FORCE is intended to be an all-in-one solution for the mass-processing of selected medium-resolution satellite image archives to enable large area + time series applications. Currently supported are Landsat 4/5 TM, Landsat 7 ETM+, Landsat 8 OLI and Sentinel-2 A/B MSI. The software is capable of processing Level 1 products as obtained from the space agencies to Level 2–4 products.
Download the code from https://github.com/davidfrantz/force.
See https://force-eo.readthedocs.io/en/latest/about.html for more details on Force.
Frantz, D. (2019): FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond.Remote Sensing, 11, 1124. DOI
D. Frantz, E. Haß, A. Uhl, J. Stoffels & J. Hill (2018): Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment, 215, 471-481. DOI
D. Frantz, A. Röder, M. Stellmes & J. Hill (2017): Phenology-adaptive pixel-based compositing using optical earth observation imagery. Remote Sensing of Environment, 190, 331-347. DOI
D. Frantz (2017). Generation of Higher Level Earth Observation Satellite Products for Regional Environmental Monitoring. Ph.D. dissertation. Faculty of Regional and Environmental Sciences, Trier University, Trier, Germany, p. 194. Online available
D. Frantz, A. Röder, M. Stellmes, and J. Hill (2016): An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications. IEEE Transactions on Geoscience and Remote Sensing, 54 (7): 3928-3943. DOI
D. Frantz, A. Röder, M. Stellmes, and J. Hill (2015): On the derivation of a spatially distributed aerosol climatology for its incorporation in a radiometric Landsat pre-processing framework.Remote Sensing Letters, 6 (8): 647-656. DOI
D. Frantz, A. Röder, T. Udelhoven & M. Schmidt (2015): Enhancing the Detectability of Clouds and Their Shadows in Multitemporal Dryland Landsat Imagery: Extending Fmask. IEEE Geoscience and Remote Sensing Letters, 12 (6): 1242–1246. DOI
D. Frantz, A. Röder, M. Stellmes, and J. Hill (2017): Phenology-adaptive pixel-based compositing using optical earth observation imagery.Remote Sensing of Environment, 190,331-347. DOI
D. Frantz, M. Stellmes, A. Röder, T. Udelhoven, S. Mader, and J. Hill (2016): Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs.IEEE Transactions on Geoscience and Remote Sensing, 54 (7): 4153-4164. DOI