K-sharp: A segmented regression approach for image sharpening and normalization

Bruno Jose Luis Aragon Solorio, Kerry Cawse-Nicholson, Glynn Hulley, Rasmus Houborg, Joshua B. Fisher

    Research output: Contribution to journalArticlepeer-review


    In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-satellites known as CubeSats are presenting a cost-effective option for EO; their capacity of working as a constellation has brought an unprecedented opportunity for EO in terms of achievable spatial and temporal resolutions, albeit at the cost of decreased accuracy and cross-sensor consistency. As such, CubeSat datasets often require post-calibration approaches before using them for scientific applications. K-sharp is a relatively simple, data-agnostic machine learning approach that combines K-means and partial least squares regression to derive relationships between two sets of images for normalization. This study used Planet's four-band CubeSat imagery to sharpen day-coincident Landsat 8 normalized difference vegetation index, albedo, and the first short-wave infrared (SWIR) band from 30 m to 3 m spatial resolution (it should be noted that the four-band CubeSat product does not include the first SWIR band, and that the calculation of albedo is not directly possible from this product). K-sharp was tested over agricultural, savanna, rainforest, and tundra sites with and without atmospheric correction. Our model reproduced surface conditions with an average r2 of 0.88 (rMAE = 11.39%) across all study sites and target variables when compared against the original Landsat 8 data. These results showcase the promising potential of K-sharp in generating precise, CubeSat-derived datasets with high radiometric quality, which can be incorporated into agricultural or ecological applications to enhance their decision-making process at fine spatial scales.
    Original languageEnglish (US)
    Pages (from-to)100095
    JournalScience of Remote Sensing
    StatePublished - Jul 26 2023

    Bibliographical note

    KAUST Repository Item: Exported on 2023-07-31
    Acknowledgements: This study was supported by the National Aeronautics and Space Administration (NASA), United States, the NASA Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Technology, administered by Universities Space Research Association under contract with NASA, ECOSTRESS Science and Applications Team: Grant No. 80NSSC20K0167, and King Abdullah University of Science and technology (KAUST). KCN and GH contributed to the research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA and with support from the ECOSTRESS mission and R&A program. We also acknowledge NASA's Commercial Smallsat Data Acquisition (CSDA) Program, which provided access to the PlanetScope imagery.
    This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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