FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Abstract

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region

“…Images from both sensors are available in the FORCE [49] archive, which holds preprocessed data for the region of interest in Trier University's local data servers. From this archive, suitable images were manually selected.…”

“…Illumination differences in satellite imagery are corrected during preprocessing [49,51] nevertheless residual effects can persist. Depending on the forest density, background effects can influence the VI.…”

Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

“…The output composite includes six reflective bands, a brightness temperature and a QF band. The QF band value is preserved from the image and modified for values 1, 11, and 14 to record the presence of water in the time-series (see Table 3, QF values [15][16][17]. Effectively, the output 16-day composites represent observation(s) with the highest quality.…”

“…The lack of Landsat Level 3 products requires users to develop and implement custom solutions for spatial and temporal data aggregation. Several initiatives are aimed to create a consistent ARD data products from the Landsat data archive, including Web-enabled Landsat Data (WELD) [15], USGS Landsat ARD [16], and FORCE ARD [17]. However, all of the cited products are available either at a limited geographic extent, limited time intervals, or provided as tools, not as datasets.…”

Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

“…Sentinel-2 from European Space Agency (ESA) provides good spatial (20-60 m) and temporal (five days) resolutions for the visible spectrum, but lacks the Thermal Infrared (TIR) band [38]. Recently, methods have been developed with the aim of taking advantage of the characteristics of both products (Landsat-8 and Sentinel-2), with efforts to generate harmonized time series of surface reflectances for land monitoring applications being especially relevant [39][40][41]. The combination of the products of both programs allows for an effective increase in spatial and temporal coverage, providing a greater availability of data to users [42].…”