scholarly journals Atmospheric correction algorithm over coastal and inland waters based on the red and NIR bands: application to Landsat-8/OLI and VNREDSat-1/NAOMI observations

2019 ◽  
Vol 27 (22) ◽  
pp. 31676 ◽  
Author(s):  
Dat Dinh Ngoc ◽  
Hubert Loisel ◽  
Lucile Duforêt-Gaurier ◽  
Cedric Jamet ◽  
Vincent Vantrepotte ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Inland Waters ◽  
Landsat 8 ◽  
Correction Algorithm ◽  
Landsat 8 Oli

scholarly journals The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters

2019 ◽  
Vol 11 (2) ◽  
pp. 169 ◽  
Author(s):  
Dian Wang ◽  
Ronghua Ma ◽  
Kun Xue ◽  
Steven Loiselle
Keyword(s):  
Remote Sensing ◽  
Lake Taihu ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Inland Waters ◽  
Landsat 8 ◽  
Atmospheric Conditions ◽  
Landsat 8 Oli ◽  
Turbid Waters ◽  
Band Ratios

The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (Rrs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best Rrs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters.

scholarly journals Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

2018 ◽  
Vol 51 (1) ◽  
pp. 525-542 ◽  
Author(s):  
L. De Keukelaere ◽  
S. Sterckx ◽  
S. Adriaensen ◽  
E. Knaeps ◽  
I. Reusen ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Inland Waters ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Algorithm Validation ◽  
Sentinel 2

Improved turbidity estimates in complex inland waters using combined NIR–SWIR atmospheric correction approach for Landsat 8 OLI data

2018 ◽  
Vol 39 (21) ◽  
pp. 7463-7482
Author(s):  
Venkata Vijay Arun Kumar Surisetty ◽  
Arvind Sahay ◽  
Ratheesh Ramakrishnan ◽  
Rabindro Nath Samal ◽  
Ajay Singh Rajawat
Keyword(s):  
Atmospheric Correction ◽  
Inland Waters ◽  
Landsat 8 ◽  
Landsat 8 Oli

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

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.

scholarly journals A tool for analysis of the influence of the Earth surface soil layer temperature on the inhomogeneity of grain crops development by the Earth remote sensing data

2021 ◽  
Author(s):  
R.V. Brezhnev ◽  
Yu.A. Maglinets ◽  
K.V. Raevich ◽  
V.G. Margaryan
Keyword(s):  
Surface Temperature ◽  
Atmospheric Correction ◽  
Sensor Data ◽  
Relational Model ◽  
Landsat 8 ◽  
Earth Surface ◽  
Agricultural Crops ◽  
Landsat 8 Oli ◽  
The Earth ◽  
Vegetation Heterogeneity

The work is devoted to the analysis of the influence of the earth surface temperature on the inhomogeneity of the agricultural crops development. The aim of the work is to expand the object-relational model for describing the inhomogeneous spatial structure of a spatial object by including surface temperature as one of the key features that allow determining the cause of vegetation heterogeneity, along with relief features, differences in the soil chemical composition and other significant characteristics. Experimental studies are carried out at sites located in Sukhobuzimsky district of Krasnoyarsk Territory, for which agricultural crops (grains) and the their sowing dates are known a priori, which allows stating any facts of the vegetation development deviation from the normative trajectory with reference to the sequence and timing norms of phenological phase changing. Landsat-8 OLI (Operational Land Imager) TIRS (Thermal Infrared Sensor) data are used as initial data for temperature measurements. Objects of research are presented in the form of a polygon map in SHP format. The temperature values are calculated using the algorithm for estimating the earth temperature developed by Weng Q., Lu D. and Schubring J. The surface reflectance values are the NDVI vegetation index values also obtained from the Landsat-8 OLI data that underwent atmospheric correction by the DOS method. The research results are implemented in the form of a software module and integrated into the Earth remote monitoring (ERM) system of SFU Space and Information Technologies Institute (SITI). The results are used within the concept of object-oriented monitoring of spatial objects developed by the team of authors, and represent index images of the surface temperature of objects, as well as vector schematic maps.

scholarly journals An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data

2020 ◽  
Vol 143 ◽  
pp. 02003
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Kaiyuan Bai ◽  
Xiaojuan Li
Keyword(s):  
Chlorophyll A ◽  
Environmental Issue ◽  
Inland Waters ◽  
Landsat 8 ◽  
Ratio Model ◽  
Landsat 8 Oli ◽  
Band Ratio ◽  
Chl A ◽  
Quality Indexes

Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).

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