Modeling and Monitoring Based on Handheld Hyperspectral Ground Truthing

Media type: E-Article
Title: Assessment of Sentinel-2 and Landsat-8 OLI for Small-Scale Inland Water Quality Modeling and Monitoring Based on Handheld Hyperspectral Ground Truthing
Contributor: Abdelal, Qasem; Assaf, Mohammed N.; Al-Rawabdeh, Abdulla; Arabasi, Sameer; Rawashdeh, Nathir A.
Published: Hindawi Limited, 2022
Published in: Journal of Sensors, 2022 (2022), Seite 1-19
Language: English
DOI: 10.1155/2022/4643924
ISSN: 1687-725X; 1687-7268
Origination:
Footnote:
Description: <jats:p>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 <jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")"> <mrow> <mn>1</mn> <mo>/</mo> <mfenced open="(" close=")"> <mrow> <mtext>B</mtext> <mn>4</mn> <mo>×</mo> <mtext>B</mtext> <mn>6</mn> </mrow> </mfenced> </mrow> </mfenced> </math> </jats:inline-formula> for estimating Chl-a (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>=</mo> <mn>0.82</mn> </math> </jats:inline-formula>, <jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mtext>RMSE</mtext> <mo>=</mo> <mn>14.52</mn> <mtext> </mtext> <mtext>mg</mtext> <mo>/</mo> <msup> <mrow> <mtext>m</mtext> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </math> </jats:inline-formula>). As for Landsat-8, red to near-infrared ratio (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mtext>B</mtext> <mn>4</mn> <mo>/</mo> <mtext>B</mtext> <mn>5</mn> </math> </jats:inline-formula>) produced the best performing model (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>=</mo> <mn>0.71</mn> </math> </jats:inline-formula>, <jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mtext>RMSE</mtext> <mo>=</mo> <mn>39.88</mn> <mtext> </mtext> <mtext>mg</mtext> <mo>/</mo> <msup> <mrow> <mtext>m</mtext> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </math> </jats:inline-formula>), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>=</mo> <mn>0.85</mn> </math> </jats:inline-formula>, <jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mtext>RMSE</mtext> <mo>=</mo> <mn>0.87</mn> </math> </jats:inline-formula> NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with <jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>=</mo> <mn>0.64</mn> </math> </jats:inline-formula>, <jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mtext>RMSE</mtext> <mo>=</mo> <mn>0.90</mn> </math> </jats:inline-formula> NTU) using Landsat-8 was performed by applying two bands (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <mtext>B</mtext> <mn>1</mn> <mo>/</mo> <mtext>B</mtext> <mn>3</mn> </math> </jats:inline-formula>). 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.</jats:p>
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