Automated Author Profile
Ramesh K Singh
Contractor to the United States Geological Survey
0000-0002-8164-3483

Current S-Index

3.9

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

2.0

Average Dataset Index per dataset

Total Datasets

Total datasets for this author

Average FAIR Score

46.1%

Average FAIR Score per dataset

Total Citations

Total citations to the author's datasets

Total Mentions

Total mentions of the author's datasets

S-Index Interpretation

The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.

What it means:

A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
It provides a single number to track your research data impact over time

Current S-Index: 3.9 (sum of 2 datasets Dataset Index scores)

More information here.

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Assessing the impact of irrigation curtailment using Landsat satellite data: A case study in the Upper Klamath Lake basin

The associated geotiff rasters represents the total actual evapotranspiration (ETa) from June through September for the years 2004, 2006, 2008-2010, and 2013-2016 for the entire Klamath Basin in southern Oregon. The ETa was created using Landsat imagery and the Operational Simplified Surface Energy Balance (SSEBop) model to estimate actual ET and the Python scripts to complete that process is also provided. Additionally, the June-September average (mean) ETa for the &amp;quot;base years&amp;quot; of 2004, 2006, 2008-2010 and ETa Anomaly (deviation from the base years average mean) for each year between 2013-2016 is provided. Text files of SSEBop daily actual ET along with actual ET from Ameriflux eddy co-variance flux tower sites is also provided including an R code to generate comparison charts.

Authors

Kagone, Stefanie ;
Schauer, Matthew P ;
Velpuri, Naga Manohar ;
Senay, Gabriel ;
Garcia, C. Amanda ;
Singh, Ramesh K ;
Friedrichs, MacKenzie O ;
Haynes, Jonathan V ;
Conlon, Terrence D

2 Citations0 Mentions46% FAIR1.1 Dataset Index

10.5066/p9bc38cl2020

Satellite-based Water Use Dynamics Using Historical Landsat Data (1984-2014) in the Southwestern United States

Historical (1984-2014) Landsat-based ET maps were generated for major irrigation districts: Palo Verde and eight other sub-basins in parts of middle and lower Central Valley, California. A total of 3,396 Landsat images were processed using the Operational Simplified Surface Energy balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study areas over the 31 years.