Behavior of completely automated evapotranspiration estimation (EEFlux)

Atiqullah Atif Author

04/04/2021 Added

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The Earth Engine Evapotranspiration Flux (EEFlux) application was designed and developed on the google earth Engine platform and utilizes Landsat imagery archive. We can request ET maps on EEFlux in a matter of seconds.

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[00:00:00.820]Welcome to the UNL students research days 2021.
[00:00:04.199]My name is Atiqullah Atif and I am a
[00:00:06.799]graduate student doing my master's in
[00:00:08.940]water resources engineering
[00:00:10.431]Department of civil and environmental
[00:00:12.521]Engineering at UNL.
[00:00:13.699]In the next few minutes, I will walk you
[00:00:15.800]through this poster presentation.
[00:00:17.760]The topic of this research poster is
[00:00:20.727]Behavior of completely automated
[00:00:23.173]evapotranspiration estimation or in
[00:00:27.033]short we say EEFlux.
[00:00:28.915]So, let's get started and I'll zoom in
[00:00:32.404]to the Introduction.
[00:00:35.556]Water is lost to the atmosphere from soil,
[00:00:37.916]ponds and vegetation in the form of water vapor.
[00:00:41.000]This natural phenomenon is called
[00:00:43.151]Evapotranspiration or ET. The importance
[00:00:46.171]of ET is such that it is the second
[00:00:48.375]largest term in the water balance equation
[00:00:52.343]ET is responsible for 75-90 percent
[00:00:55.950]consumption of the annual precipitation
[00:00:58.623]in the U.S. It also dictates the
[00:01:01.475]recharge and discharge of aquifers.
[00:01:04.220]We have several techniques for estimating
[00:01:06.640]ET, which are time consuming and have
[00:01:09.168]their own limitations. The Earth Engine
[00:01:12.119]Evapotranspiration Flux or we call it
[00:01:16.119]EEFlux application was designed and
[00:01:19.495]developed on the Google Earth Engine
[00:01:22.001]platform and utilizes Landsat
[00:01:24.031]imagery archive.
[00:01:25.988]We can request ET maps on EEFlux in a matter of seconds.
[00:01:31.020]Now, let's walk through the
[00:01:32.923]background work on ET estimation.
[00:01:36.185]There are accurate techniques to estimate ET
[00:01:39.174]but they cannot be used on regional scale due to the high number and
[00:01:43.724]operational cost requirements on
[00:01:45.658]measurement sites.
[00:01:46.888]To overcome this obstacle, surface energy balance
[00:01:50.416]technique is used in satellite-based image
[00:01:52.823]processing models like Mapping
[00:01:55.121]Evapotranspiration at high Resolution
[00:01:57.807]with Internalized Calibration or in
[00:02:00.338]short we call it METRIC to calculate ET as a residual of
[00:02:04.746]surface energy balance.
[00:02:06.406]However, despite being accurate,
[00:02:09.634]METRIC model requires skilled personnel to calibrate
[00:02:13.634]and run for each Landsat scene and image date,
[00:02:16.891]and this proves it to be time consuming.
[00:02:19.587]To minimize human interference,
[00:02:22.222]automated calibration algorithms were designed for
[00:02:26.425]METRIC model. The automated application uses
[00:02:30.709]Google Earth Engine as a platform and is referred to as
[00:02:34.821]Earth Engine Evapotranspiration Flux or we
[00:02:38.150]can say EEFlux and eeMETRIC.
[00:02:40.935]Now, we talk about the objectives.
[00:02:43.043]The objectives of this research study are to:
[00:02:47.043]Observe the behavior of eeMETRIC.
[00:02:50.612]Compare eeMETRIC results with the manual METRIC
[00:02:53.992]results to asses the utility and accuracy of eeMETRIC.
[00:02:59.079]And
[00:03:02.857]Since, eeMETRIC is based on manual METRIC,
[00:03:06.058]we will examine the difference of their products to confirm
[00:03:09.804]its functioning on Earth Engine.
[00:03:12.643]Talking about materials, we will need the following
[00:03:15.883]materials for this study
[00:03:17.358]number one, daily, monthly and seasonal ET
[00:03:20.883]maps will be needed form both models.
[00:03:25.563]Number two ArcGIS application for spatial analysis.
[00:03:30.091]eeMETRIC and EEFLux
[00:03:32.258]a web-based interface for requesting ET maps
[00:03:35.531]within seconds. And Microsoft EXCEL to do
[00:03:39.493]statistical analysis.
[00:03:41.212]Here is figure 1 showing the material we will need for this study.
[00:03:46.954]Now what is the study area?
[00:03:48.876]The study is performed on the following locations
[00:03:51.961]highlighted in Figure 2 because we had manual
[00:03:55.505]METRIC data already available for these locations.
[00:03:59.289]Here we can see the locations
[00:04:02.685]highlighted with these red rectangles.
[00:04:06.279]Here are the location names and Landsat path/row specifications.
[00:04:11.446]Here are the remaining two locations of this study area
[00:04:19.525]Now we come to the Methodology and this includes
[00:04:22.943]the following steps: First is obtaining ET maps
[00:04:26.379]from both models. Second we add the maps to
[00:04:29.785]ArcMap file as raster layers.
[00:04:33.053]We might need reprojections and or raster calculation.
[00:04:37.577]Then we choose sampling fields with the help of NLCD Land Use/Land Cover layer.
[00:04:44.276]Then we extract values for each sampling location
[00:04:48.276]and summarize them into a table.
[00:04:50.621]Then export the table to an excel database.
[00:04:54.675]And finally we create scatterplot to see
[00:04:58.455]correlation, slope of regression line,
[00:05:01.554]and calculate index agreement.
[00:05:03.960]Talking about results, so far, the study is only
[00:05:07.060]performed on Snake River Plain data.
[00:05:09.435]And it will be performed for the remaining locations
[00:05:13.435]mentioned in study area. Here are some examples products.
[00:05:17.200]The left one is the daily ET map from
[00:05:19.869]manual MATRIC application for snake river
[00:05:22.733]plain and the right one is the daily ET map from eeMETRIC.
[00:05:28.796]Here is the scatterplot you can see.
[00:05:31.836]it's showing strong correlation,
[00:05:33.867]the R square value is close to one.
[00:05:36.487]And the Index of agreement is also close to one.
[00:05:40.765]Now here we discuss some the primary results from this study.
[00:05:46.353]the average values for R square and Index of
[00:05:49.997]Agreement for Snake River Rlain data for 12 overpass
[00:05:54.494]dates are respectively 0.949 and 0.941.
[00:06:01.574]These results can further improve after cloud masking is applied.
[00:06:06.817]Also, the study of the time-based
[00:06:08.744]interpolation methods will provide further
[00:06:11.397]insight about the behavior of eeMETRIC.
[00:06:14.774]Now we come to the conclusion.
[00:06:17.156]The so far results show that eeMETRIC gives ET
[00:06:20.958]estimations comparable with manual METRIC.
[00:06:23.523]We expect similar results from the reaming 28 Landsat
[00:06:28.105]image sites, which will be studied later.
[00:06:30.723]And larger differences are noted for non-agriculture land use.
[00:06:35.497]And the reason could be that eeMATRIC
[00:06:38.315]struggles to account for background
[00:06:40.452]evaporation at the hot pixel calibration end point
[00:06:44.543]or it could be due to a bias introduced during the
[00:06:47.708]application of the Evaporative Fraction to extrapolate
[00:06:51.278]instantaneous ETrf to daily ETrf.
[00:06:55.717]At the end we acknowledge and appreciate the support and
[00:06:59.965]funding by google earth engine to initiate EEFlux and eeMETRIC.
[00:07:04.372]Also, thanks to the USGS, NASA and Windward Fund to
[00:07:08.687]support Landsat team. And last but not the least, a special
[00:07:13.514]thanks to IIE and Fulbright for funding this study.
[00:07:19.825]And with this we come to the end of this Poster presentation.
[00:07:25.422]Thank you all for your attention.

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Behavior of completely automated evapotranspiration estimation (EEFlux)

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