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WIMS Remote Sensing Needs and Applications Brainstorming Session – Group 1

Explore best practices for remote sensing projects, including utilizing ground-based data, managing statutory inflexibility, addressing data format accessibility, and filling data gaps. Overcome challenges and harness the potential of remote sensing in various applications.

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WIMS Remote Sensing Needs and Applications Brainstorming Session – Group 1

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  1. WIMS Remote Sensing Needs and Applications Brainstorming Session – Group 1 January 18, 2018

  2. Best Practices and Challenges for New Projects • Best Practices • Be aware of and take advantage of existing ground-based data and other commonly used datasets for a particular problem (e.g., CropID). RS and ground-based data are complementary, emphasize that RS does not take the place of ground or other data. • Ground-based is discrete in space, continuous in time; RS is discrete in time, continuous in space. Together, useful for: • Calibration of RS • Use RS to augment/extend existing ground-based data • Management practices evolve, communication among the states leads to best practice – e.g., stay engaged, look for champions and demonstration opportunities. • Challenges • Statutory inflexibility - use of certain data/reports is codified by the states. • Certain data must be legally/politically defensible.

  3. Formats, Accessibility, and Data Gaps • Formats • All agencies in Group 1 use GIS – having data in GIS ready geospatial format will facility use of data, e.g., GeoTIFFs, Shapefiles. Important to understand differences between raster/vector/point. RS is in raster format (generally). Time dimension important. • Accessibility • Tools for accessibility are important. USGS’s Earth Exchange was a game changer for Landsat. • Helpful to have canned processing tools. For example, complexity using Landsat isn’t with Landsat, but with processing the data. • Data Gaps • Knowing riparian consumptive use and water use for fields. • Nevada would value use of RS (land cover?) to help target resources for ground-truth. • Use of RS to complement groundwater modeling. • Landsat is widely used, but can be challenging to use under cloudy conditions and the repeat cycle can be an issue. Managers need data now! • Scale is important. Interested especially in field scale.

  4. Recommendations • Free data is great, but how do we find out what the data are? Develop a summary/catalogue of NASA water resources related datasets by variable. • Useful to explain how NASA datasets are different/complementary to spatial datasets currently in use by water managers (e.g., Ag data from USDA). • Playlist on YouTube explaining how these data can be applicable for water management. • Great to work with federal partners, but there are often strings attached (onerous reporting requirements). Reduce the strings! • Incorporate discussion about the NASA VALUABLES consortium into future dialogues

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