{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T14:48:15Z","timestamp":1785595695261,"version":"3.56.0"},"reference-count":71,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T00:00:00Z","timestamp":1557792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral images, we demonstrate a capacity to reconstruct cloud-affected pixels and predict their underlying surface reflectance values. To do this, cloudy pixels were masked and a parametric family of non-separable covariance functions was automated fitted, using a composite likelihood estimator. A subset of cloud-free pixels per scene was used to perform a kriging interpolation and to predict the spectral reflectance per each cloud-affected pixel. The approach was evaluated using a benchmark dataset of cloud-free pixels, with a synthetic cloud superimposed upon these data. An overall root mean square error (RMSE) of between 0.5% and 16% of the reflectance was achieved, representing a relative root mean square error (rRMSE) of between 0.2% and 7.5%. The spectral similarity between the predicted and reference reflectance signatures was described by a mean spectral angle (MSA) of between 1\u00b0 and 11\u00b0, demonstrating the spatial and spectral coherence of predictions. The approach provides an efficient spatiotemporal interpolation framework for cloud removal, gap-filling, and denoising in remotely sensed datasets.<\/jats:p>","DOI":"10.3390\/rs11101145","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T10:42:33Z","timestamp":1557830553000},"page":"1145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8377-8736","authenticated-orcid":false,"given":"Yoseline","family":"Angel","sequence":"first","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group (HALO), Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3604-0747","authenticated-orcid":false,"given":"Rasmus","family":"Houborg","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence (GSCE), South Dakota State University, Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-5272","authenticated-orcid":false,"given":"Matthew F.","family":"McCabe","sequence":"additional","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group (HALO), Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,14]]},"reference":[{"key":"ref_1","first-page":"655","article-title":"Ecological applications of remote sensing at multiple scales","volume":"Volume 1","author":"Pugnaire","year":"2007","journal-title":"Functional Plant Ecology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1111\/j.1469-8137.2010.03284.x","article-title":"Remote sensing of plant functional types","volume":"186","author":"Ustin","year":"2010","journal-title":"New Phytol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2987","DOI":"10.1093\/jxb\/erp156","article-title":"Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence","volume":"60","author":"Mishra","year":"2009","journal-title":"J. Exp. Bot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1080\/05704928.2012.705800","article-title":"Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review","volume":"48","author":"Dale","year":"2013","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_5","unstructured":"(2019, April 08). Preliminary Assessment of the Value of Landsat 7 ETM+ SLC-off Data, Available online: https:\/\/landsat.usgs.gov\/sites\/default\/files\/documents\/SLC_off_Scientific_Usability.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15955","DOI":"10.1029\/JD093iD12p15955","article-title":"Estimation of Saharan aerosol optical thickness from blurring effects in thematic mapper data","volume":"93","author":"Tanre","year":"1988","journal-title":"J. Geophys. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Adler-Golden, S.M., Robertson, D.C., Richtsmeier, S.C., and Ratkowski, A.J. (2009, January 27). Cloud effects in hyperspectral imagery from first-principles scene simulations. Proceedings of the SPIE Defense, Security, and Sensing, Orlando, FL, USA.","DOI":"10.1117\/12.819832"},{"key":"ref_8","unstructured":"(2019, April 08). Validation of On-Board Cloud Cover Assessment Using EO-1, Available online: https:\/\/eo1.gsfc.nasa.gov\/new\/extended\/sensorWeb\/EO-1_Validation On-board Cloud Assessment_Rpt.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM Plus data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TGRS.2012.2197682","article-title":"Cloud removal from multitemporal satellite images using information cloning","volume":"51","author":"Lin","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing Information Reconstruction of Remote Sensing Data: A Technical Review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.14358\/PERS.71.9.1079","article-title":"Cloud-free satellite image mosaics with regression trees and histogram matching","volume":"71","author":"Helmer","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/LGRS.2008.915596","article-title":"Contextual spatiospectral postreconstruction of cloud-contaminated images","volume":"5","author":"Benabdelkader","year":"2008","journal-title":"IEEE Geosc. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TGRS.2005.861929","article-title":"Contextual reconstruction of cloud-contaminated multitemporal multispectral images","volume":"44","author":"Melgani","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/JSTARS.2019.2898348","article-title":"A spatiotemporal fusion based cloud removal method for remote sensing images with land cover changes","volume":"12","author":"Shen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1898","DOI":"10.1109\/JSTARS.2015.2400636","article-title":"Smart information reconstruction via time-space-spectrum continuum for cloud removal in satellite images","volume":"8","author":"Chang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","first-page":"453","article-title":"Automated detection and removal of clouds and their shadows from Landsat TM images","volume":"82","author":"Wang","year":"1999","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.imavis.2006.03.007","article-title":"Cloud covering denoising through image fusion","volume":"25","author":"Gabarda","year":"2007","journal-title":"Image Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1080\/014311600209814","article-title":"Reconstructing cloudfree NDVI composites using Fourier analysis of time series","volume":"21","author":"Roerink","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"W10507","DOI":"10.1029\/2012WR012115","article-title":"Spatiotemporal reconstruction of gaps in multivariate fields using the direct sampling approach","volume":"48","author":"Mariethoz","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1029\/2012WR012602","article-title":"Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations","volume":"49","author":"Jha","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1080\/01431160802549294","article-title":"Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging","volume":"30","author":"Zhang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"569","DOI":"10.14358\/PERS.75.5.569","article-title":"Closest spectral fit for removing clouds and cloud shadows","volume":"75","author":"Meng","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.isprsjprs.2014.02.015","article-title":"Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model","volume":"92","author":"Cheng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cerra, D., M\u00fcller, R., and Reinartz, P. (2015, January 22\u201324). Cloud removal in image time series through unmixing. Proceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Annecy, France.","DOI":"10.1109\/Multi-Temp.2015.7245787"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/TGRS.2015.2486780","article-title":"Thin cloud removal based on signal transmission principles and spectral mixture analysis","volume":"54","author":"Xu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"115","article-title":"A defogging method based on hyperspectral unmixing","volume":"35","author":"Feng","year":"2015","journal-title":"Acta Opt. Sin."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yin, G., Mariethoz, G., and McCabe, M.F. (2017). Gap-filling of landsat 7 imagery using the direct sampling method. Remote Sens., 9.","DOI":"10.3390\/rs9010012"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.3390\/ijgi4042306","article-title":"Spatiotemporal data mining: A computational perspective","volume":"4","author":"Shekhar","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_30","first-page":"151","article-title":"Geostatistical space-time models, stationarity, separability and full symmetry","volume":"Volume 1","author":"Finkenstadt","year":"2006","journal-title":"Statistical Methods for Spatio-Temporal Systems"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1002\/env.2392","article-title":"A general procedure for selecting a class of fully symmetric space-time covariance functions","volume":"27","author":"Palma","year":"2016","journal-title":"Environmetrics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s00362-015-0674-2","article-title":"A new method to build spatio-temporal covariance functions: Analysis of ozone data","volume":"57","author":"Omidi","year":"2015","journal-title":"Stat. Pap."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1019861427772","article-title":"Nonseparable space-time covariance models: Some parametric families","volume":"34","author":"Myers","year":"2002","journal-title":"Math. Geol."},{"key":"ref_34","unstructured":"(2019, April 08). Historical Weather for 2015 in Al-Kharj Prince Sultan Air Base, Saudi Arabia. Available online: http:\/\/weatherspark.com\/history\/32768\/2015\/Al-Kharj-Riyadh-Saudi-Arabia."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2016.08.017","article-title":"Adapting a regularized canopy reflectance model (REGFLEC) for the retrieval challenges of dryland agricultural systems","volume":"186","author":"Houborg","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1002\/joc.4374","article-title":"A multi-decadal assessment of the performance of gauge and model based rainfall products over Saudi Arabia: Climatology, anomalies and trends","volume":"36","author":"McCabe","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1117\/12.417022","article-title":"EO-1\/Hyperion hyperspectral imager design, development, characterization, and calibration","volume":"4151","author":"Folkman","year":"2001","journal-title":"Proc. SPIE"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111707","DOI":"10.1117\/1.OE.51.11.111707","article-title":"Speed and accuracy improvements in FLAASH atmospheric correction of hyperspectral imagery","volume":"51","author":"Perkins","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Felde, G.W., Anderson, G.P., Gardner, J.A., Adler-Golden, S.M., Matthew, M.W., and Berk, A. (2004, January 12). Water vapor retrieval using the FLAASH atmospheric correction algorithm. Proceedings of the SPIE Defense and Security, Orlando, FL, USA.","DOI":"10.21236\/ADA423120"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2898","DOI":"10.1109\/TGRS.2005.857901","article-title":"Validation and refinement of hyperspectral\/multispectral atmospheric compensation using shadowband radiometers","volume":"43","author":"Rochford","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"key":"ref_41","first-page":"29","article-title":"Compensation of hyperspectral data for atmospheric effects","volume":"14","author":"Griffin","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2017.03.013","article-title":"Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances","volume":"194","author":"Houborg","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Adler-Golden, S.M., Matthew, M.W., Berk, A., Fox, M.J., and Ratkowski, A.J. (2008, January 7\u201311). Improvements in aerosol retrieval for atmospheric correction. Proceedings of the IGARSS 2008\u20142008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779300"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1364\/AO.27.002502","article-title":"Numerically stable algorithm for Discrete-Ordinate-Method Radiative Transfer in multiple scattering and emitting layered media","volume":"27","author":"Stamnes","year":"1988","journal-title":"Appl. Opt."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Staenz, K., Neville, R.A., Clavette, S., Landry, R., and White, H.P. (2002, January 24\u201328). Retrieval of surface reflectance from Hyperion radiance data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada.","DOI":"10.4095\/219887"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TGRS.2003.815018","article-title":"Hyperion, a space-based imaging spectrometer","volume":"41","author":"Pearlman","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Matthew, M.W., Adler-Golden, S.M., Berk, A., Felde, G., Anderson, G.P., Gorodetzkey, D., Paswaters, S., and Shippert, M. (2003, January 17\u201323). Atmospheric correction of spectral imagery: Evaluation of the FLAASH algorithm with AVIRIS data. Proceedings of the Applied Imagery Pattern Recognition Workshop, Washington, DC, USA.","DOI":"10.1117\/12.499604"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A Transform for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Int. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Matthew, M.W., Adler-Golden, S.M., Berk, A., Richtsmeier, S.C., Levine, R.Y., Bernstein, L.S., Acharya, P.K., Anderson, G.P., Felde, G.W., and Hoke, M.P. (2000, January 23). Status of atmospheric correction using a modtran4-based algorithm. Proceedings of the SPIE AeroSense 2000, Orlando, FL, USA.","DOI":"10.1117\/12.410341"},{"key":"ref_50","unstructured":"Cochran, W.G. (1977). Sampling Techniques, John Wiley & Sons. [3rd ed.]."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1080\/13658810902873512","article-title":"Sample surveying to estimate the mean of a heterogeneous surface: Reducing the error variance through zoning","volume":"24","author":"Wang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.spasta.2012.08.001","article-title":"A review of spatial sampling","volume":"2","author":"Wang","year":"2012","journal-title":"Spat. Stat."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Montero, J.M., Fern\u00e1ndez, G., and Mateu, J. (2015). Spatial and Spatio-Temporal Geostatistical Modeling and Kriging, John Wiley & Sons. [1st ed.].","DOI":"10.1002\/9781118762387"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1080\/01621459.1999.10473885","article-title":"Classes of nonseparable, spatio-temporal stationary covariance functions","volume":"94","author":"Cressie","year":"1999","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1198\/016214502760047113","article-title":"Nonseparable, stationary covariance functions for space-time data","volume":"97","author":"Gneiting","year":"2002","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1214\/aoms\/1177706875","article-title":"On the comparative anatomy of transformations","volume":"28","author":"Tukey","year":"1957","journal-title":"Ann. Math. Stat."},{"key":"ref_57","unstructured":"Package CompRandFld (2019, April 08). R Package Version 1.0.3-4. Available online: https:\/\/cran.r-project.org\/package=CompRandFld."},{"key":"ref_58","unstructured":"R Core Team (2019, April 08). R: A Language and Environment for Statistical Computing. R Package Version 3.2.2. Available online: http:\/\/www.R-project.org."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1080\/00401706.1991.10484771","article-title":"A comparison of spatial semivariogram estimators and corresponding ordinary kriging predictors","volume":"33","author":"Zimmerman","year":"1991","journal-title":"Technometrics"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"9","DOI":"10.2307\/1400419","article-title":"A Composite Likelihood Approach to Semivariogram Estimation","volume":"4","author":"Curriero","year":"1999","journal-title":"J. Agric. Biol. Environ. Stat."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v063.i09","article-title":"Analysis of random fields using CompRandFld","volume":"63","author":"Padoan","year":"2015","journal-title":"J. Stat. Softw."},{"key":"ref_62","unstructured":"Cressie, N., and Wikle, C. (2011). Statistics for Spatio-Temporal Data, John Wiley & Sons. [1st ed.]."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1080\/01431160701250416","article-title":"Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach","volume":"28","author":"Zhang","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.isprsjprs.2009.06.001","article-title":"Geostatistical interpolation of SLC-off Landsat ETM+ images","volume":"64","author":"Pringle","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Webster, R., and Oliver, M.A. (2007). Geostatistics for Environmental Scientists, John Wiley & Sons. [1st ed.].","DOI":"10.1002\/9780470517277"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1080\/01621459.2015.1072541","article-title":"Spatio-temporal covariance and cross-covariance functions of the great circle distance on a sphere","volume":"111","author":"Porcu","year":"2016","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1214\/14-STS487","article-title":"Cross-covariance functions for multivariate geostatistics","volume":"30","author":"Genton","year":"2015","journal-title":"Stat. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1111\/j.1467-9469.2011.00751.x","article-title":"Non-stationary cross-covariance models for multivariate processes on a globe","volume":"38","author":"Jun","year":"2011","journal-title":"Scand. J. Stat."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1109\/TGRS.2003.813210","article-title":"Cross comparison of EO-1 sensors and other Earth resources sensors to Landsat-7 ETM+ using Railroad Valley Playa","volume":"41","author":"Thome","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","unstructured":"Datt, B., and Jupp, D.L.B. (2004). Hyperion Data Processing Workshop, Hands-On Processing Instructions, CSIRO Office of Space Science & Applications Earth Observation Centre."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1111\/j.1467-9868.2007.00633.x","article-title":"Fixed rank kriging for very large spatial data sets","volume":"70","author":"Cressie","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:51:41Z","timestamp":1760187101000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,14]]},"references-count":71,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11101145"],"URL":"https:\/\/doi.org\/10.3390\/rs11101145","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,14]]}}}