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Correction

Correction: Singh, A., et al. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens. 2015, 7, 17113–17134

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.
Remote Sens. 2016, 8(11), 960; https://doi.org/10.3390/rs8110960
Submission received: 9 October 2015 / Accepted: 7 December 2015 / Published: 21 November 2016
The authors wish to make the following corrections to their paper [1]. There are two mistakes in this article. A change is necessary in Equation (1) (p. 17120). Due to a typing error, please replace:
A L V V = t = 1 n 1 3 × ( H t H t 1 ) × ( A t + A t + 1 + ( A t × A t + 1 ) )
with
A L V V = t = 1 n 1 3 × ( H t H t 1 ) × ( A t + A t 1 + ( A t × A t 1 ) )
Due to a mislabeling, a change is necessary in Figure 10 (p. 17126); please replace:
Figure 10. Lake Mead SSM analysis. (Top left) The combined SSM estimate (CSSME) (magenta line) and the forecast (green line) for 2013 and 2014; (Bottom left) difference between CSSME and in situ observations; (Top right) estimated seasonal component; (Bottom right) estimated trend component. The dashed cyan lines indicate the upper and lower 95% confidence limit.
Figure 10. Lake Mead SSM analysis. (Top left) The combined SSM estimate (CSSME) (magenta line) and the forecast (green line) for 2013 and 2014; (Bottom left) difference between CSSME and in situ observations; (Top right) estimated seasonal component; (Bottom right) estimated trend component. The dashed cyan lines indicate the upper and lower 95% confidence limit.
Remotesensing 08 00960 g001
with
Figure 10. Lake Mead SSM analysis. (Top left) The combined SSM estimate (CSSME) (black line) and the forecast (green line) for 2013 and 2014; (Bottom left) difference between CSSME and in situ observations; (Top right) estimated seasonal component; (Bottom right) estimated trend component. The dashed cyan lines indicate the upper and lower 95% confidence limit.
Figure 10. Lake Mead SSM analysis. (Top left) The combined SSM estimate (CSSME) (black line) and the forecast (green line) for 2013 and 2014; (Bottom left) difference between CSSME and in situ observations; (Top right) estimated seasonal component; (Bottom right) estimated trend component. The dashed cyan lines indicate the upper and lower 95% confidence limit.
Remotesensing 08 00960 g002
These changes have no material impact on the conclusions of our paper. We apologize to our readers. The manuscript will be updated and the original will remain online on the article webpage.

Reference

  1. 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. [Google Scholar] [CrossRef]

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MDPI and ACS Style

Singh, A.; Kumar, U.; Seitz, F. Correction: Singh, A., et al. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens. 2015, 7, 17113–17134. Remote Sens. 2016, 8, 960. https://doi.org/10.3390/rs8110960

AMA Style

Singh A, Kumar U, Seitz F. Correction: Singh, A., et al. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens. 2015, 7, 17113–17134. Remote Sensing. 2016; 8(11):960. https://doi.org/10.3390/rs8110960

Chicago/Turabian Style

Singh, Alka, Ujjwal Kumar, and Florian Seitz. 2016. "Correction: Singh, A., et al. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens. 2015, 7, 17113–17134" Remote Sensing 8, no. 11: 960. https://doi.org/10.3390/rs8110960

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