Behavior of completely automated evapotranspiration estimation (EEFlux)

Atiqullah Atif Author

04/04/2021 Added

65 Plays

Description

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.

Searchable Transcript

Search:

[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.

The screen size you are trying to search captions on is too small!

You can always jump over to MediaHub and check it out there.

Comments

0 Comments

Behavior of completely automated evapotranspiration estimation (EEFlux)

Description

Searchable Transcript

Comments icon comment

Related Channels

Comments