Development of ET SEBAL model in Google Earth Engine
- Descrição
- Currículo
- FAQ
- Revisões
In this course, you will master a step-by-step guide to developing a script in Google Earth Engine for the most famous Evapotranspiration model – Surface Energy Balance Algorithm for Land (SEBAL) for agricultural areas. Before starting a course, please read the requirements for the course – you need to have a Google Earth Engine profile (free to open), and a basic or better intermediate level of scripting in GEE or JavaScript.
As a research study area an agricultural field that is located in Dubai Emirate, the UAE was taken. You can apply this course to your study area by making minor changes. It is good if you also have some knowledge/ experience of evapotranspiration.
The course is divided into a theoretical part and a practical part, the latter of which constitutes almost 90 % of the course. Each practical part of the course contains an attached script in txt format.
After finishing the course, besides developing the SEBAL model, you can apply different parts of the course in your other projects, that involve remote sensing.
The course also contains two QGIS plugins ( to calculate instantaneous reference evapotranspiration and correlation coefficients) developed deliberately for the course which is free to download.
Get ready to boost your knowledge in remote sensing and Google Earth Engine!
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14
Starting the project in GEE -
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Adding Landsat 8 Image collection
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Dataset subdivided by path and row
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Creating newDataset function
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Starting newDataset, defining parameters, defining geometry
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Computation of spectral radiance for each band
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20
Inverse squared relative earth sun distance
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21
Computation of reflectivity for each band
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22
Top of the atmopshere albedo
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23
Surface albedo
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24
Incoming shortwave radiation
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Outgoing longwave radiation - NDVI, SAVI, LAI
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26
Surface emissivity
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27
Surface temperature
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28
Outgoing longwave radiation
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29
Finishing newDataset
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30
Correcting the errors
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31
Image bounds for image path and row
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32
Chosing cold and hot pixels
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33
Importing the location of a cold pixel for incoming longwave radiation
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34
Incoming Longwave Radiation
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35
Creating newDataset2
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36
Computing Rn
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Adding a geometry
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Test Rn
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All images with computed bands and dates
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Export to drive and assets
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Discovering Rn image in ArcGIS or QGIS
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48
Importing our hot and cold pixels
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49
Visualization parameters
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50
Defining bands to be sampled
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51
Add layers to corraborate where cold and hot pixels are
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52
Function for G and Rn ratio
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53
Soil heat flux (G) and Rn and G ratio computation
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54
Adding layers
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55
Sampling bands for hot and cold pixels
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56
Basic undertanding of variables for H and their values
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57
Momentum roughness length for a weather station
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58
Friction velocity at the weather station
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59
Wind velocity at 200 meters for a weather station
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60
Air density for hot and cold pixels
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61
Exporting data in CSV format
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62
Theory of Iteration processs of H
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63
The weather data we need for Etr
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64
Adding ECMWF Climate Reanalysis collection
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65
Selecting necessary climate data
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66
u,v, surface net solar radiation and dewpoint temperature
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67
Conversion to Feature Collection from Image Collection
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68
Weather station elevation and export climate data
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69
Calculation of reference evapotranspiration
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70
Time issues around weather data and ETr
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71
Developing script for instantenous wind and ETr. Part 1
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72
Developing script for instantenous wind and ETr. Part 2
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73
Developing script for instantenous wind and ETr. Part 3
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74
Instantenous wind speed and ETr in QGIS plugin
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75
Working with GetSebal plugin
