{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:54:40Z","timestamp":1770288880446,"version":"3.49.0"},"reference-count":89,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T00:00:00Z","timestamp":1640908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201706040079"],"award-info":[{"award-number":["201706040079"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth\u2019s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 \u00d7 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.<\/jats:p>","DOI":"10.3390\/rs14010172","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1885-340X","authenticated-orcid":false,"given":"Zhipeng","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland"},{"name":"Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland"}]},{"given":"Giuseppe","family":"Amatulli","sequence":"additional","affiliation":[{"name":"School of the Environment, Yale University, New Haven, CT 06511, USA"},{"name":"Center for Research Computing, Yale University, New Haven, CT 06511, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-9268","authenticated-orcid":false,"given":"Petri K. E.","family":"Pellikka","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland"},{"name":"Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3899-8860","authenticated-orcid":false,"given":"Janne","family":"Heiskanen","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland"},{"name":"Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111558","DOI":"10.1016\/j.rse.2019.111558","article-title":"Transitioning from change detection to monitoring with remote sensing: A paradigm shift","volume":"238","author":"Woodcock","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2012.04.019","article-title":"A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images","volume":"124","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2019.02.016","article-title":"Benefits of the free and open Landsat data policy","volume":"224","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"210","article-title":"Burned area detection based on Landsat time series in savannas of southern Burkina Faso","volume":"64","author":"Liu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/0034-4257(95)00227-8","article-title":"Combined use of optical and microwave remote sensing data for crop growth monitoring","volume":"56","author":"Clevers","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111685","DOI":"10.1016\/j.rse.2020.111685","article-title":"Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery","volume":"240","author":"Bolton","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yan, L., and Roy, D.P. (2018). Large-area gap filling of Landsat reflectance time series by spectral-angle-mapper based spatio-temporal similarity (SAMSTS). Remote Sens., 10.","DOI":"10.3390\/rs10040609"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Egorov, A.V., Roy, D.P., Zhang, H.K., Li, Z., Yan, L., and Huang, H. (2019). Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) observation coverage over the conterminous United States and implications for terrestrial monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11040447"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.rse.2012.08.035","article-title":"Remote sensing of tropical ecosystems: Atmospheric correction and cloud masking matter","volume":"127","author":"Hilker","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Brooks, E.B., Wynne, R.H., and Thomas, V.A. (2018). Using window regression to gap-fill Landsat ETM+ post SLC-Off data. Remote Sens., 10.","DOI":"10.3390\/rs10101502"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2012.12.012","article-title":"Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method","volume":"131","author":"Zeng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3656","DOI":"10.1109\/TGRS.2017.2656162","article-title":"Multitemporal Landsat missing data recovery based on tempo-spectral angle model","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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_18","first-page":"102319","article-title":"A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics","volume":"99","author":"Tang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/83.935036","article-title":"Filling-in by joint interpolation of vector fields and gray levels","volume":"10","author":"Ballester","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1109\/TGRS.2008.2005780","article-title":"A MAP-based algorithm for destriping and inpainting of remotely sensed images","volume":"47","author":"Shen","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s40808-020-00940-5","article-title":"Applicability of ordinary Kriging modeling techniques for filling satellite data gaps in support of coastal management","volume":"7","author":"Kostopoulou","year":"2021","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.1109\/TGRS.2017.2670021","article-title":"An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data","volume":"55","author":"Ng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2020.02.008","article-title":"Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning","volume":"162","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1145\/355780.355786","article-title":"A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points","volume":"4","author":"Akima","year":"1978","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1145\/321607.321609","article-title":"A new method of interpolation and smooth curve fitting based on local procedures","volume":"17","author":"Akima","year":"1970","journal-title":"J. ACM (JACM)"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Evenden, G.I. (1989). Review of Three Cubic Spline Methods in Graphics Applications.","DOI":"10.3133\/ofr8919"},{"key":"ref_28","first-page":"925","article-title":"Comparison Between Akima and Beta-Spline Interpolators for Digital Elevation Models","volume":"29","author":"Dias","year":"1993","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2018.05.007","article-title":"Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR","volume":"213","author":"Fassnacht","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1029\/90EO00319","article-title":"Free software helps map and display data","volume":"72","author":"Wessel","year":"1991","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_31","first-page":"443","article-title":"A simple method for monotonic interpolation in one dimension","volume":"239","author":"Steffen","year":"1990","journal-title":"Astron. Astrophys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1117\/1.JRS.12.012005","article-title":"Modeling and intercomparison of field and laboratory hyperspectral goniometer measurements with G-LiHT imagery of the Algodones Dunes","volume":"12","author":"Bachmann","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ascom.2016.05.006","article-title":"VARTOOLS: A program for analyzing astronomical time-series data","volume":"17","author":"Hartman","year":"2016","journal-title":"Astron. Comput."},{"key":"ref_34","unstructured":"Kempeneers, P. (2018). PKTOOLS-Processing Kernel for Geospatial Data, Open Source Geospatial Foundation. Version 2.6.7.6."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"McInerney, D., and Kempeneers, P. (2015). Open Source Geospatial Tools\u2014Applications in Earth Observation, Springer.","DOI":"10.1007\/978-3-319-01824-9"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40965-017-0031-6","article-title":"Orfeo ToolBox: Open source processing of remote sensing images","volume":"2","author":"Grizonnet","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Inglada, J., and Christophe, E. (2009, January 12\u201317). The Orfeo Toolbox remote sensing image processing software. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417481"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational high resolution land cover map production at the country scale using satellite image time series. Remote Sens., 9.","DOI":"10.3390\/rs9010095"},{"key":"ref_39","unstructured":"Inglada, J. (2016, February 04). OTB Gapfilling, a Temporal Gapfilling for Image Time Series Library. Available online: http:\/\/tully.ups-tlse.fr\/jordi\/temporalgapfilling."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112419","DOI":"10.1016\/j.rse.2021.112419","article-title":"Recurrent-based regression of Sentinel time series for continuous vegetation monitoring","volume":"263","author":"Garioud","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"Amatulli, G., Casalegno, S., D\u2019Annunzio, R., Haapanen, R., Kempeneers, P., Lindquist, E., Pekkarinen, A., Wilson, A.M., and Zurita-Milla, R. (2014, January 10\u201313). Teaching spatiotemporal analysis and efficient data processing in open source environment. Proceedings of the 3rd Open Source Geospatial Research & Education Symposium, Helsinki, Finland."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"110228","DOI":"10.1016\/j.jenvman.2020.110228","article-title":"Comprehensive evaluation of a spatio-temporal gap filling algorithm: Using remotely sensed precipitation, LST and ET data","volume":"261","author":"Siabi","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sarafanov, M., Kazakov, E., Nikitin, N.O., and Kalyuzhnaya, A.V. (2020). A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI. Remote Sens., 12.","DOI":"10.3390\/rs12233865"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.3390\/rs6043123","article-title":"Window regression: A spatial-temporal analysis to estimate pixels classified as low-quality in MODIS NDVI time series","volume":"6","author":"Epiphanio","year":"2014","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1080\/15481603.2017.1286726","article-title":"Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring","volume":"54","author":"Mondal","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1109\/TGRS.2017.2785240","article-title":"Predicting missing values in spatio-temporal remote sensing data","volume":"56","author":"Gerber","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.010","article-title":"Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States","volume":"206","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111718","DOI":"10.1016\/j.rse.2020.111718","article-title":"Spatially and temporally complete Landsat reflectance time series modelling: The fill-and-fit approach","volume":"241","author":"Yan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tang, Z., Adhikari, H., Pellikka, P.K., and Heiskanen, J. (October, January 26). Producing a Gap-free Landsat Time Series for the Taita Hills, Southeastern Kenya. Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324671"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5228","DOI":"10.1109\/JSTARS.2017.2760202","article-title":"A deep-learning-based forecasting ensemble to predict missing data for remote sensing analysis","volume":"10","author":"Das","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1109\/TGRS.2018.2810208","article-title":"Missing data reconstruction in remote sensing image with a unified spatial\u2013temporal\u2013spectral deep convolutional neural network","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Qiu, S., Lin, Y., Shang, R., Zhang, J., Ma, L., and Zhu, Z. (2019). Making Landsat time series consistent: Evaluating and improving Landsat analysis ready data. Remote Sens., 11.","DOI":"10.3390\/rs11010051"},{"key":"ref_55","unstructured":"Atto, A., Bovolo, F., and Bruzzone, L. (2022). Change Detection and Image Time-Series Analysis 2: Supervised Methods, John Wiley & Sons."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","article-title":"Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors","volume":"150","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Irish, R.R. (2000, January 24\u201326). Landsat 7 automatic cloud cover assessment. Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. Proceedings of the International Society for Optics and Photonics, Orlando, FL, USA.","DOI":"10.1117\/12.410358"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1111\/bmsp.12198","article-title":"A new quantile estimator with weights based on a subsampling approach","volume":"73","author":"Navruz","year":"2020","journal-title":"Br. J. Math. Stat. Psychol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.isprsjprs.2009.02.003","article-title":"Accuracy assessment of digital elevation models by means of robust statistical methods","volume":"64","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1080\/00031305.1996.10473566","article-title":"Sample quantiles in statistical packages","volume":"50","author":"Hyndman","year":"1996","journal-title":"Am. Stat."},{"key":"ref_62","unstructured":"Beygelzimer, A., Kakadet, S., Langford, J., Arya, S., Mount, D., Li, S., and Li, M.S. (2019, February 16). Package \u2018FNN\u2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/FNN\/FNN.pdf."},{"key":"ref_63","unstructured":"Team, R.C. (2013, September 25). R: A Language and Environment for Statistical Computing: R Foundation for Statistical Computing. Available online: https:\/\/www.r-project.org\/."},{"key":"ref_64","unstructured":"Kuhn, M. (2015, August 06). A Short Introduction to the caret Package: R Foundation for Statistical Computing. Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/vignettes\/caret.html."},{"key":"ref_65","unstructured":"Yan, L., and Roy, D.P. (2020). SAMSTS Satellite Time Series Gap Filling Source Codes-Landsat, South Dakota State University."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1080\/01621459.1993.10476408","article-title":"Alternatives to the median absolute deviation","volume":"88","author":"Rousseeuw","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.jesp.2013.03.013","article-title":"Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median","volume":"49","author":"Leys","year":"2013","journal-title":"J. Exp. Soc. Psychol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1111\/j.0006-341X.2003.00125.x","article-title":"Incorporation of clustering effects for the Wilcoxon rank sum test: A large-sample approach","volume":"59","author":"Rosner","year":"2003","journal-title":"Biometrics"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0895-4356(98)00168-1","article-title":"Increasing physicians\u2019 awareness of the impact of statistics on research outcomes: Comparative power of the t-test and Wilcoxon rank-sum test in small samples applied research","volume":"52","author":"Bridge","year":"1999","journal-title":"J. Clin. Epidemiol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","unstructured":"Pipia, L., Amin, E., Belda, S., Salinero-Delgado, M., and Verrelst, J. (2021). Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13030403"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, X., Li, N., and Pan, Y. (2020). Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai\u2013Tibetan Plateau. Remote Sens., 12.","DOI":"10.3390\/rs12071077"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.5194\/bg-10-4055-2013","article-title":"A comparison of methods for smoothing and gap filling time series of remote sensing observations; application to MODIS LAI products","volume":"10","author":"Kandasamy","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111901","DOI":"10.1016\/j.rse.2020.111901","article-title":"Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud","volume":"247","author":"Maneta","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_79","unstructured":"Dong, Y., Liang, T., Zhang, Y., and Du, B. (2020). Spectral-Spatial Weighted Kernel Manifold Embedded Distribution Alignment for Remote Sensing Image Classification. IEEE Trans. Cybern., 1\u201313."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1080\/014311600750037499","article-title":"Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details","volume":"21","author":"Liu","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.rse.2003.08.004","article-title":"Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI\u2013rainfall relationship","volume":"88","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/LGRS.2013.2284282","article-title":"An Improved Adaptive Intensity\u2013Hue\u2013Saturation Method for the Fusion of Remote Sensing Images","volume":"11","author":"Leung","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhou, Z.G., and Tang, P. (2016, January 10\u201315). Improving time series anomaly detection based on exponentially weighted moving average (EWMA) of season-trend model residuals. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729882"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2018.07.030","article-title":"Hyperspectral pansharpening via improved PCA approach and optimal weighted fusion strategy","volume":"315","author":"Li","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_85","first-page":"4","article-title":"Practical machine learning tools and techniques","volume":"2","author":"Witten","year":"2005","journal-title":"Data Min."},{"key":"ref_86","unstructured":"Hechenbichler, K., and Schliep, K. (2004). Weighted k-nearest-neighbor techniques and ordinal classification. Int. J. Chem. Mol. Eng."},{"key":"ref_87","unstructured":"Schliep, K., Hechenbichler, K., and Schliep, M.K. (2016, August 29). Package \u2018kknn\u2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/kknn\/kknn.pdf."},{"key":"ref_88","unstructured":"Michie, D., Spiegelhalter, D.J., and Taylor, C.C. (1994). Machine Learning, Neural and Statistical Classification, Ellis Horwood."},{"key":"ref_89","unstructured":"Pyle, D. (1999). Data Preparation for Data Mining, Morgan Kaufmann."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:56:49Z","timestamp":1760169409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,31]]},"references-count":89,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14010172"],"URL":"https:\/\/doi.org\/10.3390\/rs14010172","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,31]]}}}