Development Of Et Sebal Model In Google Earth Engine

Step by step guide for croplands

What you'll learn

SEBAL

Evapotranspiration

Google Earth Engine

Remote Sensing

NDVI, SAVI, LAI

Surface Temperature

Incoming Longwave Radiation

Incoming Shortwave Radiation

Surface Emissivity

Momentum Roughness Length

Sensible Heat Flux

Soil Heat Flux

Outgoing longwave radiation

Net radiation flux

Requirements

Basic (or better Intermediate) knowledge of Google Earth Engine

Good Internet Connection

Description

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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Manual

Section 2: Fundamental theory

Lecture 3 Evapotranspiration

Lecture 4 Surface energy balance

Lecture 5 SEBAL model

Lecture 6 Net radiation flux (Rn)

Lecture 7 Soil heat flux(G)

Lecture 8 Sensible heat flux(H)

Lecture 9 Cold and Hot anchor pixels

Lecture 10 Air density

Section 3: Before starting the script

Lecture 11 Image Collections of GEE that will be used

Lecture 12 Necessary functions that will be used.Part 1

Lecture 13 Necessary functions that will be used.Part 2

Section 4: Net Radiation Flux (Rn)

Lecture 14 Starting the project in GEE

Lecture 15 Adding Landsat 8 Image collection

Lecture 16 Dataset subdivided by path and row

Lecture 17 Creating newDataset function

Lecture 18 Starting newDataset, defining parameters, defining geometry

Lecture 19 Computation of spectral radiance for each band

Lecture 20 Inverse squared relative earth sun distance

Lecture 21 Computation of reflectivity for each band

Lecture 22 Top of the atmopshere albedo

Lecture 23 Surface albedo

Lecture 24 Incoming shortwave radiation

Lecture 25 Outgoing longwave radiation - NDVI, SAVI, LAI

Lecture 26 Surface emissivity

Lecture 27 Surface temperature

Lecture 28 Outgoing longwave radiation

Lecture 29 Finishing newDataset

Lecture 30 Correcting the errors

Lecture 31 Image bounds for image path and row

Lecture 32 Chosing cold and hot pixels

Lecture 33 Importing the location of a cold pixel for incoming longwave radiation

Lecture 34 Incoming Longwave Radiation

Lecture 35 Creating newDataset2

Lecture 36 Computing Rn

Lecture 37 Adding a geometry

Lecture 38 Test Rn

Lecture 39 All images with computed bands and dates

Lecture 40 Export to drive and assets

Lecture 41 Discovering Rn image in ArcGIS or QGIS

Section 5: Momentum roughness length (z)

Lecture 42 Copernicus land cover classes areas

Lecture 43 Momentum roughness length for the study area

Lecture 44 Sampling

Lecture 45 Pixel count and export

Lecture 46 Correcting Zom -import of LAI

Lecture 47 Correcting zom for agricultural fields with LAI values

Section 6: Soil Heat Flux (G)

Lecture 48 Importing our hot and cold pixels

Lecture 49 Visualization parameters

Lecture 50 Defining bands to be sampled

Lecture 51 Add layers to corraborate where cold and hot pixels are

Lecture 52 Function for G and Rn ratio

Lecture 53 Soil heat flux (G) and Rn and G ratio computation

Lecture 54 Adding layers

Lecture 55 Sampling bands for hot and cold pixels

Lecture 56 Basic undertanding of variables for H and their values

Lecture 57 Momentum roughness length for a weather station

Lecture 58 Friction velocity at the weather station

Lecture 59 Wind velocity at 200 meters for a weather station

Lecture 60 Air density for hot and cold pixels

Lecture 61 Exporting data in CSV format

Section 7: Sensible Heat Flux (H)

Lecture 62 Theory of Iteration processs of H

Lecture 63 The weather data we need for Etr

Lecture 64 Adding ECMWF Climate Reanalysis collection

Lecture 65 Selecting necessary climate data

Lecture 66 u,v, surface net solar radiation and dewpoint temperature

Lecture 67 Conversion to Feature Collection from Image Collection

Lecture 68 Weather station elevation and export climate data

Lecture 69 Calculation of reference evapotranspiration

Lecture 70 Time issues around weather data and ETr

Lecture 71 Developing script for instantenous wind and ETr. Part 1

Lecture 72 Developing script for instantenous wind and ETr. Part 2

Lecture 73 Developing script for instantenous wind and ETr. Part 3

Lecture 74 Instantenous wind speed and ETr in QGIS plugin

Lecture 75 Working with getsebal plugin

Section 8: Finishing the SEBAL model

Lecture 76 Part 1

Lecture 77 Part 2

Lecture 78 Part 3

Lecture 79 Part 4

Section 9: Сhanging the developed script for other images of the year

Lecture 80 Re-computing the momentum roughness length corrected for January 13

Lecture 81 Re-computing the soil heat flux, values for hot and cold pixels and weather d

Lecture 82 Export weather parameters for a new image

Lecture 83 Computation ofr hourly ETr

Lecture 84 Instantenous wind speed and ETr for the new image

Lecture 85 Obtaining Correlation coefficients

Lecture 86 Calculating SEBAL for January 13

Remote sensing specialists,GIS specialists,Remote sensing master students,GIS master students,Hydrologists,Ecologists,Crop scientists,Ecologists,Environmentalists