Improving remote sensing retrieval of global ocean transparency with optical water classification

Jia, Tianxia; Zhang, Yonglin; Weng, Chih‐Huang; Dong, Rencai

doi:10.1016/j.ecolind.2022.109359

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…Keivan Kabiri [17] proposed a method for the estimation of Zsd based on MODIS Kd(490) data and apply it in the northern Persian Gulf, the Strait of Hormuz, and the Gulf of Oman. Many other researchers have studied the spatial and temporal distributions of transparency in global oceans, and inland waters [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. However, few scholars discuss the influence of different atmospheric correction (AC) algorithms on the accuracy of retrieved transparency.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Liu,

Hu,

Tian

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Water transparency is one of the important parameters to describe the optical properties of seawater. It is a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. First, this article verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Second, the water transparency estimation results retrieved from Geostationary Ocean Color Imager (GOCI) using four atmospheric correction (AC) algorithms were compared and analyzed with in-situ data in the study area. The four AC algorithms are the standard AC algorithms of Korea Ocean Satellite Center (KOSC) GOCI GDPS1.3 and GDPS2.0, the standard near-infrared AC algorithm of NASA, and management unit of the North Sea mathematical models. At last, GOCI data on Mar 09, 2018, were used to analyze the differences in hourly changes of transparency caused by different AC algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R 2 >0.8, MAPE = 16.4%). The difference in transparency value caused by the AC algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency is presented based on different AC algorithms, presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different AC algorithms are not completely consistent. Index Terms-Atmospheric correction (AC), geostationary ocean color imager (GOCI), hourly change, Jiaozhou bay and Qingdao coastal area, water transparency.

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Section: Introductionmentioning

confidence: 99%

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Liu,

Hu,

Tian

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Erosion is increasing the sediment in the water, which could lead to silt buildup, thereby affecting the water transparency in Saguling Dam [3] [4]. The level of water transparency directly influences the processes of photosynthesis and the degree of eutrophication in the water body [3].…”

Section: Introductionmentioning

confidence: 99%

Remote sensing estimation of water transparency for Saguling Dam in the past decade (2013-2022) based on Landsat 8

Awfa,

Wicaksono,

Putra

et al. 2024

E3S Web of Conf.

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Water transparency (i.e., Secchi disk depth (SDD) reflects light transmission capacity of a water body and influences growth of aquatic plants, aquatic organisms, and primary productivity. In this study, using remote sensing reflectance (Landsat 8) and SDD datasets, we predicted the water transparency in Saguling Dam for the first time. The results indicated that the models in the visible (Band 1, Band 2, and Band 3) combined with near infrared (Band 4 and Band 5) are the most robust and reliable to estimate SDD for Saguling Dam. Subsequently, multiple linear regression model was built using 125 pairs of in situ SDD results and concurrent with Landsat images during the last decade (i.e., 2013-2022). The models were validated with an independent dataset of 33 SDD measurement. The in situ remote sensing model compared well with the in situ SDD measurement where determinant coefficient (adjusted R2) and root mean square error (RMSE) ranging from 0.35 - 0.94 and 0.3 - 1.4 m, respectively. Finally, the model was applied to Landsat 8 images acquired between 2013-2022 to elucidate the spatial distribution of SDD in Saguling Dam for each year (i.e. temporal variations) with water area around 33.7 km2. The estimation results indicated that water transparency values in the inlet and outlet zone have relatively low SDD, with SDD value ranging from 0.019 – 1.01 m and 0.24 – 1.04 m, respectively. Furthermore, based on the National Water Quality Standards (i.e., Indonesia Government Regulations No. 22 year 2021/ Peraturan Pemerintah No. 22 Tahun 2021), the estimation of spatial SDD results indicated that > 99% of Saguling Dam water bodies were classified as Class IV (i.e., could be only used for irrigation). This study provides the first comprehensive remote sensing model for Saguling Dam and can provide essential information for local water quality conservation.

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Regional to global assessments of ocean transparency dynamics from 1997 to 2019

Guo,

Zhao,

Cao

et al. 2023

Progress in Oceanography

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Improving remote sensing retrieval of global ocean transparency with optical water classification

Cited by 6 publications

References 43 publications

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Remote sensing estimation of water transparency for Saguling Dam in the past decade (2013-2022) based on Landsat 8

Regional to global assessments of ocean transparency dynamics from 1997 to 2019

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