Intro#
Overview#
Welcome to the third day of Computational Tools for Climate Science course! Today’s focus is on satellite remote sensing for climate applications. Various international agencies and organizations have been using environmental satellite to monitor our earth system since the late 1970s. With more than 40 years of long-term satellite data records, we can understand the change of different components of the earth system, including land, ocean, atmosphere. More importantly, the long-term remote sensing data can help us study the impact of climate change on natural and human systems, such as ecosystem’s response to climate and climate impact on agriculture systems.
During today’s curriculum, you will learn the fundamental concepts of satellite remote sensing, different sources of satellite remote sensing data for climate applications, how to access remote sensing datasets using python, and how to apply selected remote sensing datasets for climate applications. After today’s content, you should be familiar with basic concept of satellite remote sensing and basic computational tools to access and analyze satellite remote sensing data for climate applications.
Day Learning Objectives#
Understand the theory, technology, and availability of satellite remote sensing for climate applications.
Access and analyze satellite remote sensing products for climate applications.
Install and import feedback gadget#
Show code cell source
# @title Install and import feedback gadget
!pip3 install vibecheck datatops --quiet
from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
return DatatopsContentReviewContainer(
"", # No text prompt
notebook_section,
{
"url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
"name": "comptools_4clim",
"user_key": "l5jpxuee",
},
).render()
feedback_prefix = "W1D3_Intro"