Читать книгу Earth Observation Using Python - Rebekah B. Esmaili - Страница 13

There are thousands of datasets containing observations of the Earth. This chapter describes some satellite types, orbits, and missions, which benefit a variety of fields within Earth sciences, including atmospheric science, oceanography, and hydrology. Data are received on the ground through receiver stations and processed for use using retrieval algorithms. But the raw data requires further manipulation to be useful, and Python is a good choice for analysis and visualization of these datasets.

At present, there are over 13,000 satellite‐based Earth observations freely and openly listed on www.data.gov. Not only is the quantity of available data notable, its quality is equally impressive; for example, infrared sounders can estimate brightness temperatures within 0.1 K from surface observations (Tobin et al., 2013), imagers can detect ocean currents with an accuracy of 1.0 km/hr (NOAA, 2020), and satellite‐based lidar can measure the ice‐sheet elevation change with a 10 cm sensitivity (Garner, 2015). Previously remote parts of our planet are now observable, including the open oceans and sparsely populated areas. Furthermore, many datasets are available in near real time with image latencies ranging from less than an hour down to minutes – the latter being critically important for natural disaster prediction. Having data rapidly available enables science applications and weather prediction as well as to emergency management and disaster relief. Research‐grade data take longer to process (hours to months) but has a higher accuracy and precision, making it suitable for long‐term consistency. Thus, we live in the “golden age” of satellite Earth observation. While the data are accessible, the tools and skills necessary to display and analyze this information require practice and training.

Python is a modern programming language that has exploded in popularity, both within and beyond the Earth science community. Part of its appeal is its easy‐to‐learn syntax and the thousands of available libraries that can be synthesized with the core Python package to do nearly any computing task imaginable. Python is useful for reading Earth‐observing satellite datasets, which can be difficult to use due to the volume of information that results from the multitude of sensors, platforms, and spatio‐temporal spacing. Python facilitates reading a variety of self‐describing binary datasets in which these observations are often encoded. Using the same software, one can complete the entirety of a research project and produce plots. Within a notebook environment, a scientist can document and distribute the code to other users, which can improve efficiency and transparency within the Earth sciences community.

Satellite data often require some pre‐processing to make it usable, but which steps to take and why are not always clear. Data users often misinterpret concepts such as data quality, how to perform an atmospheric correction, or how to implement the complex gridding schemes necessary to compare data at different resolutions. Even to a technical user, the nuances can be frustrating and difficult to overcome. This book walks you through some of the considerations a user should make when working with satellite data.

The primary goal of this text is to get the reader up to speed on the Python coding techniques needed to perform research and analysis using satellite datasets. This is done by adopting an example‐driven approach. It is light on theory but will briefly cover relevant background in a nontechnical manner. Rather than getting lost in the weeds, this book purposefully uses realistic examples to explain concepts. I encourage you to run the interactive code alongside reading the text. In this chapter, I will discuss a few of the satellites, sensors, and datasets covered in this book and explain why Python is a great tool for visualizing the data.