Copula-based probabilistic spectral algorithms for high-frequent streamflow estimation

Sahoo, Debi Prasad; Sahoo, Bhabagrahi; Tiwari, Manoj Kumar

doi:10.1016/j.rse.2020.112092

Cited by 29 publications

(12 citation statements)

References 74 publications

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“…Following this approach, Tarpanelli et al (2013Tarpanelli et al ( , 2017Tarpanelli et al ( , 2019 extended and deepened the analysis demonstrating the big effectiveness to calculate the reflectance ratio between a land pixel, C, located near the river in an area free of surface water even during high flooding and a water pixel, M, located in the bank of the river in an area subject to liquid surface enlargement. The ratio (C/M) was employed as a sensitive and consistent measurement of surface water and has been tested for several satellite sensors, e.g., MODIS, MERIS, OLCI, Sentinel-2 (Tarpanelli et al 2020;Sahoo et al 2020;Hou et al 2020;Shi et al 2020).…”

Section: Optical and Sar Sensor Data As Tools For Supporting The Rive...mentioning

confidence: 99%

Water Resources in Africa: The Role of Earth Observation Data and Hydrodynamic Modeling to Derive River Discharge

Tarpanelli

Paris²,

Sichangi

et al. 2022

Surv Geophys

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For more than a century, river discharge has been measured indirectly through observations of water level and flow velocity, but recently the number of gauging stations worldwide has decreased and the situation is particularly serious in African countries that suffer more than others from discontinuous and incomplete monitoring. As one of the most vulnerable regions in the world to extreme weather events and global warming, African countries need adequate and reliable monitoring. Decades of available Earth Observations data represent a tool complementary to the hydro-monitoring network and, in recent decades, they have demonstrated their potential, especially for data-poor regions. In this paper, a review of methods for hydrological and hydraulic modeling and for estimating river discharge by the use of satellite data, specifically radar altimetry and optical sensors, is provided, with particular focus on their role in the climate changes monitoring. More emphasis is placed on their relevance on African basins highlighting limits and advantages.

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Section: Optical and Sar Sensor Data As Tools For Supporting The Rive...mentioning

confidence: 99%

Water Resources in Africa: The Role of Earth Observation Data and Hydrodynamic Modeling to Derive River Discharge

Tarpanelli

Paris²,

Sichangi

et al. 2022

Surv Geophys

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“…In previous studies, the reflectance ratio was correlated to river discharge and mean flow velocity [13,19,[21][22][23]. Here, to infer possible new relationships between reflectance ratio and hydraulic variables characterizing the river, the C/M time series are compared to the time series of water level, flow area, surface width, river discharge and flow velocity registered at each gauged station (Figure 1).…”

Section: Reflectance Ratio As a Proxy Of River Flow-related Variablesmentioning

confidence: 99%

“…This principle was already applied in several studies [16][17][18][19][20][21][22][23] and extended to the Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), ENVISAT MEdium Resolution Imaging Spectrometer (MERIS) and Landsat datasets. Specifically, in accordance with study by Tarpanelli et al [19], MODIS resulted in being the best performer, especially thanks to its daily temporal resolution and ease of data processing, two features not shared by MERIS and Landsat.…”

Section: Introductionmentioning

confidence: 99%

“…Shi et al [22] faced the estimation of river discharge at two study areas in the Murray-Darling basin with 60-90 m width analyzing the Harmonized Landsat and Sentinel-2 surface reflectance product [24], and obtained relative root mean square error, rRMSE, ranging from 19% and 53%. With the purpose to solve the problem of the cloud coverage for the tropical areas, Sahoo et al [23] proposed an integrated MODIS-Landsat fusion approach to estimate high-frequency discharge and tested the procedure in the Brahmani River Basin in eastern India. The method proposed to merge two datasets through a copula-based model to improve the temporal resolution of the single datasets and demonstrated the improved results versus the original approach of Tarpanelli et al [13].…”

Section: Introductionmentioning

confidence: 99%

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River Flow Monitoring by Sentinel-3 OLCI and MODIS: Comparison and Combination

Tarpanelli

Iodice²,

Brocca

et al. 2020

Remote Sensing

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The monitoring of rivers by satellite is an up-to-date subject in hydrological studies as confirmed by the interest of space agencies to finance specific missions that respond to the quantification of surface water flows. We address the problem by using multi-spectral sensors, in the near-infrared (NIR) band, correlating the reflectance ratio between a dry and a wet pixel extracted from a time series of images, the C/M ratio, with five river flow-related variables: water level, river discharge, flow area, mean flow velocity and surface width. The innovative aspect of this study is the use of the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3 satellites, compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) used in previous studies. Our results show that the C/M ratio from OLCI and MODIS is more correlated with the mean flow velocity than with other variables. To improve the number of observations, OLCI and MODIS products are combined into multi-mission time series. The integration provides good quality data at around daily resolution, appropriate for the analysis of the Po River investigated in this study. Finally, the combination of only MODIS products outperforms the other configurations with a frequency slightly lower (~1.8 days).

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“…Recent studies demonstrated the usage of the second approach where many algorithms for detecting and monitoring the different water quality parameters are used, e.g. (Saberioon et al 2020;Greeling et al 2020;Sahoo et al 2020;and Chawla et al 2020). These algorithms elucidate a sensitivity reduction to noisy data and atmospheric effects.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Monitoring Using Landsat 8 and Sentinel-2 Satellite Data (2014–2020) in Timsah Lake, Ismailia, Suez Canal Region (Egypt)

Seleem

Bafi

Karantzia

et al. 2022

J Indian Soc Remote Sens

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The present study aims to monitor the water quality in Timsah Lake by satellite data, the largest water body in Ismailia district, Egypt. To achieve this, multi-temporal space-born data of both Landsat-8 OLI- (L1 and L2) and Sentinel-2 (2A and 2B) were used during 2014–2020. The methodologies of Case 2 regional coast colour algorithm and aquatic reflectance were applied for monitoring the physical properties of different water characteristics encompassing chlorophyll-a, total suspended matter (TSM), and aquatic reflectance. Satellite data from dry and wet seasons (2014–2020) were selected to examine seasonal and temporal variation. Comparing water quality through the dry and wet seasons (2014–2020) is to avoid the difference in atmospheric conditions and detect seasonal variations. The present study results have shown that the Western Lagoon and its streams are considered the primary source of pollution. Specifically, lower values are observed during August 2015 for TSM (4–17 g m−3) and chlorophyll-a (2–11 g m−3), while the highest values of TSM (23–50 g m−3) appear during August 2018 and for chlorophyll-a during August 2016–2018 (20–40 g m−3). For the wet season for TSM, the lowest values are observed during 2016 (3–10 g m−3), and the maximum is noticed during 2019 (13–29 g m−3); for the chlorophyll-a, maximum values are observed during 2017 (10–23 g m−3).

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Copula-based probabilistic spectral algorithms for high-frequent streamflow estimation

Cited by 29 publications

References 74 publications

Water Resources in Africa: The Role of Earth Observation Data and Hydrodynamic Modeling to Derive River Discharge

Water Resources in Africa: The Role of Earth Observation Data and Hydrodynamic Modeling to Derive River Discharge

River Flow Monitoring by Sentinel-3 OLCI and MODIS: Comparison and Combination

Water Quality Monitoring Using Landsat 8 and Sentinel-2 Satellite Data (2014–2020) in Timsah Lake, Ismailia, Suez Canal Region (Egypt)

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