This document summarizes the use of remote sensing products for local water supply and use applications. It discusses how remote sensing can provide greater spatial and temporal coverage compared to traditional methods. Case studies are presented on using remote sensing to monitor urban irrigation, estimate snowpack, and calculate crop consumptive use through evapotranspiration modeling. Remote sensing allows coverage of large areas with periodic monitoring and provides data to inform water management decisions.

1. Use of Remote Sensing
Products for Local Water Supply
and Use Applications
Jason Polly, GIS Group Leader
September 2014

2. Introduction
Water related data-driven decisions require sufficient spatial and temporal
coverage for appropriate implementation. Available supply is directly related to use
applications.
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Consumptive Needs - Projected Water Use
Content and imagery courtesy of the report “SWSI 2010” by
Colorado Water Conservation Board (CWCB)

3. Traditional Methods of
Estimating Water Supply
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Technology used
Traditional methods
Municipal Meters
Gauging Stations
Weather Stations
Ground Water Monitoring
Wells
Irrigation Flow Meters

4. Remote Sensing (RS) Overview
Remote Sensing
The science and art of obtaining information about an object area or phenomenon through the analysis
of data acquired by a device that is not in contact wit the object, area or phenomenon under
investigation.
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5. Comparison –
Traditional and RS Techniques
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Criteria Traditional Remote Sensing
Spatial Coverage Single location Can cover large
scale applications
Temporal
Coverage
Limited by date of
instillation
Limited by sensor
repeat cycle
Precision Limited by data
logging
capabilities
Limited by sensor
resolution
Cost Maintenance
(often yearly)
Free for large
government
sensors and cost
based for
commercial
collection

6. Case Studies
• Urban Irrigation Monitoring (South Adams County Water and Sanitation
District, SACWSD)
• Snow Pack (Dolores Water Conservancy District, DWCD)
• Crop Consumptive Use (Colorado Water Conservation Board, CWCB.
Wyoming State Engineer's Office,. ect)
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7. Urban Irrigation Monitoring
Reusable Water
Water reuse is any arrangement that utilizes legally reusable
municipal return flows to increase municipal water supplies.
Return flows are water that returns to a river after being
treated at a wastewater treatment plant or to alluvial aquifers
via percolation.
Reuse can be accomplished in at least two ways:
1) return flows can be physically reused for non-potable and
potable purposes.
2) return flows can be reused under various substitution or
exchange arrangements.
To increase water supply through reuse, municipal return flows
must be legally reusable. Under Colorado water law, reusable
Water available to Front Range water utilities can generally
come from the following sources:
1. Water imported to the South Platte or its tributaries from
another river basin
2. Nontributary groundwater from Denver Basin aquifers
3. The historically consumed portion of water rights changed
from oneuse to another, such as from irrigation to
municipal use
4. Water diverted under a water right that has been decreed
to allow for reuse
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Content and imagery courtesy of the report “Filling the Gap” by
Western Resource Advocates (WRA), Trout Unlimited (TU), and
the Colorado Environmental
Coalition (CEC)

8. Urban Irrigation Monitoring
Lawn Irrigation Return Flow (LIRF)
Major irrigated areas were identified in residential,
commercial, industrial, and remaining urban zones District
(SACWSD) for the year 2013. High-resolution WorldView-2
satellite images, acquired in May-June produced the early
season image. The late season image covering the entire
district was generated using data acquired in July-
September. Using semi-automated remote sensing
classification techniques, the early and late season images
were used in combination to produce the 2013 irrigated
acreage estimation. Results were summarized by parcel,
and irrigated acreage estimates are reported for each
Return Flow Plot (RFP’s) serviced by the district.
High-resolution satellite imagery for the following 2013
approximate dates:
Mid May- early June, 2013 (Early Season Image)
July-September, 2013 (Late Season Image)
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Methodology Flow Chart

9. Urban Irrigation Monitoring
Lawn Irrigation Return Flow (LIRF)
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The technical specifications of the WorldView-2 products. The high-resolution
panchromatic band provides a very detailed spatial representation of urban features,
while the infrared band-4 capability of the multi-spectral bands allows for a better
discrimination of irrigated vegetation as compared to natural color imagery. In addition,
11-bit WorldView-2 imagery provides excellent radiometric resolution.
World View 2 – Sensor Technical Specifications

10. Urban Irrigation Monitoring
Lawn Irrigation Return Flow (LIRF)
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11. Urban Irrigation Monitoring
Lawn Irrigation Return Flow (LIRF)
The Normalized Difference Vegetation Index (NDVI) has been in
use for many years to measure and monitor plant growth (vigor),
vegetation cover, and biomass production from multi-spectral
satellite data (Jackson and Huete 1991, Jensen 1996, Lillesand
and Kiefer 2000). NDVI was derived from the difference between
the near-infrared region of the electromagnetic spectrum (e.i.,
WorldView-2 Band 4), and the visible red region (e.i., WorldView-
2 Band 3), using the following equation:
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NDVI – Irrigation Scaling

12. Urban Irrigation Monitoring
Lawn Irrigation Return Flow (LIRF)
Multi-temporal Image Analysis Approach
Since acceptable imagery was obtained for the early and late season periods, a multi-temporal image
analysis approach was possible.. This approach was adopted from the 2009 (Riverside, 2009) analysis and
greatly increases the overall accuracy on the analysis by capturing irrigated areas on two separate dates.
Demonstrating the approach:
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Early/Late Seasonal Image Comparisons

13. Urban Irrigation Monitoring
Lawn Irrigation Return Flow (LIRF)
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Land Cover Classification
Augmentation Plan, Case No. 2001CW258, all deep
percolation occurring under trees is fully consumed
and therefore does not return to the stream system.
This results in a lower percentage of return flows
being claimed when using the Cottonwood Curve
than if trees and shrubs were not present.
To produce a more detailed land cover
classification, Riverside used an unsupervised
classification technique to separate the ‘Trees and
Shrubs’ from the irrigated class previously obtained
from the NDVI analysis, as well as the ‘Water’ class
from the ‘Non-irrigated’ class.
The unsupervised classification was performed in
ERDAS Imagine using the ISODATA algorithm to
iteratively divide the WorldView2 data into clusters
or groups of pixels with similar spectral
characteristics.
The remote sensing analyst then assigned each
cluster to its corresponding land cover categories
(e.g., irrigated grass, trees and shrubs, non-irrigated,
and water).

14. Urban Irrigation Monitoring
Lawn Irrigation Return Flow (LIRF)
The parcels included for LIRF analysis and parcels not included for LIRF analysis were clipped by the RFP zones to
summarize the irrigated acreage. The parcels were then overlaid with the NDVI classification and the unsupervised
classification raster data. The irrigated areas were tabulated to obtain the irrigated acreage in the included parcels and
parcels not included at this time for the summary reports.
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Methodology Flow Chart
Summary Statistics by Return Flow Area

15. SnowPack (RS Methods)
NOAA National Weather Service's National Operational
Hydrologic Remote Sensing Center (NOHRSC) SNOw Data
Assimilation System (SNODAS)
• SNODAS is a modeling and data assimilation system
developed by the NOHRSC to provide the best possible
estimates of snow cover and associated variables to support
hydrologic modeling and analysis.
• The aim of SNODAS is to provide a physically consistent
framework to integrate snow data from satellite and airborne
platforms, and ground stations with model estimates of snow
cover (Carroll et al. 2001).
• The snow model has high spatial (1km) and temporal (1 hour)
resolutions and is run for the conterminous United States.
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Image/photo courtesy of Andrew P. Barrett and the National Snow and Ice Data
Center, University of Colorado, Boulder

16. SnowPack (RS Methods)
Process Historical SNODAS Data and Provide SWE Traces for
the 2013 Snowmelt Season
• Riverside processed the SNODAS Snow Water Equivalent
(SWE) grids for the period October 2003-July 2013 to generate
daily time series of basin-average SWE.
• The McPhee watershed was divided into six sub-basins to
show snowpack patterns in different parts of the watershed.
The SWE traces for the 2013 snowmelt season were updated
weekly from March-May 2013 to help characterize the 2013
snow season in real time. The historical SNODAS SWE time
series were also provided for context for the 2013 snow
conditions.
• The SNODAS SWE time series were plotted using a graph
similar to that commonly used for SNOTEL data This type of
graph is useful for assessing basic information about the
magnitude of the snow accumulation and the timing of the
snowmelt.
• In WY 2013, the snow accumulation for the McPhee
watershed was average to below-average. The snow melted
out relatively late, particularly for the modest snow
accumulations, due to cool spring temperatures.
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Image courtesy of Amy Volckens Riverside Technology, inc.

17. SnowPack (RS Methods)
Prepare Operational SNODAS maps for the 2013 Snowmelt
Season
• In addition to the basin-average time series,
Riverside prepared several maps showing
the SWE conditions being modeled by
SNODAS. The maps included labels with the
current snowpack volume in each sub-basin
as well as the seven-day change in the
snowpack volume.
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18. SnowPack (RS Methods)
Prepare Operational SNODAS maps for the 2013 Snowmelt
Season
SNODAS SWE on February 7, 2012 SNODAS SWE on February 7, 2013
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19. SnowPack (RS Methods)
Prepare Operational SNODAS maps for the 2013 Snowmelt
Season
SNODAS SWE on April 3, 2012 SNODAS SWE on April 2, 2013
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20. Remote Sensing-Based ET
Estimation
METRIC method:
Mapping EvapoTranspiration with high Resolution and
Internalized Calibration
(developed by Dr. Rick Allen, University of Idaho)
METRIC is a sort of “hybrid” between pure remotely-sensed energy balance and weather-based ET
• Advantages
• Can acquire data rapidly over large regions
• Do not require irrigation diversion and pumping well records
• Can detect use of subsurface supplies
• Do not require crop classification
• Can detect actual field conditions
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methods
Combines the strengths of energy balance from satellite and accuracy of ground-based reference ET
calculation:
 satellite-based energy balance provides the spatial information and distribution of
available energy and sensible heat fluxes over a large area (and does most of the “heavy
lifting”)
 reference ET calculation “anchors” the energy balance surface and provides “reality” to
the product.

21. • New Mexico
– Water consumption by invasive vegetation along the Rio Grande
• Colorado
– Conjunctive management of ground-water and surface water by State
Engineer along the South Platte
– Assessment of water shortage and salinity impacts along the Arkansas
River
• Nebraska
– Ground-water management and mitigation in the Ogallala Aquifer in
western Nebraska
– Testing against measured ET in central NE
• Wyoming
– Green River Basin crop consumptive use estimates
• Morocco
– Used in providing a complete water budget where no ground water
records exist.
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Remote Sensing-Based ET
Estimation

22. Crop Consumptive Use
(METRIC method)
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23. Remote Sensing-Based ET
Estimation
METRIC method:
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The satellite can not “see” ET therefore
ET is calculated as a “residual” of the energy balance: ET = Rn – G - H
R n
Net radiation
H
Heating of air ET
Evapotranspiration
G
Soil heat flux
Basic Truth:
Evaporation
consumes
Energy

24. Remote Sensing-Based ET
• Net Radiation (Rn), calculated using
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– Sun-earth geometry
– Spectral reflectance from the surface
– Thermal radiance from the surface
– Transmissivity of Atmosphere
– Ground Heat Flux (G), Calculated using
– Vegetation Amount
– Net radiation
– Thermal radiance
– Sensible Heat Flux (H), Calculated using
– Thermal radiance
– Wind speed
– Surface cover type and roughness
– Surface to air temperature difference, dT
Rn
H ET
G
underlined terms are
obtained from the
satellite data
Estimation

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Remote Sensing-Based ET
Estimation
METRIC Requirements:
•Satellite images with Thermal Band
High resolution (Landsat 5, 7 and now 8) is
needed for field scale maps
•Good quality weather data for best calibration

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Future Implications
Criteria Remote Sensing Perceived Future Implications
Spatial Coverage Can cover large scale
applications
Greater cloud free repeat cycles with
increased satellite constellations
Temporal Coverage Limited by sensor
repeat cycle
Greater cloud free repeat cycles with
increased satellite constellations
Precision Limited by sensor
resolution
Recent lift on U.S satellite resolution
restrictions .25m panchromatic.
Cost Free for large
government sensors
and cost based for
commercial collection
2007 Landsat archive open to public.

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Questions
Jason.Polly@Riverside.com
2950 E. Harmony Road Suite 390
Fort Collins, CO 80528