# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 1 Introduction  
We are currently experiencing an exciting new era of Earth observation, wherein multiple, freely available remote sensing systems provide us data at unprecedented spatial, temporal, and spectral resolutions. The Landsat mission occupies a prominent role in this development: The opening of the Landsat archive in 2008 [1] has fundamentally changed the usage of Earth observation data [2]toward mass utilization of every dataset available [3] with exponential increases in access statistics [4]. This unique long-term data record [5] is currently amended by the Sentinel-2 constellation, which provides even higher spatial, temporal, and spectral resolution [6]. Currently, the European Space Agency (ESA) and the U.S. Geological Survey (USGS) publish a daily data volume of 4 TB Sentinel-2 [7] and 1.5 TB Landsat data [8; 9], thus quickly accumulating petabyte-scale archives. This regular data influx might eventually enable us to achieve sustainable development goals by closely monitoring environmental status and change at relevant scales and global extent [10; 11]. However, this flood of data can easily be overwhelming, both in terms of volume and usage complexity. While the first point is merely a technical burden that can be leveled out with enough processing power and investments in storage [12], Earth observation data still need to be processed to a considerable degree before being adequate for most analyses.  
Thus, there is an urgent need for data, that have been preprocessed to allow immediate analysis with a minimum of additional user effort, and which guarantee interoperability both through time and with other datasets. Such data are termed analysis ready data (ARD) ([[http://ceos.org/ard/](http://ceos.org/ard/)]([http://ceos.org/ard/](http://ceos.org/ard/))), which is mostly used to describe radiometrically and geometrically consistent data that include cloud and other poor-quality observation flags for filtering data prior to analysis [13]. Whilst the development, harmonization, and provision of ARD [13; 14] is a huge step forward for increasing scientific and operational uptake from broader user groups, a significant amount of processing is still necessary after having crossed the ARD barrier: (i) ARD still amount for the same, or even more, data volume than the original data; (ii) spatial and temporal variability in data availability and partial incompleteness due to clouds, acquisition orbits, and observation scenarios require specialized algorithms--or an additional round of processing--to generate spatial completeness and temporal equidistance before being ready for analysis. Therefore, it is key to go beyond ARD, and thus to provide methods and data to facilitate higher-level processing or even generate and distribute highly analysis ready data (hARD) to end users.  
This paper describes the scientific, open source software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), which is being developed as an 'all-in-one' solution for the mass-processing and analysis of medium-resolution satellite image archives to enable both large area and time series applications. FORCE supports processing of Landsat 4/5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Sentinel-2 A/B Multispectral Instrument (MSII) imagery. The software is capable of processing Level 1 products to Level 2-4 products, which represent different degrees of ARD. This manuscript gives an overview of the processing capabilities of FORCE version 2.1, and will end with an overview of current applications, followed by a summary on current and future improvements.