Atmospheric correction issues for water quality assessment from remote sensing: the case of Lake Qarun (Egypt)

Bitelli, Gabriele; Mandanici, Emanuele

doi:10.1117/12.865083

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Both contributions to the recorded signal depend on the RSRF, which controls broadband quantities. Furthermore, over low-reflectance surfaces such as water, the contribution of atmospheric scattering may be dominant [16]. The comparisons were therefore performed on surface reflectance values obtained by atmospheric correction.…”

Section: Real Imagesmentioning

confidence: 99%

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

2016

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Abstract:The availability of new generation multispectral sensors of the Landsat 8 and Sentinel-2 satellite platforms offers unprecedented opportunities for long-term high-frequency monitoring applications. The present letter aims at highlighting some potentials and challenges deriving from the spectral and spatial characteristics of the two instruments. Some comparisons between corresponding bands and band combinations were performed on the basis of different datasets: the first consists of a set of simulated images derived from a hyperspectral Hyperion image, the other five consist instead of pairs of real images (Landsat 8 and Sentinel-2A) acquired on the same date, over five areas. Results point out that in most cases the two sensors can be well combined; however, some issues arise regarding near-infrared bands when Sentinel-2 data are combined with both Landsat 8 and older Landsat images.

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Section: Real Imagesmentioning

confidence: 99%

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

2016

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“…Some previous studies have made preliminary analysis and discussion about the turbidity of coastal waters using remote sensing data [35]. Nevertheless, the sensitive bands of satellite sensors are different for different types of water bodies [36], [37]. To accurately retrieve turbidity information from lake water, a band with sufficient sensitivity is required.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Turbidity Variation for Lakes in Daqing of Northeast China Using Landsat Images From 1984 to 2018

Wang

Song

Wen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011-2018) ，this study developed a retrieval model suitable for turbidity. The determination coefficient (R 2 ) of the model was 0.946 and the root mean square error (RMSE) was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China . The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a (P< 0.05) with obvious seasonal differences (i.e., decreased from May to the lowest in July, and then increased from September onwards). Finally, we quantitatively examined how several typical factors affect turbidity variation at different scales. We found that water turbidity was highly correlated with NDVI (R=0.56, P< 0.001), followed by water temperature and wind speed (0.02

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Evaluation method of water quality for river based on multi-spectral remote sensing data

Xiao

Jian

Xiongfei

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the rapid development of the regional economy, water pollution has gradually become an environmental problem that cannot be ignored. As an important water source in central China, the Han River should strengthen water quality monitoring and management in order to ensure the sustainable development of watershed and related areas. Taking typical sections of middle and lower reaches of the Han River as the study area, this paper focuses on rapid river water quality assessment using multispectral remote sensing images.Based on measured water quality data and synchronous spatial high and medium-resolution remote sensing data (multi-spectral data of ZY3 and HJ1A) in 2013, neural network algorithm is used to establish water quality index retrieval model for the study area, and then water quality status is assessed accordingly. The results show that BP neural network retrieval model of water quality index that is established based on multispectral data of ZY3 satellite has higher accuracy and that its assessment results are of high credibility and strong applicability, which can really reflect changes in water quality and better achieve water quality assessment for the study area. In addition, water quality assessment results show that major excessive factors in the study area are total nitrogen and total phosphorus; the polluting type is organic pollution; water quality varies greatly with seasons. INTRODUCTIONWater quality evaluation is a fundamental link in water environment management and monitoring. Only through water quality monitoring can water quality be reasonably evaluated and targeted water environment management planning and scheme be developed. In terms of water quality evaluation, traditional methods like water sample collection, indicator analysis and grade evaluation can only provide water quality status at the sampling point instead of large area of waters, while large-scale field sampling will consume a large amount of manpower, materials and financial resources. In recent years, with the rapid development of remote sensing technique, more and more researchers carried out fast, continuous and dynamic monitoring on waters by means of remote sensing technique.Further, this technique has been adopted by lots of domestic and foreign scholars on water quality evaluation (Wu, 2012, Gu, 2014, Zhu, 2013, Bitelli, 2010, Markogianni , 2014, Syahreza ,2012, Alparslan, 2007, Thiemann , 2000, and most of these studies used remote-sensing data to quantitatively retrieve concentration of water quality parameter and then establish a water quality evaluation model on this basis. The difficulty of this method mainly lies in the establishment of a definite linear relationship between remote sensing data and water quality parameter. Existing studies have shown that neural networks can better simulate the complex nonlinear relationship between remote sensing signal and water quality parameter concentration and have significantly higher retrieval accuracy than empirical models (Keiner,1998, Buckton, 1999, ...

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Atmospheric correction issues for water quality assessment from remote sensing: the case of Lake Qarun (Egypt)

Cited by 3 publications

References 8 publications

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

Quantifying Turbidity Variation for Lakes in Daqing of Northeast China Using Landsat Images From 1984 to 2018

Evaluation method of water quality for river based on multi-spectral remote sensing data

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