Satellite-derived bathymetry from Landsat-8 and Sentinel-2A images: Assessment of atmospheric correction and depth derivation models for shallow waters

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.

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Contemporary comparative assessment of atmospheric correction influence on radiometric indices between Sentinel-2A and Landsat 8 imagery

Geocarto International ◽

10.1080/10106049.2019.1590465 ◽

2019 ◽

Vol 36 (1) ◽

pp. 13-27 ◽

Cited By ~ 3

Author(s):

Luka Rumora ◽

Mario Miler ◽

Damir Medak

Keyword(s):

Atmospheric Correction ◽

Comparative Assessment ◽

Landsat 8 ◽

Sentinel 2A

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Modelling Reservoir Chlorophyll-a, TSS, and Turbidity Using Sentinel-2A MSI and Landsat-8 OLI Satellite Sensors with Empirical Multivariate Regression

Journal of Sensors ◽

10.1155/2020/8858408 ◽

2020 ◽

Vol 2020 ◽

pp. 1-21

Author(s):

Yashon O. Ouma ◽

Kimutai Noor ◽

Kipkemoi Herbert

Keyword(s):

Water Quality ◽

Optical Sensors ◽

Multivariate Regression ◽

Atmospheric Correction ◽

Quality Parameters ◽

Water Quality Parameters ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Band Adjustment ◽

Sentinel 2A

Sentinel-2A/MSI (S2A) and Landsat-8/OLI (L8) data products present a new frontier for the assessment and retrieval of optically active water quality parameters including chlorophyll-a (Chl-a), suspended particulate matter (TSS), and turbidity in reservoirs. However, because of their differences in spatial and spectral samplings, it is critical to evaluate how well the sensors are suited for the seamless generation of the water quality parameters (WQPs). This study presents results from the retrieval of the WQP in a reservoir from L8 and S2A optical sensors, after atmospheric correction and standardization through band adjustment. An empirical multivariate regression model (EMRM) algorithmic approach is proposed for the estimation of the water quality parameters in correlation with in situ laboratory measurements. From the results, both sensors estimated Chl-a concentrations with R2 of greater than 70% from the visible green band for L8 and a combination of green and SWIR-1 bands for S2A. While the NMSE% was nearly the same for both sensors in Chl-a estimation, the RMSE was <10 μg/L and >10 μg/L for L8 and S2A estimations of Chl-a, respectively. For TSS retrieval, L8 outperformed S2A by 31% in accuracy with R2>0.9 from L8’s red, blue, and green bands, as compared to 0.47≤R2≥0.61 from S2A’s red and NIR bands. The RMSE were the same as for Chl-a, and the NMSE% were both in the same range. Both sensors retrieved turbidity with high and nearly equal accuracy of R2>70% from the visible and NIR bands, with equal RMSE at <10% NTU and NMAE% from S2A being higher by more than 30% as compared to L8’s NMAE% at 15%. The study concluded that the higher performance accuracy of L8 is attributed to its higher SNR and spectral bandwidth placement as compared to S2A bands. Comparatively, S2A overestimated Chl-a and turbidity but performed equally well compared to OLI in the estimation of TSS. The results show that while absolute accuracy of retrieval of the WQPs still requires improvements, the developed algorithms are broadly able to discern the biooptical water quality in reservoirs.

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Quantification of Polychlorinated Biphenyl (PCB) Concentration in San Francisco Bay Using Satellite Imagery

Remote Sensing ◽

10.3390/rs10071110 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1110 ◽

Cited By ~ 3

Author(s):

Annette Hilton ◽

Jesse Bausell ◽

Raphael Kudela

Keyword(s):

San Francisco ◽

Satellite Imagery ◽

Polychlorinated Biphenyl ◽

San Francisco Bay ◽

Atmospheric Correction ◽

Cost Effective ◽

Landsat 8 ◽

Valuable Insight ◽

Sentinel 2A

The U.S. Environmental Protection Agency banned the use of polychlorinated biphenyls (PCBs) in 1979, due to the high environmental and public health risks with which they are associated. However, PCBs continue to persist in the San Francisco Bay (SFB), often at concentrations deemed unsafe for humans. In situ PCB monitoring within the SFB is extremely limited, due in large part to the high monetary costs associated with sampling. Here we offer a cost effective alternative to in situ PCB monitoring by demonstrating the feasibility of indirectly quantifying PCBs in the SFB via satellite remote sensing using a two-step approach. First, we determined the relationship between in situ PCB concentrations and suspended sediment concentrations (SSC) in the SFB. We then correlated in situ SSC with spatially and temporally consistent Landsat 8 and Sentinel 2A reflectances. We demonstrate strong relationships between SSC and PCBs in all three SFB sub-embayments (R2 > 0.28–0.80, p < 0.01), as well as a robust relationship between SSC and satellite measurements for both Landsat 8 and Sentinel 2A (R2 > 0.72, p < 0.01). These relationships held regardless of the atmospheric correction regime that we applied. The end product of these relationships is an empirical two-step relationship capable of deriving PCBs from satellite imagery. Our approach of estimating PCBs in the SFB by remotely sensing SSC is extremely cost-effective when compared to traditional in situ techniques. Moreover, it can also be utilized to generate PCB concentration maps for the SFB. These maps could one day serve as an important tool for PCB remediation in the SFB, as they can provide valuable insight into the spatial distribution of PCBs throughout the bay, as well as how this distribution changes over time.

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A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

Remote Sensing ◽

10.3390/rs13152869 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2869

Author(s):

MohammadAli Hemati ◽

Mahdi Hasanlou ◽

Masoud Mahdianpari ◽

Fariba Mohammadimanesh

Keyword(s):

Time Series ◽

Change Detection ◽

Policy Development ◽

Atmospheric Correction ◽

Open Data ◽

Detection Methods ◽

Landsat 8 ◽

Landsat Images ◽

Detection Analysis ◽

The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

Remote Sensing ◽

10.3390/rs13101927 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1927

Author(s):

Fuqin Li ◽

David Jupp ◽

Thomas Schroeder ◽

Stephen Sagar ◽

Joshua Sixsmith ◽

...

Keyword(s):

Coastal Waters ◽

Atmospheric Correction ◽

Correction Method ◽

In Situ Measurements ◽

Landsat 8 ◽

Correction Algorithm ◽

Simple Method ◽

North East ◽

The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

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Non-Binary Snow Index for Multi-Component Surfaces

Remote Sensing ◽

10.3390/rs13142777 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2777

Author(s):

Mario Arreola-Esquivel ◽

Carina Toxqui-Quitl ◽

Maricela Delgadillo-Herrera ◽

Alfonso Padilla-Vivanco ◽

Gabriel Ortega-Mendoza ◽

...

Keyword(s):

Spectral Characteristics ◽

Ice Cover ◽

Landsat 8 ◽

Water Index ◽

Bare Land ◽

Snow Water ◽

Precision Assessment ◽

Normalized Difference Snow Index ◽

Sentinel 2A ◽

Water Vegetation

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

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