Coastal and inland water pixels extraction algorithm (WiPE) from spectral shape analysis and HSV transformation applied to Landsat 8 OLI and Sentinel-2 MSI

Ngoc, Dat Dinh; Loisel, Hubert; Jamet, Cédric; Vantrepotte, Vincent; Duforêt-Gaurier, Lucile; Minh, Chung Doan; Mangin, Antoine

doi:10.1016/j.rse.2019.01.024

Cited by 41 publications

(35 citation statements)

References 25 publications

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“…These sensors data can be employed in at least two aspects. First, a synergy of high-resolution images (e.g., Landsat-8 OLI and Sentinel-2 MSI as in [73,74]) provide an improved reference dataset for validation purposes. Second, high-resolution satellite images can be integrated with the MinVC NDVI to derive long-term high-resolution water surface area data (e.g., [36,37]).…”

Section: Improvements Of Current Ndvi Minvc Algorithmmentioning

confidence: 99%

Compositing the Minimum NDVI for Daily Water Surface Mapping

Fan

Liu

et al. 2020

Remote Sensing

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Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000–2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R2 = 0.914, root mean square error, RMSE = 223.7 km2), the cloud free MODIS image based result (R2 = 0.824, RMSE = 356.7 km2), the recent Landsat-MODIS image fusion based result (R2 = 0.765, RMSE = 403 km2), and the hydrodynamic modeling result (R2 = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping.

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Section: Improvements Of Current Ndvi Minvc Algorithmmentioning

confidence: 99%

Compositing the Minimum NDVI for Daily Water Surface Mapping

Fan

Liu

et al. 2020

Remote Sensing

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“…usgs.gov) of the US Geological Survey (USGS). Water pixels were identified using the WiPE masking procedure [28]. Since there was no direct matchup data for VNREDSat-1/NAOMI, an indirect evaluation of the VNREDSat-1/NAOMI derived from SPM was performed using Landsat-8/OLI images processed using the RED-NIR NAOMI atmospheric correction [24] and V1SPM algorithm.…”

Section: Satellite Match-up Data Setmentioning

confidence: 99%

A Simple Empirical Band-Ratio Algorithm to Assess Suspended Particulate Matter from Remote Sensing over Coastal and Inland Waters of Vietnam: Application to the VNREDSat-1/NAOMI Sensor

Ngoc

Loisel

Vantrepotte

et al. 2020

Water

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VNREDSat-1 is the first Vietnamese satellite enabling the survey of environmental parameters, such as vegetation and water coverages or surface water quality at medium spatial resolution (from 2.5 to 10 m depending on the considered channel). The New AstroSat Optical Modular Instrument (NAOMI) sensor on board VNREDSat-1 has the required spectral bands to assess the suspended particulate matter (SPM) concentration. Because recent studies have shown that the remote sensing reflectance, Rrs(λ), at the blue (450–520 nm), green (530–600 nm), and red (620–690 nm) spectral bands can be assessed using NAOMI with good accuracy, the present study is dedicated to the development and validation of an algorithm (hereafter referred to as V1SPM) to assess SPM from Rrs(λ) over inland and coastal waters of Vietnam. For that purpose, an in-situ data set of hyper-spectral Rrs(λ) and SPM (from 0.47 to 240.14 g·m−3) measurements collected at 205 coastal and inland stations has been gathered. Among the different approaches, including four historical algorithms, the polynomial algorithms involving the red-to-green reflectance ratio presents the best performance on the validation data set (mean absolute percent difference (MAPD) of 18.7%). Compared to the use of a single spectral band, the band ratio reduces the scatter around the polynomial fit, as well as the impact of imperfect atmospheric corrections. Due to the lack of matchup data points with VNREDSat-1, the full VNREDSat-1 processing chain (atmospheric correction (RED-NIR) and V1SPM), aiming at estimating SPM from the top-of-atmosphere signal, was applied to the Landsat-8/OLI match-up data points with relatively low to moderate SPM concentration (3.33–15.25 g·m−3), yielding a MAPD of 15.8%. An illustration of the use of this VNREDSat-1 processing chain during a flooding event occurring in Vietnam is provided.

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“…In practice, the ROI used for parameterizing the SWIR-based relationships was performed considering the following successive masks: 1) Water mask: A land and cloud detection was first performed on the whole image. The method used here is based on the recent works by Ngoc et al, (in review [35]), which detects cloud and cloud shadow over glint-free pixels and which is based on the statistical analysis of the Rayleigh corrected TOA reflectance (i.e. spectral shape analysis, Hue-Saturation-Value analysis).…”

Section: Region Of Interest (Roi) Definitionmentioning

confidence: 99%

“…-Water pixel definition: the approach chosen for masking land and cloud pixels was here based on Ngoc et al, (in review) [35]. Note that a main issue still remains regarding the identification of thin cloud affected pixels over sun glinted areas.…”

Section: Automated Sun Glint Correction Proceduresmentioning

confidence: 99%

Automated SWIR based empirical sun glint correction of Landsat 8-OLI data over coastal turbid water

Zorrilla¹,

Vantrepotte²,

Ngoc³

et al. 2019

Opt. Express

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The optimal exploitation of the oceanic information provided by recent high spatial resolution sensors such as Landsat 8-OLI is strongly conditioned by the quality of the water reflectance signal retrieval. One main issue stands in the ability to correct water pixels for the contamination of the sun glint, which might induce a seasonal or permanent loss of data according to the latitude. The SWIR information now provided for the most recent high spatial resolution sensors was used for evaluating the sun glint level and correcting the radiative signal for its effect. This has been performed transposing historical empirical formalisms based on the NIR signal. An automated SWIR-based sun glint correction procedure was then developed using a 4-year OLI archive gathered over very turbid waters of French Guiana (227 scenes). This procedure allows the practical limitations associated with past similar empirical methods (sensitivity to water turbidity and manual image per image correction) to be overcome. While a satisfactory preservation of the information over sun glint free pixels was observed, comparison exercises based on in situ R rs data gathered in sun glint affected areas emphasize the relevance of the proposed methodology (correction by a factor of 14 of the averaged bias in the R rs values after removing sun glint effects). Current limitations in the applicability of this SWIR-based empirical automated method are mainly associated with the presence of high cloud coverage, thin clouds in the OLI scene or highly spatially variable marine or atmospheric signal (around 47%, 42% and 11%, respectively, of the total of 12% of failure over French Guiana OLI archive). The potential large applicability of the procedure developed in this work was eventually demonstrated over contrasted coastal environments.

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Coastal and inland water pixels extraction algorithm (WiPE) from spectral shape analysis and HSV transformation applied to Landsat 8 OLI and Sentinel-2 MSI

Cited by 41 publications

References 25 publications

Compositing the Minimum NDVI for Daily Water Surface Mapping

Compositing the Minimum NDVI for Daily Water Surface Mapping

A Simple Empirical Band-Ratio Algorithm to Assess Suspended Particulate Matter from Remote Sensing over Coastal and Inland Waters of Vietnam: Application to the VNREDSat-1/NAOMI Sensor

Automated SWIR based empirical sun glint correction of Landsat 8-OLI data over coastal turbid water

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