Crop Mapping Using SAR & Optical Remote Sensing Training (PDF)

National Aeronautics and Space Administration Crop Mapping using Synthetic Aperture Radar (SAR) and Optical Remote Sensing April 4, 2023 NASA’s Applied Remote Sensing Training Program (ARSET) https://appliedscie...

National Aeronautics and Space Administration Crop Mapping using Synthetic Aperture Radar (SAR) and Optical Remote Sensing April 4, 2023 NASA’s Applied Remote Sensing Training Program (ARSET) https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset/about-arset Part of NASA’s Applied Sciences Capacity Building Program Water Resources Empowering the global community through online and in-person remote sensing training Topics for trainings include: – Water Resources – Air Quality – Disasters – Land Air Quality Disasters – Climate & Energy (recently added) Land NASA’s Applied Remote Sensing Training Program 2 NASA’s Applied Remote Sensing Training Program (ARSET) https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset ARSET’s goal is to increase the use of Earth science remote sensing and model data in decision-making through training for: – Professionals in the public and private sector – Environmental managers – Policy makers All ARSET materials are freely available to use and adapt for your curriculum. If you use the methods and data presented in ARSET trainings, please acknowledge the NASA Applied Remote Sensing Training (ARSET) program. NASA’s Applied Remote Sensing Training Program 3 Training Format Three 2.5-hour sessions including Training materials and recordings will presentations, demonstrations, and be available from: question and answer sessions https://appliedsciences.nasa.gov/join- mission/training/english/arset-crop- The same content will be presented at mapping-using-synthetic-aperture-radar- two different times each day. sar-and-optical-0 Session A will be presented in English. Session B will be presented in Spanish. – Session A: 10:00-12:30 EST (UTC-4) – Session B: 13:00-15:30 EST (UTC-4) NASA’s Applied Remote Sensing Training Program 4 Homework and Certificate Homework Assignment: – Answers must be submitted via Google Form – Due Date: April 25, 2023 A certificate of completion will be awarded to those who: – Attend all live webinars – Complete the homework assignment by the deadline (access from website) – You will receive a certificate approximately two months after the completion of the course from: [email protected] NASA’s Applied Remote Sensing Training Program 5  Prerequisites Fundamentals of Remote Agricultural Crop Classification Mapping Crops and their Sensing: with Synthetic Aperture Radar Biophysical Characteristics with and Optical Remote Sensing: Polarimetric SAR and Optical https://appliedsciences.nasa.gov/join- Remote Sensing: https://appliedsciences.nasa.gov/join- mission/training/english/fundamentals- https://appliedsciences.nasa.gov/join- mission/training/english/arset- mission/training/english/arset- remote-sensing agricultural-crop-classification- mapping-crops-and-their-biophysical- synthetic-aperture-radar-and characteristics NASA’s Applied Remote Sensing Training Program 6 Training Outline April 4, 2023 April 6, 2023 April 11, 2023 Crop Classification with Crop Classification with Monitoring Crop Growth Time Series of Time Series Optical and Through SAR-Derived Crop Polarimetric SAR Data Radar Data Structural Parameters NASA’s Applied Remote Sensing Training Program 7  Training Objectives After participating in this 3-part training, attendees will be able to: Explain how polarimetric parameters are used for crop condition assessment Demonstrate how to perform Sentinel-1 SAR preprocessing to derive quasi polarimetric parameters Perform a calibration of a SAR-based vegetation index to NDVI Monitor crop growth with multitemporal polarimetric SAR (PolSAR) data from Sentinel-1 Examine crop growth using a canopy structure dynamic model and time series of Sentinel- 1 imagery Classify crop type using a time series of radar and optical imagery (Sentinel-1 & Sentinel-2) NASA’s Applied Remote Sensing Training Program 8 National Aeronautics and Space Administration Crop Classification with Time Series of Polarimetric SAR Data Armando Marino April 4, 2023 Introduction Armando Marino The University of Stirling, Scotland NASA’s Applied Remote Sensing Training Program Credit: Armando Marino 10 Learning outcomes: By the end of this practical you will learn how to: Run Python code for machine learning of multitemporal PolSAR data Pre-process PolSAR data for using machine learning Format the data in feature vectors Run random forest and K-Means classifiers Evaluate the accuracy of your classifiers NASA’s Applied Remote Sensing Training Program 11 Before you start: This practical builds on skills from a previous ARSET training: Mapping Crops and their Biophysical Characteristics with Polarimetric SAR and Optical Remote Sensing https://appliedsciences.nasa.gov/join-mission/training/english/arset-mapping- crops-and-their-biophysical-characteristics If you are not very familiar with Pythion, you may want to go through the materials from the previous training before you attempt this training. In the training folder, you will find files with and without solutions. My suggestion is to try to solve the coding exercises on your own before you listen to the training or look at the solutions. NASA’s Applied Remote Sensing Training Program 12 Python “Python is a programming language that lets you work quickly and integrate systems more effectively.” https://www.python.org/ You can find many tutorials or books on the web. The one I use is the following: https://docs.python.org/3/tutorial/ NASA’s Applied Remote Sensing Training Program 13 Downloading/Installing: Anaconda My suggestion is to use the Anaconda installer, because it comes with most of the common libraries: https://www.anaconda.com/products/individual?modal=nucleus If you do not want to use Anaconda, please make sure you use Python 3.x version (3.6+ will be fine), but NOT 2.7, since some functions have changed! The 2.7 version will NOT run with the code I am sharing! NASA’s Applied Remote Sensing Training Program 14 Jupyter Notebook Anaconda will install Jupyter Notebook and you should see its icon in the Start menu (Windows OS). NASA’s Applied Remote Sensing Training Program 15 Jupyter Notebook Jupyter opens in a web browser, and you can upload scripts using the Upload button. NASA’s Applied Remote Sensing Training Program 16 Spyder Anaconda will install the Python editor Spyder and you should see the icon below. Spyder is a handy editor, and you may want to use it when you are scripting operational/automatic processing stacks. NASA’s Applied Remote Sensing Training Program 17 Data: Sentinel-1 ESA; Location: Angus, Scotland NASA’s Applied Remote Sensing Training Program 18 Data: Sentinel-1 ESA; Location: Angus, Scotland The crops are mostly cereals, potatoes, and rapeseed oil. NASA’s Applied Remote Sensing Training Program 19 Data: Sentinel-1 ESA; Location: Angus, Scotland Dundee Pauli RGB for 15th March 2019. Angus, Scotland. Multi-looked 7x28, geocoded. NASA’s Applied Remote Sensing Training Program 20 Data: Sentinel-1 ESA; Location: Angus, Scotland Pauli RGB for 15th March 2019. Angus, Scotland. Multi-looked 7x28, geocoded. This is the small area we will initially concentrate on in this practical. NASA’s Applied Remote Sensing Training Program 21 Questions? Please enter your questions in the Q&A box. We will answer them in the order they were received. We will post the Q&A to the training website following the conclusion of the webinar. https://earthobservatory.nasa.gov/images/6034/pothole-lakes-in-siberia NASA’s Applied Remote Sensing Training Program 22 Contacts Trainer: – Armando Marino: [email protected] Training Webpage: – https://appliedsciences.nasa.gov/join- mission/training/english/arset-crop-mapping-using- synthetic-aperture-radar-sar-and-optical-0 ARSET Website: – https://appliedsciences.nasa.gov/arset Check out our sister programs: Twitter: @NASAARSET NASA’s Applied Remote Sensing Training Program 23  Thank You! NASA’s Applied Remote Sensing Training Program 24