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<p>The Multi-angle Imaging SpectroRadiometer (<span class="smallcaps">MISR</span>) instrument on NASA's Terra platform has been acquiring global measurements of the spectrodirectional reflectance of the Earth since 24 February 2000 and is still operational as of this writing. The primary radiometric data product generated by this instrument is known as the Level 1B2 (L1B2) Georectified Radiance Product (<span class="smallcaps">GRP</span>): it contains the 36 radiometric measurements acquired by the instrument's nine cameras, each observing the planet in four spectral bands. The product version described here is projected on a digital elevation model and is available from the NASA Langley Atmospheric Science Data Center (ASDC; <span class="uri">http://doi.org/10.5067/Terra/MISR/MI1B2T_L1.003</span>; <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx10">Jovanovic et al.</a>, <a href="#bib1.bibx10">1999</a></span>). The <span class="smallcaps">MISR</span> instrument is highly reliable. Nevertheless, its onboard computer occasionally becomes overwhelmed by the number of raw observations coming from the cameras' focal planes, especially when switching into or out of <span class="smallcaps">Local Mode</span> acquisitions that are often requested in conjunction with field campaigns. Whenever this occurs, one or more lines of data are dropped while the computer resets and readies itself for accepting new data. Although this type of data loss is minuscule compared to the total number of measurements acquired and is marginal for atmospheric studies dealing with large areas and long periods of time, this outcome can be crippling for land surface studies that focus on the detailed analysis of particular scenes at specific times. This paper describes the problem, reports on the prevalence of missing data, proposes a practical solution to optimally estimate the values of the missing data and provides evidence of the performance of the algorithm through specific examples in southern Africa. The software to process <span class="smallcaps">MISR</span> <span class="smallcaps">L1B2</span> <span class="smallcaps">GRP</span> data products as described here is openly available to the community from the GitHub website (<span class="uri">https://github.com/mmverstraete</span> or <a href="https://doi.org/10.5281/zenodo.3519988">https://doi.org/10.5281/zenodo.3519988</a>). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020, <a href="https://doi.org/10.5880/fidgeo.2020.012">https://doi.org/10.5880/fidgeo.2020.012</a>) includes five items: (A1) a compressed archive (<code>L1B2_Out.zip</code>) containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual (<code>L1B2_Out.pdf</code>) describing how to install, uncompress and explore those files; (A3) an additional compressed archive (<code>L1B2_Suppl.zip</code>) containing a similar set of results, only for eight other sites, spanning a much wider range of geographical, climatic and ecological conditions; (A4) a companion user manual (<code>L1B2_Suppl.pdf</code>) describing how to install, uncompress and explore those additional files; and (A5) a separate input <span class="smallcaps">MISR</span> data archive (<code>L1B2_input_68050.zip</code>) for <span class="smallcaps">Path</span> 168, <span class="smallcaps">Orbit</span> 68050. This latter archive is usable with the second set (B; Verstraete and Vogt, 2020; <a href="https://doi.org/10.5880/fidgeo.2020.011">https://doi.org/10.5880/fidgeo.2020.011</a>), which includes (B1) a stand-alone, self-contained, executable version of the <span class="smallcaps">L1B2</span> correction codes (<code>L1B2_Soft_Win.zip</code>) that uses the IDL Virtual Machine technology and does not require a paid IDL license as well as (B2) a user manual (<code>L1B2_Soft_Win.pdf</code>) that explains how to install, uncompress and use this software.</p>