Mr. Shard Chander Profile

Shard Chander

at Space Applications Ctr

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SPIE Journal Paper | 30 April 2024

Examining the impact of hydro-geomorphological features in satellite river width-based discharge estimations

M. S. Adarsh, C. T. Dhanya, Shard Chander

JARS, Vol. 18, Issue 02, 024503, (April 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.024503

KEYWORDS: Satellites, Synthetic aperture radar, Landsat, Earth observing sensors, Rain, Satellite imaging, Clouds, Microwave radiation, Tunable filters, Optical surfaces

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We investigate the reliability of satellite river width (SRW) measurements to estimate the river discharge and its sensitivity to various hydro-geomorphological features. The study encompasses SRW extents at 141 in-situ hydrological observation stations, across seven tropical basins in India, with a mean annual discharge ranging from 2351 m3/s to less than 1 m3/s. Integrating optical (Sentinel-2, Landsat) and synthetic-aperture radar (SAR; Sentinel-1) data in the Google Earth Engine (GEE), 63,885 images are processed in the GEE to generate a dense time series of the SRW. Results demonstrate a good correlation (>0.50) between the SRW and in-situ discharge at 61 stations, primarily in the Godavari and Mahanadi basins. Furthermore, SRW-based rating curves exhibit reliable predictive capabilities at 44 stations, highlighting the potential to develop SRW rating curves in sparsely gauged basins. Investigations on the possible impact of different hydro-geomorphological features on the performance of the SRW to estimate the river discharge revealed optimal conditions in river reaches at lower elevations with substantial temporal variations in the discharge and associated variation in the river width along with a history of maximum water spread. Consequently, the Surface Water and Ocean Topography satellite’s river networks in the region are classified based on these findings, with 3567 out of 6132 river reaches identified as suitable for reliable SRW-based discharge estimation.

SPIE Journal Paper | 24 January 2017

Development of water level estimation algorithms using SARAL/Altika dataset and validation over the Ukai reservoir, India

Shard Chander, Debojyoti Ganguly

JARS, Vol. 11, Issue 01, 016012, (January 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.016012

KEYWORDS: Algorithm development, Global Positioning System, Satellites, Radar, Ka band, Water, Kinematics, Atmospheric modeling, Contamination, Data modeling

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Water level was estimated, using AltiKa radar altimeter onboard the SARAL satellite, over the Ukai reservoir using modified algorithms specifically for inland water bodies. The methodology was based on waveform classification, waveform retracking, and dedicated inland range corrections algorithms. The 40-Hz waveforms were classified based on linear discriminant analysis and Bayesian classifier. Waveforms were retracked using Brown, Ice-2, threshold, and offset center of gravity methods. Retracking algorithms were implemented on full waveform and subwaveforms (only one leading edge) for estimating the improvement in the retrieved range. European Centre for Medium-Range Weather Forecasts (ECMWF) operational, ECMWF re-analysis pressure fields, and global ionosphere maps were used to exactly estimate the range corrections. The microwave and optical images were used for estimating the extent of the water body and altimeter track location. Four global positioning system (GPS) field trips were conducted on same day as the SARAL pass using two dual frequency GPS. One GPS was mounted close to the dam in static mode and the other was used on a moving vehicle within the reservoir in Kinematic mode. In situ gauge dataset was provided by the Ukai dam authority for the time period January 1972 to March 2015. The altimeter retrieved water level results were then validated with the GPS survey and in situ gauge dataset. With good selection of virtual station (waveform classification, back scattering coefficient), Ice-2 retracker and subwaveform retracker both work better with an overall root-mean-square error <15 cm. The results support that the AltiKa dataset, due to a smaller foot-print and sharp trailing edge of the Ka-band waveform, can be utilized for more accurate water level information over inland water bodies.

Proceedings Article | 12 May 2016 Paper

Development of water level estimation algorithms using SARAL/Altika dataset and validation over the Ukai Reservoir, India

S. Chander, D. Ganguly

Proceedings Volume 9878, 98780M (2016) https://doi.org/10.1117/12.2227808

KEYWORDS: Global Positioning System, Algorithm development, Kinematics, Satellites, Radar, Ka band, Data processing, Scattering, Contamination, Knowledge management

Read Abstract +

Water level was retrieved, using AltiKa radar altimeter onboard the SARAL satellite, over Ukai reservoir using modified retrieval algorithms specifically for inland water bodies. The methodology was based on waveform classification, waveform retracking and dedicated inland range corrections algorithms. The 40 Hz waveforms were classified based on the linear discriminant analysis (LDA) and Bayesian classifier. Waveforms were retracked using Brown, Threshold, and Offset Centre of Gravity methods. Retracking algorithms were implemented on full waveform and sub-waveforms (only one leading edge) for estimating the improvement in the estimated range. ECMWF operational, ERA reanalysis pressure fields and global ionosphere maps were used to exactly estimate the range corrections. The microwave and optical images were used for estimating the extent of the water body and altimeter track location. Four GPS field trips were conducted, same day on the SARAL pass, using two Dual frequency GPS. One GPS was mounted close to Dam as static mode and the other was used on a moving vehicle within the reservoir in Kinematic mode. Tide gauge dataset was provided by the flood cell, Ukai dam authority for the time period 1972-2015. The altimeter retrieved water level results were then validated with the GPS survey and in-situ tide gauge dataset. With good selection of virtual station (waveform classification, back scattering coefficient), Ice-2 retracker and subwavefom retracker both works better with overall RMSE better than 15 cm. The results supports that AltiKa dataset, due to smaller foot-print and sharp trailing edge of Ka band waveform, can be utilized for more accurate water level information over inland water bodies.

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