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Introduction

Evolution of Remote Sensing Research​

Research in remote sensing has seen profound changes over the past several decades. Initially, the field was the exclusive domain of a few governments with the technological and financial resources to deploy satellites and process the imagery. This early period was characterized by a primary focus 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 and the foundational steps towards creating the European Space Agency (ESA) marked a pivotal shift towards supporting civilian space programs alongside space and aeronautics research. Despite these advancements, access to satellite data remained a significant challenge for the research community. The data, while increasingly available, 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.

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. The Tutorials section forms the bulk of the material, introducing foundational and advanced remote sensing concepts designed to equip users with the knowledge needed to harness the power of geospatial analysis. For practical aspects such as software setup, understanding data structures, and other preparatory knowledge, the Appendix serves as a vital resource, offering detailed guides and explanations. Lastly, the Miscellaneous section addresses a variety of specialized topics, including the handling of NetCDF and other file types, catering to more specific or advanced user needs. This structured approach ensures users can easily navigate through the material, from basic concepts to complex applications.