scholarly journals Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

2019 ◽  
Vol 11 (2) ◽  
pp. 184 ◽  
Author(s):  
Kun Xue ◽  
Ronghua Ma ◽  
Dian Wang ◽  
Ming Shen
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Lake Taihu ◽  
Optical Parameters ◽  
Water Type ◽  
Water Types ◽  
Remote Sensing Reflectance ◽  
Inorganic Matter ◽  
Suspended Particulate ◽  
Huai River

Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing reflectance spectra (NRrs(λ)) using the k-means clustering approach, and were identified in the Sentinel 3A OLCI (Ocean Land Color Instrument) image data over lakes in the LYHR Basin. The results showed that 1) Each OWT is associated with different bio-optical properties, such as the concentration of chlorophyll-a (Chla), suspended particulate matter (SPM), proportion of suspended particulate inorganic matter (SPIM), and absorption coefficient of each component. One optical water type showed an obvious characteristic with a high contribution of mineral particles, while one type was mostly determined by a high content of phytoplankton. The other types belonged to the optically mixed water types. 2) Class-specific Chla inversion algorithms performed better for all water types, except type 4, compared to the overall dataset. In addition, class-specific inversion algorithms for estimating the Chla-specific absorption coefficient of phytoplankton at 443 nm (a*ph(443)) were developed based on the relationship between a*ph(443) and Chla of each OWT. The spatial variations in the class-specific model-derived a*ph(443) values were illustrated for 2 March 2017, and 24 October 2017. 3) The dominant water type and the Shannon index (H) were used to characterize the optical variability or similarity of the lakes in the LYHR Basin using cloud-free OLCI images in 2017. A high optical variation was located in the western and southern parts of Lake Taihu, the southern part of Lake Hongze, Lake Chaohu, and several small lakes near the Yangtze River, while the northern part of Lake Hongze had a low optical diversity. This work demonstrates the potential and necessity of optical classification in estimating bio-optical parameters using class-specific inversion algorithms and monitoring of the optical variations in optically complex and dynamic lake waters.

scholarly journals Inherent Optical Properties in Lake Taihu Derived from VIIRS Satellite Observations

2019 ◽  
Vol 11 (12) ◽  
pp. 1426 ◽  
Author(s):  
Wei Shi ◽  
Menghua Wang ◽  
Yunlin Zhang
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Interannual Variability ◽  
Near Infrared ◽  
Lake Taihu ◽  
Temporal Variations ◽  
Satellite Observations ◽  
Seasonal And Interannual Variability ◽  
Remote Sensing Reflectance

Using in situ remote sensing reflectance and inherent optical property (IOP) measurements, a near-infrared (NIR)-based IOP algorithm is developed and tuned for Lake Taihu, in order to derive the particle backscattering coefficient bbp(λ), total absorption coefficient at(λ), dissolved and detrital absorption coefficient adg(λ), and phytoplankton absorption coefficient aph(λ), with satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). The IOP algorithm for Lake Taihu has a reasonably good accuracy. In fact, the determination coefficients between the retrieved and in situ IOPs are 0.772, 0.638, and 0.487 for at(λ), adg(λ), and aph(λ), respectively. The IOP products in Lake Taihu that have been derived from VIIRS-SNPP observations show significant spatial and temporal variations. Southern Lake Taihu features enhanced bbp(λ) and adg(λ), while northern Lake Taihu shows higher aph(λ). The seasonal and interannual variability of adg(λ) and bbp(λ) in Lake Taihu is quantified and characterized with the highest bbp(λ) and adg(λ) in the winter, and the lowest in the summer. In the winter, bbp(443) and adg(443) can reach over ~1.5 and ~5.0 m−1, respectively, while they are ~0.5–1.0 and ~2.0 m−1 in the summer. This study shows that in Lake Taihu adg(λ) is the most significant IOP, while aph(λ) is the least in terms of the IOP values and contributions to remote sensing reflectance. The highest bbp(λ) and adg(λ) occurred in the winter between 2017–2018, and the lowest bbp(λ) and adg(λ) occurred in the summer of 2014. In comparison, the seasonal and interannual variability of mean aph(λ) for Lake Taihu is less significant, even though enhanced seasonal and interannual variability can be found in some parts of Lake Taihu, such as in the northern Lake Taihu region.

Hyperspectral absorption coefficient of “pure” seawater in the range of 350–550  nm inverted from remote sensing reflectance

2015 ◽  
Vol 54 (3) ◽  
pp. 546 ◽  
Author(s):  
Zhongping Lee ◽  
Jianwei Wei ◽  
Ken Voss ◽  
Marlon Lewis ◽  
Annick Bricaud ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Remote Sensing Reflectance

Deciphering Optical Water Types of Wetlands using multispectral Earth observation datasets

2020 ◽  
Author(s):  
Satyasri Allaka ◽  
Manudeo Singh ◽  
Rajiv Sinha
Keyword(s):  
Chlorophyll Content ◽  
Color Index ◽  
Coastal Wetland ◽  
Hyperspectral Data ◽  
Anthropogenic Factors ◽  
Optical Parameters ◽  
Spectral Indices ◽  
Water Types ◽  
Inorganic Matter ◽  
Sensitive Parameters

<p>Wetlands are important and highly productive ecosystems in a variety of geomorphic settings ranging from inland to coastal environments. Wetlands are very dynamic in nature and are driven by the water and sediment fluxes carried by the streamlets throughout the year. Wetlands are under tremendous pressure all over the world due to various natural and anthropogenic factors, and therefore, require an immediate attention for their conservation. The available studies on wetland have given much less importance to the internal dynamics of the wetlands, which is primarily driven by hydrology and Land Use Land Cover (LULC) changes. Here, we propose to use the Optical Water Types (OWTs) concept to understand the hydrodynamics within the wetland.</p><p>The OWTs are the aquatic counterpart of terrestrial LULC classification and can be created by clustering of optically sensitive parameters like chlorophyll content, turbidity, suspended organic and inorganic matter using remote sensing reflectance, absorption, and scattering parameters. The Forel Ule (FU) color index, a visual color comparison scale of water bodies ranging from blue to cola brown (1-21), used a similar idea but is fairly limited in scope. The hyperspectral datasets have distinct absorption and reflection spectrum for various optically sensitive parameters, and therefore, they are particularly suited for this work. However, the availability of the high-resolution hyperspectral data is very limited and hence this research explores the possibility of deciphering the OWTs using multispectral datasets.</p><p>A possible approach to create OWTs is using the spectral indices of the multispectral datasets which are sensitive to the optical parameters instead of using the FU color index as a single parameter. In this work, various spectral indices which are independent and highly sensitivity to chlorophyll content, turbidity, suspended organic and inorganic matter are identified using the principal component analysis. The OWT clusters are created using the iso-cluster unsupervised classification similar to the LULC classification but the spectral indices are taken into account instead of directly using the spectral bands of satellite datasets. In this work, the Sentinel – 2A and 2B datasets are used to create independent OWT clusters of the Chilika (a Coastal wetland, along the east coast of India covering an area of 1,165 km<sup>2</sup>) and Kaabar Tal (an inland wetland in north Bihar plains, India covering an area of 51 km<sup>2</sup>) using the supervised classification method. The developed framework is very simple and robust in nature but the only disadvantage is that the clusters are variable in the temporal context. However, the temporal variations can be integrated with the spatial analysis to understand the wetland dynamics in the context of both space and time.</p>

scholarly journals A simple empirical model for remote sensing reflectance of Lake Taihu waters in autumn

2007 ◽  
Vol 19 (3) ◽  
pp. 227-234 ◽  
Author(s):  
MA Ronghua ◽  
◽  
SONG Qingjun ◽  
TANG Junwu ◽  
PAN Delu
Keyword(s):  
Remote Sensing ◽  
Empirical Model ◽  
Lake Taihu ◽  
Remote Sensing Reflectance ◽  
Simple Empirical Model

scholarly journals SPECTRAL OF REMOTE SENSING REFLECTANCE OF SURFACE WATERS

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Bisman Nababan ◽  
Anak A.G. Wirapramana ◽  
Risti E. Arhatin
Keyword(s):  
Remote Sensing ◽  
Gulf Of Mexico ◽  
Surface Waters ◽  
Coastal Waters ◽  
General Pattern ◽  
Water Type ◽  
Offshore Area ◽  
Minimum Value ◽  
Remote Sensing Reflectance ◽  
Blue Wavelength

<p>Spectral measurements of remote sensing reflectance (R<sub>rs</sub>) of surface waters in the northeastern Gulf of Mexico were conducted in various seasons in 1999-2000 using Fieldspec Analytical Spectral Devices (ASD) Spectroradiometer. Filtering process was performed on the data to eliminate invalid data. In general, in coastal waters particularly near rivers mouth (water type-2) the R<sub>rs</sub> spectrals were relatively low at blue, maximum at green, and decreased to a minimum value at the red wavelength. In offshore waters (type-1), the general pattern of R<sub>rs</sub> spectrals were maximum at the blue wavelength and then continued to decline at the green wavelength until the minimum value at the red wavelength except during summer where R<sub>rs</sub> spectrals in most offshore area having the maximum value at the green wavelength due to the phytoplankton bloom as a result of freshwater intrusion from the Mississippi river. In general, the patterns and values of R<sub>rs</sub> ​​were significantly different among seasons and locations. Results showed that R<sub>rs</sub> values ​​at the blue wavelength (λ=400 nm) were generally higher in the spring than in other seasons ranging of 0.007-0.018 sr<sup>-1</sup> in offshore waters and 0.004-0.015 sr<sup>-1</sup> in coastal waters. During spring, Rrs values at the green ​​wavelength (λ=500 nm) were also higher than in other seasons ​​ranging of 0.005-0.013 sr<sup>-1</sup> found in coastal waters. However, during summer in coastal waters, the maximum values of R<sub>rs</sub> spectrals were found in different green wavelength on different locations showed the differences in the type and composition of phytoplankton, organic materials, and suspension matters at those locations.</p> Keywords: remote sensing reflectance, phytoplankton, offshore, coastal, Gulf of Mexico

scholarly journals Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 742 ◽  
Author(s):  
Tuuli Soomets ◽  
Kristi Uudeberg ◽  
Dainis Jakovels ◽  
Agris Brauns ◽  
Matiss Zagars ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Inland Waters ◽  
Water Type ◽  
Water Types ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Level 2 ◽  
Sentinel 2

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

Sign in / Sign up

Export Citation Format

Share Document