{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T11:18:32Z","timestamp":1775733512516,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2013,10,22]],"date-time":"2013-10-22T00:00:00Z","timestamp":1382400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.<\/jats:p>","DOI":"10.3390\/rs5105346","type":"journal-article","created":{"date-parts":[[2013,10,23]],"date-time":"2013-10-23T07:45:42Z","timestamp":1382514342000},"page":"5346-5368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data"],"prefix":"10.3390","volume":"5","author":[{"given":"Wei","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ainong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaan","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1472-5259","authenticated-orcid":false,"given":"Jinhu","family":"Bian","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7333-4325","authenticated-orcid":false,"given":"Guangbin","family":"Lei","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Qin","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengquan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geographic Sciences, University of Maryland, College Park, MD 20741, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/0034-4257(94)90013-2","article-title":"How unique are spectral signatures?","volume":"49","author":"Price","year":"1994","journal-title":"Remote Sens. Environ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2005.09.007","article-title":"Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers","volume":"100","author":"Sakamoto","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"110","DOI":"10.3390\/rs5010110","article-title":"Snow cover maps from MODIS images at 250 m resolution, Part 1: Algorithm description","volume":"5","author":"Notarnicola","year":"2013","journal-title":"Remote Sens"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2013.04.015","article-title":"Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study","volume":"136","author":"Zhou","year":"2013","journal-title":"Remote Sens. Environ"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/S0034-4257(03)00037-3","article-title":"Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge","volume":"85","author":"Rees","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_6","first-page":"819","article-title":"Development of large-area land cover and forest change indicators using multi-sensor Landsat imagery: Application to the Humber River Basin, Canada","volume":"13","author":"Lier","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.3390\/rs3061067","article-title":"Integrated Landsat image analysis and hydrologic modeling to detect impacts of 25-year classification change on surface runoff in a Philippine watershed","volume":"3","author":"Santillan","year":"2011","journal-title":"Remote Sens"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s10661-007-9929-2","article-title":"Coastal dynamic and shoreline mapping: Multi-sources spatial data analysis in Semarang Indonesia","volume":"142","author":"Marfai","year":"2008","journal-title":"Environ. Monit. Assess"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.3390\/rs3091943","article-title":"A multi-resolution multi-temporal technique for detecting and mapping deforestation in the Brazilian Amazon rainforest","volume":"3","author":"Arai","year":"2011","journal-title":"Remote Sens"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1016\/j.rse.2009.04.011","article-title":"Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics","volume":"113","author":"Kaiser","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1080\/01431160903505286","article-title":"Using MERIS fused images for classification mapping and vegetation status assessment in heterogeneous landscapes","volume":"32","author":"Clevers","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3724\/SP.J.1010.2012.00080","article-title":"A model for spatial and temporal data fusion (In Chinese)","volume":"31","author":"Wu","year":"2012","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0034-4257(00)00153-X","article-title":"Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses","volume":"75","author":"Maselli","year":"2001","journal-title":"Remote Sens. Environ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2007.04.004","article-title":"Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series","volume":"112","author":"Busetto","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/LGRS.2005.861699","article-title":"Spatial resolution improvement by merging MERIS-ETM images for coastal water monitoring","volume":"3","author":"Polidori","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/36.763276","article-title":"Unmixing-based multisensor multiresolution image fusion","volume":"37","author":"Zhukov","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1080\/01431169308904402","article-title":"Linear mixing and the estimation of ground cover proportions","volume":"14","author":"Settle","year":"1993","journal-title":"Int. J. Remote Sens"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/0034-4257(95)00100-F","article-title":"Unmixing multiple classification type reflectances from coarse spatial resolution satellite data","volume":"54","author":"Oleson","year":"1995","journal-title":"Remote Sens. Environ"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/LGRS.2008.919685","article-title":"Unmixing-based Landsat TM and MERIS FR data fusion","volume":"5","author":"Clevers","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_24","unstructured":"Strobl, J., Blaschke, T., and Griesebner, G. (2000). Angewandte Geographische Informations-Verarbeitung XII, Wichmann Verlag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0034-4257(02)00085-8","article-title":"Achieving sub-pixel geolocation accuracy in support of MODIS land science","volume":"83","author":"Wolfe","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_28","unstructured":"Wald, L (2000, January 26\u201328). Quality of High Resolution Synthesized Images: Is There a Simple Criterion?. Sophia Antipolis, France."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1080\/014311600210641","article-title":"Beware of per-pixel characterization of land cover","volume":"21","author":"Townshend","year":"2000","journal-title":"Int. J. Remote Sens"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0034-4257(01)00298-X","article-title":"Impact of sensor\u2019s point spread function on land cover characterization: Assessment and deconvolution","volume":"80","author":"Huang","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2006.06.008","article-title":"The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions","volume":"105","author":"Tan","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/S0924-2716(03)00013-3","article-title":"Image fusion\u2014The ARSIS concept and some successful implementation schemes","volume":"58","author":"Ranchin","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kohonen, T (2001). Self-Organizing Maps, Springer-Verlag. [3rd ed.].","DOI":"10.1007\/978-3-642-56927-2"},{"key":"ref_34","first-page":"132","article-title":"Multitemporal fusion of Landsat\/TM and ENVISAT\/MERIS for crop monitoring","volume":"23","author":"Alonso","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/LGRS.2011.2120591","article-title":"Regularized multiresolution spatial unmixing for ENVISAT\/MERIS and Landsat\/TM image fusion","volume":"8","author":"Alonso","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in spectral mixture analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/10\/5346\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:50:03Z","timestamp":1760219403000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/10\/5346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,10,22]]},"references-count":36,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2013,10]]}},"alternative-id":["rs5105346"],"URL":"https:\/\/doi.org\/10.3390\/rs5105346","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,10,22]]}}}