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Introduction

Evolution of Remote Sensing Research​

Research in remote sensing has undergone profound changes over the past several decades. Initially, the field was dominated by a few governments possessing the technological and financial resources to deploy satellites and process the imagery. This early period was primarily focused on military and intelligence applications, with civilian use of satellite data being a secondary consideration.

The establishment of the National Aeronautics and Space Administration (NASA) in the United States in 1958, along with the foundational steps toward creating the European Space Agency (ESA), marked a pivotal shift toward supporting civilian space programs alongside space and aeronautics research. Despite these advancements, access to satellite data remained a significant challenge for the research community. Although data was increasingly available, it was cumbersome and expensive to obtain. Researchers faced a daunting task: even after identifying the necessary data, they had to navigate the complex process of downloading it onto mainframe computers. These machines were among the few with enough storage and processing power to handle the extensive pre-processing required—including ortho-rectification and atmospheric corrections—before any analysis could begin.

Today, the landscape of remote sensing is vastly different. The advent of commercial satellite companies and advancements in cloud computing have democratized access to high-quality satellite data. Researchers can now access vast datasets quickly and affordably, leveraging powerful cloud-based platforms to process and analyze imagery with unprecedented efficiency. This evolution has expanded the applications of remote sensing beyond military and intelligence to include environmental monitoring, urban planning, disaster response, and more, empowering a broader community of users to harness the power of satellite imagery for diverse and impactful purposes.

Introduction to Google Earth Engine (GEE)​

With the advent of Google Earth Engine (GEE) in 2010, the landscape of remote sensing research underwent another significant transformation. Google's initiative to make the world's information universally accessible and useful found a remarkable application in organizing and simplifying geospatial data, presenting an end-to-end solution that addressed many historical challenges faced by remote sensing researchers.

Key Features and Capabilities:

  • Vast Data Repository: Google Earth Engine has amassed petabytes of imagery from both public and private sources, including major platforms like Landsat, MODIS, and Sentinel. This extensive collection simplifies the search for and access to geospatial data.
  • Powerful Processing Capabilities: Leveraging Google's substantial computing power, GEE enables users to process data and perform sophisticated analyses, including advanced machine learning with TensorFlow Processing Units (TPUs). This feature democratizes access to powerful computational resources for researchers around the globe.
  • Pre-built and Custom Algorithms: GEE not only geo-rectifies imagery but also provides a suite of pre-built algorithms to facilitate various analyses. For researchers looking to create bespoke solutions, GEE supports both JavaScript and Python, allowing for the development of custom algorithms and the utilization of preferred data processing and visualization tools.
  • Data Ownership and Flexibility: Users can import their own data for analysis within GEE, enhancing the platform's utility. Importantly, Google Earth Engine allows users to retain full ownership of the analyses and algorithms they develop, promoting fair and open use.

Implications for Remote Sensing Research:

Google Earth Engine has emerged as an invaluable resource for remote sensing researchers. It provides a versatile toolset that supports a wide range of applications, from environmental monitoring and resource management to climate change research. By simplifying data access, processing, and analysis, GEE enables researchers to focus more on insights and less on the intricacies of data management and computational challenges.

Structure of Site​

This site is structured to facilitate a comprehensive learning journey through remote sensing, organized into three main sections.

For practical aspects such as software setup, understanding data structures, and other preparatory knowledge, the Getting Started section serves as a vital resource. It provides the essential foundation that users need to begin their exploration into remote sensing.

The Tutorials section forms the bulk of the material, introducing both foundational and advanced remote sensing concepts. This section is designed to equip users with the knowledge needed to harness the power of geospatial analysis, offering detailed guides and explanations to support learning and application.

Lastly, the Guides section addresses a variety of specialized topics, including the handling of NetCDF, working with databases and machine learning frameworks. This section caters to more specific or advanced user needs, providing in-depth coverage of niche areas within remote sensing.

This structured approach ensures users can easily navigate through the material, from basic concepts to complex applications, supporting a thorough and progressive learning experience.