Spectral Reflectance Newsletter #3
Spectral Reflectance Newsletter #3
Weekly newsletter on Earth Observation
Level-2 News
ICEYE ESA archive collection now available to users [link]
ICEYE ESA archive collection is now available worldwide through ESA’s Third Party Missions programme.
Three modes are available for ICEYE Level 1 products:
- Spot (up to 1 m ground resolution),
- Strip and Scan, with two different processing levels: Single Look Complex (SLC) — Level 1A and Ground Range Detected (GRD) — Level 1B.
Greenland just experienced the warmest September in the ERA5 temperature record [link]
Surface air temperatures in Greenland were more than 8°C higher than 1991–2020 average, marking the warmest temperatures for September in the record. This increase was associated with the prevailing warmer-than-average winds.
Satellites detect methane plume in Nord Stream leak [link]
Read this latest post by ESA for a timeline of the Nord Stream 2 gas pipeline leak as it was monitored from space and how several different sensors and were used.
Competitions
Space4Energy Hackathon [link]
“Earth Observation Ideas for a Better Future. Transform the future of energy with satellite data.
The goal of the Hackathon is to provide solutions based on satellite data and services, as an answer to specific challenges defined by industry players from Austria.”
Registration deadline: October 15th, 2022
AgriFieldNet India Challenge [link]
In this challenge the goal is to classify crop types in agricultural fields across Northern India using multispectral observations from Sentinel-2 satellite.
Competition closes on 31 October 2022
Developer’s Orbit
“What does ‘ML Ready Data’ mean to you in an EO context?”
Follow this Twitter thread for some great answers and links to more resources. One amazing blog post hidden in that thread is this one, written by Matthew Hanson and E84 Team.
I did keep one excerpt from that post that describes the complexity of Analysis-Ready Data (ARD);
“The predominant view of ARD is that it meets some sort of minimum processing “level” (e.g., Level 2 surface reflectance), however as has been previously noted this depends on the workflow the data will be used in. Some workflows may be flexible in what they need, while others may require specific processing steps or have a maximum tolerance of uncertainty in geometric or measurement accuracy. It is the workflows that should determine what processing is required to produce a valid output.”
This emphasizes that ARD means different things to different users and workflows and making assumptions while creating an ARD product risks breaking end-users’ requirements.
Learning
Cloud-Based Remote Sensing with Google Earth Engine: Fundamentals and Applications [link]
This book is the collective work of more than 100 individuals who contributed more than 10,000 lines of code and 55 chapters. This free resource covers topics ranging from the basics of programming in JavaScript and reading images to advanced image processing and developing complete applications that involve creating mosaics and data from multiple sensors.
awesome-gee-community-catalog
And as long as we are talking about Google Earth Engine, read this post about the awesome-gee-community-catalog. The catalog consists of community-sourced geospatial datasets made available for use by the larger Google Earth Engine community and shared publicly as Earth Engine assets.
Aerosols in Air: Earth science data cubes for determining potential factors engendering aerosols [link]
We are reading this post written by Rabia Munsaf Khan for an introduction to aerosols, their contribution to climate change and how we monitor them using ground sensors and satellites.