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Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. The confounding effects of transient anomalies are particularly troublesome for spatiotemporal analysis of land surface processes. While spatiotemporal characterization provides a statistical basis to quantify the most significant temporal patterns and their spatial distributions without the need for a priori assumptions about the observed changes, the presence of transient anomalies can obscure the statistical properties of the spatiotemporal processes of interest. The objective of this study is to implement and evaluate a robust approach to distinguish clouds and other transient anomalies from diurnal and annual thermal cycles observed with time-lapse thermography. The approach uses Robust Principal Component Analysis (RPCA) to statistically distinguish low-rank (L) and sparse (S) components of the land surface temperature image time series, followed by a spatiotemporal characterization of its low rank component to quantify the dominant diurnal and annual thermal cycles in the study area. RPCA effectively segregates clouds, sensor anomalies, swath gaps, geospatial displacements and transient thermal anomalies into the sparse component time series. Spatiotemporal characterization of the low-rank component time series clearly resolves a variety of diurnal and annual thermal cycles for different land covers and water bodies while segregating transient anomalies potentially of interest.<\/jats:p>","DOI":"10.3390\/rs16020255","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T10:48:06Z","timestamp":1704797286000},"page":"255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Robust Cloud Suppression and Anomaly Detection in Time-Lapse Thermography"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9336-7391","authenticated-orcid":false,"given":"Christopher","family":"Small","sequence":"first","affiliation":[{"name":"Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1632-1955","authenticated-orcid":false,"given":"Daniel","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Geography, San Diego State University, San Diego, CA 92182, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Irish, R.R. (2000, January 24\u201328). Landsat 7 Automatic Cloud Cover Assessment. Proceedings of the AeroSense 2000, Orlando, FL, USA.","DOI":"10.1117\/12.410358"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and Expansion of the Fmask Algorithm: Cloud, Cloud Shadow, and Snow Detection for Landsats 4\u20137, 8, and Sentinel 2 Images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1364\/AO.9.000561","article-title":"Infrared Reflectance of High Altitude Clouds","volume":"9","author":"Hovis","year":"1970","journal-title":"Appl. Opt."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"583","DOI":"10.2151\/jmsj1965.60.1_583","article-title":"Spectral Reflectance of Clouds in the Near-Infrared: Comparison of Measurements and Calculations","volume":"60","author":"Twomey","year":"1982","journal-title":"J. Meteorol. Soc. Japan Ser. II"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1364\/AO.18.001881","article-title":"Diffuse Reflectance of Clouds: A Semiempirical Model","volume":"18","author":"Young","year":"1979","journal-title":"Appl. Opt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0012-8252(03)00042-4","article-title":"Optical Properties of Terrestrial Clouds","volume":"64","author":"Kokhanovsky","year":"2004","journal-title":"Earth-Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1175\/1520-0450(1971)010<0260:MOCEIT>2.0.CO;2","article-title":"Measurements of Cloud Emissivity in the 8\u201313 \u03bc Waveband","volume":"10","author":"Allen","year":"1971","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1175\/1520-0469(1976)033<0287:OOCIEE>2.0.CO;2","article-title":"Observations of Cloud Infrared Effective Emissivity","volume":"33","author":"Cox","year":"1976","journal-title":"J. Atmos. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhang, T., Wu, Y., Niu, Z., and Wang, Q. (2018). Cloud Effective Emissivity Retrievals Using Combined Ground-Based Infrared Cloud Measuring Instrument and Ceilometer Observations. Remote Sens., 10.","DOI":"10.3390\/rs10122033"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32141","DOI":"10.1029\/1998JD200032","article-title":"Discriminating Clear Sky from Clouds with MODIS","volume":"103","author":"Ackerman","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4231","DOI":"10.1175\/1520-0469(1995)052<4231:SOTMCF>2.0.CO;2","article-title":"Selection of the 1.375-\u039cm MODIS Channel for Remote Sensing of Cirrus Clouds and Stratospheric Aerosols from Space","volume":"52","author":"Gao","year":"1995","journal-title":"J. Atmos. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.rse.2012.05.031","article-title":"Spatiotemporal Dimensionality and Time-Space Characterization of Multitemporal Imagery","volume":"124","author":"Small","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","unstructured":"Small, C., Okujeni, A., Van der Linden, S., and Waske, B. (2018). Comprehensive Remote Sensing, Elsevier."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e2019WR026058","DOI":"10.1029\/2019WR026058","article-title":"ECOSTRESS: NASA\u2019s Next Generation Mission to Measure Evapotranspiration From the International Space Station","volume":"56","author":"Fisher","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1109\/TGRS.2019.2945701","article-title":"In-Flight Validation of the ECOSTRESS, Landsats 7 and 8 Thermal Infrared Spectral Channels Using the Lake Tahoe CA\/NV and Salton Sea CA Automated Validation Sites","volume":"58","author":"Hook","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","unstructured":"Logan, T., and Johnson, W. (2015). ECOSTRESS Level-1 Focal Plane Array and Radiometric Calibration Algorithm Theoretical Basis Document (ATBD)."},{"key":"ref_18","unstructured":"Smyth, M., and Leprince, S. (2018). ECOSTRESS Level-1B Resampling and Geolocation Algorithm Theoretical Basis Document (ATBD)."},{"key":"ref_19","unstructured":"Hulley, G., and Hook, S. (2015). ECOSTRESS Level-2 Land Surface Temperature and Emissivity Algorithm Theoretical Basis Document (ATBD)."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/36.700995","article-title":"A Temperature and Emissivity Separation Algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Images","volume":"36","author":"Gillespie","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/36.317447","article-title":"Separating Temperature and Emissivity in Thermal Infrared Multispectral Scanner Data: Implications for Recovering Land Surface Temperatures","volume":"31","author":"Kealy","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"230","article-title":"A Temperature-Emissivity Separation Method Using an Empirical Relationship between the Mean, the Maximum, and the Minimum of the Thermal Infrared Emissivity Spectrum","volume":"14","author":"Matsunaga","year":"1994","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_23","first-page":"1","article-title":"Robust Principal Component Analysis?","volume":"58","author":"Li","year":"2011","journal-title":"J. ACM (JACM)"},{"key":"ref_24","first-page":"167","article-title":"Sparse and Low-rank Matrix Decomposition via Alternating Direction Methods","volume":"9","author":"Yuan","year":"2013","journal-title":"Pac. J. Optim."},{"key":"ref_25","unstructured":"Lorenz, E. (1956). Empirical Orthogonal Functions and Statistical Weather Prediction, Massachusetts Institute of Technology. Statistical Forecasting Project."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/LGRS.2012.2185034","article-title":"Robustness of Annual Cycle Parameters to Characterize the Urban Thermal Landscapes","volume":"9","author":"Bechtel","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.3390\/rs70302850","article-title":"A New Global Climatology of Annual Land Surface Temperature","volume":"7","author":"Bechtel","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3184","DOI":"10.3390\/rs4103184","article-title":"Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany","volume":"4","author":"Bechtel","year":"2012","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112642","DOI":"10.1016\/j.rse.2021.112642","article-title":"Satellite-Derived Quantification of the Diurnal and Annual Dynamics of Land Surface Temperature","volume":"265","author":"Sismanidis","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/LGRS.2017.2779829","article-title":"Mapping the Spatiotemporal Dynamics of Europe\u2019s Land Surface Temperatures","volume":"15","author":"Sismanidis","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Small, C., and Sousa, D. (2019). Spatiotemporal Characterization of Mangrove Phenology and Disturbance Response: The Bangladesh Sundarban. Remote Sens., 11.","DOI":"10.3390\/rs11172063"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"760650","DOI":"10.3389\/frsen.2022.760650","article-title":"Joint Characterization of Spatiotemporal Data Manifolds","volume":"3","author":"Sousa","year":"2022","journal-title":"Front. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:43:11Z","timestamp":1760103791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020255"],"URL":"https:\/\/doi.org\/10.3390\/rs16020255","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]}}}