The challenge of Analysis Ready Data in Earth Observation

Towards seamless integration: The quest for interoperable and harmonized EO data

One of the major challenges when building applications using EO data is having “clean-ready-to-use-data”, or what we most commonly refer to as Analysis Ready Data (ARD); that is, data that we can use almost out-of-the-box and do time-series analysis, train machine learning models and derive high-level products without spending huge amounts of time in data preparation in order to work on the actual task.

NOTE 1: Although there are efforts towards non-optical ARD (e.g. Sentinel-1 ARD¹), this post covers optical ARD.
NOTE 2: I have split this article into two sections.
In the first section I explore what we “traditionally” consider as ARD and ARD products such as the Landsat ARD and the Harmonized Landsat and Sentinel-2 (HLS) ARD. I follow ARD-related workshops and highlight several points I find interesting in the presentations. I also dedicate a section related to Planetscope data and Planet’s efforts towards creating an ARD product.
In the second section, I review a recent study conducted by the Landsat Advisory Group exploring earth observation products, including data, algorithms, and workflows. I dedicate Section 2 of this article on it, as I consider it an “expanded” version of ARD.