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Correction published on 21 November 2016, see Remote Sens. 2016, 8(11), 960.

Open AccessArticle
Remote Sens. 2015, 7(12), 17113-17134; doi:10.3390/rs71215872

Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation

1
Deutsches Geodätisches Forschungsinstitut, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany
2
School of Environment & Natural Resources (SENR), Doon University, 248001 Dehradun, India
*
Author to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 9 October 2015 / Revised: 1 December 2015 / Accepted: 7 December 2015 / Published: 18 December 2015
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Abstract

To reduce hydrological uncertainties in the regular monitoring of poorly gauged lakes and reservoirs, multi-dimensional remote sensing data have emerged as an excellent alternative. In this paper, we propose three methods to delineate the volume of such equipotential water bodies through a combination of altimetry (1D), Landsat (2D) and bathymetry (2D) data, namely an altimetry-bathymetry-volume method (ABV), a Landsat-bathymetry-volume method (LBV) and an altimetry-Landsat-volume-variation method (ALVV). The first two data products are further merged by a Kalman-filter-based state space model (SSM) to obtain a combined estimate (CSSME) time series and near future prediction. To validate our methods, we tested them on the well-measured Lake Mead and further applied them on the poorly gauged Aral Sea, which has inaccurate bathymetry and very limited ground observation data. We updated the lake bathymetry of the Aral Sea, which was more than half a century old. The resultant remote sensing products have a very good long-term agreement among each other. The Lake Mead volume estimations are very highly coherent with the ground observations for all cases (R2 > 0.96 and NRMSE < 2.1%), except for the forecast (R2 = 0.75 and NRMSE = 3.7%). Due to lack of in situ data for the Aral Sea, the estimated volumes are compared, and the entire Aral Sea LBV and ABV have R2 = 0.91 and NRMSE = 5.5%, and the forecast compared to CSSME has R2 = 0.60 and NRMSE = 2.4%. View Full-Text
Keywords: remote sensing product; water storage; Landsat; altimetry; state space model (SSM); lakes and reservoirs; Lake Mead; Aral Sea remote sensing product; water storage; Landsat; altimetry; state space model (SSM); lakes and reservoirs; Lake Mead; Aral Sea
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Singh, A.; Kumar, U.; Seitz, F. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens. 2015, 7, 17113-17134.

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