Linear spectral unmixing algorithm for modelling suspended sediment concentration of flash floods, upper Tekeze River, Ethiopia

Gebreslassie, Hagos; Melesse, Assefa M.; Bishop, Kevin; Gebremariam, Azage Gebreyohannes

doi:10.1016/j.ijsrc.2019.07.007

Cited by 12 publications

(7 citation statements)

References 33 publications

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“…It varies between 0-1, the more the value approaches 1, the better the result is [70,71]. A model with high R 2 and low RMSE were considered as a good model [72].…”

Section: Verification Methods Of Npp Calculationmentioning

confidence: 99%

Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

et al. 2021

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Monitoring vegetation net primary productivity (NPP) is very important for evaluating ecosystem health. However, the nonlinear characteristics of the vegetation NPP remain unclear in the six provinces along the Maritime Silk Road in China. In this study, using NDVI and meteorological data from 1982 to 2015, NPP was estimated with the Carnegie-Ames-Stanford Approach (CASA) model based on vegetation type dynamics, and its nonlinear characteristics were explored through the ensemble empirical mode decomposition (EEMD) method. The results showed that: (1) The total NPP in the changed vegetation types caused by ecological engineering and urbanization increased but decreased in those caused by agricultural reclamation and vegetation destruction, (2) the vegetation NPP was dominated by interannual variations, mainly in the middle of the study area, while by long-term trends, mainly in the southwest and northeast, (3) for most of the vegetation types, NPP was dominated by the monotonically increasing trend. Although vegetation NPP in the urban land mainly showed a decreasing trend (monotonic decrease and decrease from increase), there were large areas in which NPP increased from decreasing. Although vegetation NPP in the farmland mainly showed increasing trends, there were large areas that faced the risk of NPP decreasing; (4) dynamical changes of vegetation type by agricultural reclamation and vegetation destruction made the NPP trend monotonically decrease in large areas, leading to ecosystem degradation, while those caused by urbanization and ecological engineering mainly made the NPP increase from decreasing, leading to later recovery from early degradation. Our results highlighted the importance of vegetation type dynamics for accurately estimating vegetation NPP, as well as for assessing their impacts, and the importance of nonlinear analysis for deepening our understanding of vegetation NPP changes.

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“…It varies between 0-1, the more the value approaches 1, the better the result is [70,71]. A model with high R 2 and low RMSE were considered as a good model [72].…”

Section: Verification Methods Of Npp Calculationmentioning

confidence: 99%

Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

et al. 2021

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“…This study discovered that the atmospheric correction and inverse K-M model increased the accuracy of TSM concentration derived from MERIS data. Furthermore, the R 2 (0.96) was higher in turbid water than in clear water, and the proposed method gave a better accuracy result compared to the TSM derived using empirical approach (R 2 =0.75) [14]. Diversities between TSM measured, and TSM derived from MERIS data were due to (i) clouds cover, (ii) increasing mixed pixel, (iii) reflectance effect from coral reefs and islands around the estuary, and (iv) the presence of the tidal flats.…”

Section: Spectral Shape Of Ltoa Meris Imagesmentioning

confidence: 88%

“…The remote sensing reflectance (Rrs) simulation was established from TSM concentration using a K-M model. Subsequently, the relationship between Rrs and TSM concentration [14,9] was computed based on equation 1:…”

Section: Establishing a Look-up Table (Lut) Of Toa Radiance Simulationsmentioning

confidence: 99%

“…Meanwhile, the study of water quality in Case-2 waters was disturbed due to heterogeneity, river discharge, tides, resuspension of bottom sediment [12], and failure of most of the atmospheric correction methods [13]. The coastal waters from satellite imagery consist of several materials mixed in one pixel, such as clear water, suspended sediments, phytoplankton, and others [14]. Therefore, spectral unmixing techniques are used to solve the mixed pixel problem [1].…”

Section: Introductionmentioning

confidence: 99%

“…However, spectral unmixing is the most widespread method to decompose a mixed pixel into endmembers and abundances [20,21] and has been successfully used for flood [22] and coral reef mapping [23]. Furthermore, it was also applied for retrieving suspended sediment concentration from MERIS data [24,25,14]. Meanwhile, its successful application for mapping water quality in subtropical waters has become the basis for consideration in this study, namely the methodology development for mapping TSM concentrations in equatorial tropical waters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The estimation of total suspended matter from satellite imagery of tropical coastal water Berau Estuary, Indonesia

Ambarwulan

Salama

Verhoef

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Water in coastal and estuary areas needs to be investigated as human activities is allegedly decrease their quality. This has led to optical remote sensing for mapping optical water quality using empirical and semi-analytical approaches. Therefore, this study aims to estimate the Total Suspended Matter (TSM) concentration from Medium Resolution Imaging Spectrometer (MERIS) data using a spectral unmixing approach combined with a radiative transfer model. This approach was implemented in the equatorial tropical coastal water, the Berau estuary, Indonesia, by generating a look-up table (LUT) of top-of-atmosphere radiance spectra through the coupled forward models, and the endmembers were selected from the LUT. The spectral unmixing algorithm was employed to the MERIS data for decomposing the image into haze and sediment components. The data were then transformed into images of a constant haze level corresponding to 50 km visibility, and the atmospheric correction was applied. Furthermore, the TMS concentration was retrieved using the inverse semi-analytical Kubelka-Munk model. The result gave overestimated TSM concentration values on clear waters. However, in turbid waters, a lower RMSE was obtained, and the coefficient of determination was higher than in clear waters.

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Measurement of suspended sediment concentration in open channel flows based on hyperspectral imagery from UAVs

Kwon

Shin

Seo

et al. 2022

Advances in Water Resources

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Linear spectral unmixing algorithm for modelling suspended sediment concentration of flash floods, upper Tekeze River, Ethiopia

Cited by 12 publications

References 33 publications

Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

Nonlinear Characteristics of NPP Based on Ensemble Empirical Mode Decomposition from 1982 to 2015—A Case Study of Six Coastal Provinces in Southeast China

The estimation of total suspended matter from satellite imagery of tropical coastal water Berau Estuary, Indonesia

Measurement of suspended sediment concentration in open channel flows based on hyperspectral imagery from UAVs

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