Capability assessment of Sentinel-1 data for estimation of snow hydrological potential in the Khanabad watershed in the Hindu Kush Himalayas of Afghanistan

Mahmoodzada, Abdul Basir; Varade, Divyesh; Shimada, Sawahiko; Rezazada, Farid Ahmad; Jawher, Abdul Nasir; Toghyan, Mohammadi

doi:10.1016/j.rsase.2022.100758

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…The headwaters are primarily monitored at Pyanj River and at other streams in the headwaters of the Vakhsh River (Figure 2). The Pyanj and Vakhsh Rivers receive water from snowmelt from most of the snow cover area in the watershed [36,37]. A maximum river discharge is typically observed in the spring and summer, contributed from the snowmelt, and, in fall, the river discharge reduces and comprises contributions from the glacier melts.…”

Section: Study Areamentioning

confidence: 99%

Quantification of Amu River Riverbank Erosion in Balkh Province of Afghanistan during 2004–2020

Mahmoodzada,

Varade,

Shimada

et al. 2023

Land

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In this study, we propose quantifying the Amu River riverbank erosion with the modelled river discharge in Kaldar District, Balkh Province of Afghanistan from 2004 to 2020. We propose a framework synergizing multi-source information for modelling the erosion area based on three components: (1) river discharge, (2) river width, and (3) erosion area. The total river discharge for the watershed shared by Afghanistan and Tajikistan was modelled using hydrological parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data through multivariate linear regression with ground station data. The river width was determined manually using the Normalized Difference Water Index (NDWI) derived from Landsat data. The riverbank erosion area was derived from the digital shoreline analysis using the NDWI. The digital shoreline analysis showed that, between 2008 and 2020, the average riverbank erosion area in Kaldar District is about 5.4 km2 per year, and, overall, 86.3 km2 during 2004–2020 due to flood events. The significantly higher land loss events occurred at 10 km2 bank erosion during the years 2008–2009 and 2015–2016, and 19 km2 peak erosion occurred during 2011–2012. A linear relation between the erosion area with respect to the discharge intensity and the specific stream power was observed with an R2 of 0.84 and RMSE of 1.761 for both.

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Section: Study Areamentioning

confidence: 99%

Quantification of Amu River Riverbank Erosion in Balkh Province of Afghanistan during 2004–2020

Mahmoodzada,

Varade,

Shimada

et al. 2023

Land

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“…The Polarimetric Kalman Filter (PKF) is an innovation in signal processing and remote sensing that opens up an era of complexity in data analysis and precision improvement in different scientific fields and engineering [1][2][3][4][5][6][7][8][9][10][11]. The PKF, an extension of the conventional Kalman Filter (KF), includes polarimetric data into the filtering process offering a more detailed approach to understanding and dealing with the complexities present in multi-dimensional datasets.…”

Section: Introductionmentioning

confidence: 99%

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

Obaideen,

AlShabi,

Gadsden

et al. 2024

Polarization: Measurement, Analysis, and Remote Sensing XVI

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This paper presents a comprehensive study on the evolution, applications, and impact of the Polarimetric Kalman Filter (PKF) in the fields of signal processing and remote sensing. By employing a methodological framework that integrates literature collection, screening and selection, bibliometric analysis, and synthesis, we aim to elucidate the PKF's contributions and potential research trajectories within its applications. Our findings highlight the PKF's significant role in enhancing data assimilation, improving predictive accuracy, and refining measurement techniques across various domains, particularly in meteorological research and environmental monitoring. The analysis reveals the interdisciplinary nature of PKF applications and its capability to merge theoretical advancements with practical implementations, underlining the importance of sophisticated signal processing algorithms in interpreting complex environmental data. The study also identifies emerging trends, such as the integration of PKF with machine learning and artificial intelligence, indicating future directions for research that promise to push the boundaries of current methodologies and applications. This paper underscores the PKF's pivotal role in advancing signal processing and remote sensing technologies, offering insights into its continuing development and the expanding scope of its applications.

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Remote sensing of snow cover dynamics and climate implications in the Indus, Ganga, and Brahmaputra river basins

Dixit,

Goswami,

Jain

et al. 2024

Clim Dyn

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Capability assessment of Sentinel-1 data for estimation of snow hydrological potential in the Khanabad watershed in the Hindu Kush Himalayas of Afghanistan

Cited by 6 publications

References 39 publications

Quantification of Amu River Riverbank Erosion in Balkh Province of Afghanistan during 2004–2020

Quantification of Amu River Riverbank Erosion in Balkh Province of Afghanistan during 2004–2020

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

Remote sensing of snow cover dynamics and climate implications in the Indus, Ganga, and Brahmaputra river basins

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