{ "cells": [ { "cell_type": "markdown", "id": "745e28aa-0779-4263-ae40-d88b5694bcb5", "metadata": {}, "source": [ "# Exercise: GeeMap, GEE, and Sentinel-2 data\n", "\n", "This lesson is modified from [An Introduction to Cloud-Based Geospatial Analysis with Earth Engine and Geemap](https://geemap.org/workshops/AGU_2023/). Codes, annotation, and formatting are produced wuth assistant from Jupter AI using ChatGPT 3.5 Turbo and ChatGPT4o.\n", "\n", "[](https://mybinder.org/v2/gh/aselshall/eds/HEAD)\n", "\n", "-----" ] }, { "cell_type": "markdown", "id": "b79f70ce-587b-4ac8-b0ea-8ef09784e058", "metadata": {}, "source": [ "## 1. Environment Setup" ] }, { "cell_type": "markdown", "id": "cea4b299-de29-4755-a984-0b0124ab73e2", "metadata": {}, "source": [ "You need to install [GeeMap](https://geemap.org/) library. Installing geemap library can take more than 5 minutes depending on the specs of your machine." ] }, { "cell_type": "code", "execution_count": 1, "id": "d95aa91b-65a3-4636-abb8-3b6010981f63", "metadata": {}, "outputs": [], "source": [ "#pip install geemap" ] }, { "cell_type": "markdown", "id": "365ce7be-3da1-4eeb-9ce8-d8135192942f", "metadata": {}, "source": [ "You need to [register](https://code.earthengine.google.com/register) to [Google Earth Engine](https://earthengine.google.com/) and follow the instructions [here]( https://docs.google.com/document/d/1ZGSmrNm6_baqd8CHt33kIBWOlvkh-HLr46bODgJN1h0/edit?usp=sharing) to create a Cloud Project. " ] }, { "cell_type": "markdown", "id": "6d6028df-d6ec-4ad5-bbe0-a192f2fb5556", "metadata": {}, "source": [ "
Note
\n", "If you have an Earth Engine account with your university email but have not used the Earth Engine Python API, creating a new account with your personal Gmail is a good idea. The API needs a Google Cloud Project, which universities may restrict users from creating.\n", "Note
\n", "Bofore starting this exercise you need to set up Google Earth Engine (GEE) and important geemap as shown above.\n", "Note
\n", "COPERNICUS/S2_HARMONIZED provides harmonized Sentinel-2 surface reflectance imagery. This collection combines data from Sentinel-2A and Sentinel-2B satellites and ensures consistent data quality across both satellites. It applies atmospheric correction to convert the top-of-atmosphere (TOA) reflectance values to surface reflectance values.\n", "Note
\n", "Sometime the below function will take overever to run. In that case restart kernel and clear all outputs. This will fix the problem.\n", "