Sentinel-2/Landsat-8 product consistency and implications for monitoring aquatic systems

Pahlevan, Nima; Chittimalli, Sandeep Kumar; Balasubramanian, Sundarabalan V.; Vellucci, Vincenzo

doi:10.1016/j.rse.2018.10.027

Cited by 213 publications

(143 citation statements)

References 41 publications

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“…Therefore, the map reflecting the dam's discharge is still missing. When Landsat-9 and Sentinel-2C are launched within the 2021-2023 timeframe, combined with existing L8 and Sentinel-2A/B [62], the revisit time will significantly be improved, i.e., near daily, on average. Only within that timeframe, will it be possible to explore the utility of SSC retrievals during flood events.…”

Section: Discussionmentioning

confidence: 99%

Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)

Phạm

Pahlevan

et al. 2018

Remote Sensing

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Analyzing the trends in the spatial distribution of suspended sediment concentration (SSC) in riverine surface water enables better understanding of the hydromorphological properties of its watersheds and the associated processes. Thus, it is critical to identify an appropriate method to quantify spatio-temporal variability in SSC. This study aims to estimate SSC in a highly turbid river, i.e., the Red River in Northern Vietnam, using Landsat 8 (L8) images. To do so, in situ radiometric data together with SSC at 60 sites along the river were measured on two different dates during the dry and wet seasons. Analyses of the in situ data indicated strong correlations between SSC and the band-ratio of green and red channels, i.e., r-squared = 0.75 and a root mean square error of~0.3 mg/L. Using a subsample of in situ radiometric data (n = 30) collected near-concurrently with one L8 image, four different atmospheric correction methods were evaluated. Although none of the methods provided reasonable water-leaving reflectance spectra (ρ w ), it was found that the band-ratio of the green-red ratio is less sensitive to uncertainties in the atmospheric correction for mapping SSC compared to individual bands. Therefore, due to its ease of access, standard L8 land surface reflectance products available via U.S. Geological Survey web portals were utilized. With the empirical relationship derived, we produced Landsat-derived SSC distribution maps for a few images collected in wet and dry seasons within the 2013-2017 period. Analyses of image products suggest that (a) the Thao River is the most significant source amongst the three major tributaries (Lo, Da and Thao rivers) providing suspended load to the Red River, and (b) the suspended load in the rainy season is nearly twice larger than that in the dry season, and it correlates highly with the runoff (correlation coefficient = 0.85). Although it is demonstrated that the atmospheric correction in tropical areas over these sediment-rich waters present major challenges in the retrievals of water-leaving reflectance spectra, the study signifies the utility of band-ratio techniques for quantifying SSC in highly turbid river waters. With Sentinel-2A/B data products combined with those of Landsat-8, it would be possible to capture temporal variability in major river systems in the near future.

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Section: Discussionmentioning

confidence: 99%

Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)

Phạm

Pahlevan

et al. 2018

Remote Sensing

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“…The turbidity algorithm used here is directly comparable across platforms and thus differences can be mainly attributable to the atmospheric correction and performance of the bio-optical algorithm across a wide dynamic range. Inconsistencies in radiometric calibration between the two sensors are expected to contribute to < 6% of the total uncertainty (Pahlevan et al, 2019). The wide range of turbidities observed across inland waters in this study suggests a blended approach that utilizes different algorithms at different reflectance ranges such as used for Chl-a (Hu et al, 2012) may be necessary to map turbidity across the entire dynamic range.…”

Section: Tablementioning

confidence: 93%

“…This assumption is based on the fact that even very shallow water absorbs longer wavelength light and thus should be dark (R rs~0 sr −1 ) in the SWIR bands (Wang and Shi, 2007;Shi and Wang, 2009). However, SWIR signals from strong adjacency effects over inland water pixels have been widely documented in most (although not all, see Pahlevan et al, 2019) cases. While SWIR signals over water could be caused by emergent macrophytes (Dogliotti et al, 2018), nearby land pixels contributing SWIR signals are more likely the cause (Richter et al, 2006;Bulgarelli and Zibordi, 2018).…”

Section: Negative R Rs Retrievalsmentioning

confidence: 99%

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

Kuhn

Valério

Ward

et al. 2019

Remote Sensing of Environment

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Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the > 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to shipborne, underway in situ validation measurements. The land surface product shows the best agreement (4% Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36% higher reflectance values in the visible bands compared to aquatic methods (ACOLITE and SeaDAS) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 sr −1 ) compared to Landsat-8 (0.001 sr −1 ). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59% and 35% respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 5-31% depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance. sediment loading, warming and eutrophication (Whitehead et al., 2009;Malmqvist et al., 2008). In terrestrial, ocean, coastal and lake ecosystems, satellites have been increasingly marshalled for ecological monitoring (Smith, 2003;Valerio et al., 2017), yet rivers have received relatively little attention in the field of aquatic remote sensing, in part

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“…El seguimiento de la calidad de agua es cada vez más necesario, no solo en cuanto a las necesidades de consumo humano, agronómico e industrial, sino también para el buen funcionamiento de los ecosistemas naturales. El desarrollo de la teledetección está proporcionando útiles herramientas para el estudio y seguimiento de la calidad de aguas continentales y marinas (Gholizadeh et al, 2016;Tyler et al, 2016;Page et al, 2018;Pahlevan et al, 2019). Los satélites Sentinel-2 (S2) de la ESA ofrecen imágenes de buena calidad radiométrica y adecuada resolución espacial y temporal, muy útiles en los estudios de calidad del agua, principalmente en aguas continentales debido al pequeño tamaño de muchos lagos y embalses.…”

Section: Introductionunclassified

“…Los satélites Sentinel-2 (S2) de la ESA ofrecen imágenes de buena calidad radiométrica y adecuada resolución espacial y temporal, muy útiles en los estudios de calidad del agua, principalmente en aguas continentales debido al pequeño tamaño de muchos lagos y embalses. Por ello, diferentes investigaciones llevadas a cabo en los últimos años están desarrollando métodos operativos para la estimación de la calidad del agua con S2 (Ansper y Alikas, 2019;Toming et al, 2016;Pahlevan et al, 2019;Page et al, 2019), como el proyecto llevado a cabo por los autores de este artículo Ecological Status of AQuatic Systems with Sentinel Satellites (ESAQS, https://ipl.uv.es/esaqs/), cuyo objetivo principal fue desarrollar y validar algoritmos para la estimación de indicadores ecológicos de calidad en aguas continentales, entre ellos la concentración de clorofila-a [Chl-a], la transparencia, la materia orgánica disuelta y los sólidos en suspensión, a partir de imágenes S2 en diferentes lagos y embalses de la cuenca del Júcar (Pereira-Sandoval et al, 2019a).…”

Section: Introductionunclassified

Turbidez y profundidad de disco de Secchi con Sentinel-2 en embalses con diferente estado trófico en la Comunidad Valenciana

et al. 2019

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Transparency or turbidity is one of the main indicators in studies of water quality using remote sensing. Transparency can be measured in situ through the Secchi disc depth (SD), and turbidity using a turbidimeter. In recent decades, different relationships between bands from different remote sensing sensors have been used for the estimation of these variables. In this paper, several indices and spectral bands have been calibrated in order to estimate transparency from Sentinel-2 (S2) images from field data, obtained throughout 2017 and 2018 in Júcar basin reservoirs with a great variety of trophic states. Three atmospheric correction methods developed for waters have been applied to the S2 level L1C images taken at the same day as the field data: Polymer, C2RCC and C2X. From the spectra obtained from S2 and the SD field data, it has been found that the smallest error is obtained with the images atmospherically corrected with Polymer and a potential adjustment of the reflectivities’ ratio of the blue and green bands (R490/R560), which allow the estimation of SD with a relative error of 13%. Also the C2X method presents good adjustment with the same bands ratio, although with a greater error, while the correction C2RCC shows the worst correlation. The relationship between SD (in m) and turbidity (in NTU) has also been obtained, which provides an operational method for estimating turbidity with S2. The relationship for the different reservoirs between SD and chlorophyll-a concentration, suspended solids and dissolved organic matter, is also shown.

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Sentinel-2/Landsat-8 product consistency and implications for monitoring aquatic systems

Cited by 213 publications

References 41 publications

Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)

Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

Turbidez y profundidad de disco de Secchi con Sentinel-2 en embalses con diferente estado trófico en la Comunidad Valenciana

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