{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:30:11Z","timestamp":1762507811911,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T00:00:00Z","timestamp":1564099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000159","name":"Natural Resources Canada","doi-asserted-by":"publisher","award":["GRIP program"],"award-info":[{"award-number":["GRIP program"]}],"id":[{"id":"10.13039\/501100000159","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A key challenge in developing models for the fusion of surface reflectance data across multiple satellite sensors is ensuring that they apply to both gradual vegetation phenological dynamics and abrupt land surface changes. To better model land cover spatial and temporal changes, we proposed previously a Prediction Smooth Reflectance Fusion Model (PSRFM) that combines a dynamic prediction model based on the linear spectral mixing model with a smoothing filter corresponding to the weighted average of forward and backward temporal predictions. One of the significant advantages of PSRFM is that PSRFM can model abrupt land surface changes either through optimized clusters or the residuals of the predicted gradual changes. In this paper, we expanded our approach and developed more efficient methods for clustering. We applied the new methods for dramatic land surface changes caused by a flood and a forest fire. Comparison of the model outputs showed that the new methods can capture the land surface changes more effectively. We also compared the improved PSRFM to two most popular reflectance fusion algorithms: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced version of STARFM (ESTARFM). The results showed that the improved PSRFM is more effective and outperforms STARFM and ESTARFM both visually and quantitatively.<\/jats:p>","DOI":"10.3390\/rs11151759","type":"journal-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T08:45:39Z","timestamp":1564130739000},"page":"1759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Improvement of Clustering Methods for Modelling Abrupt Land Surface Changes in Satellite Image Fusions"],"prefix":"10.3390","volume":"11","author":[{"given":"Detang","family":"Zhong","sequence":"first","affiliation":[{"name":"Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6th floor, 560 Rochester Street, Ottawa, ON K1S 5K2, Canada"}]},{"given":"Fuqun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6th floor, 560 Rochester Street, Ottawa, ON K1S 5K2, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,26]]},"reference":[{"key":"ref_1","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_2","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_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"6213","DOI":"10.1080\/01431161.2014.951097","article-title":"Spatio-temporal reflectance fusion via unmixing: Accounting for both phenological and land-cover changes","volume":"35","author":"Huang","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"096095","DOI":"10.1117\/1.JRS.9.096095","article-title":"Spatiotemporal image-fusion model for enhancing temporal resolution of Landsat-8 surface reflectance images using MODIS images","volume":"9","author":"Hazaymeh","year":"2015","journal-title":"J. Appl. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Kwan, C., Budavari, B., Gao, F., and Zhu, X. (2018). A Hybrid Color Mapping Approach to Fusing MODIS and Landsat Images for Forward Prediction. Remote Sens., 10.","key":"ref_8","DOI":"10.3390\/rs10040520"},{"doi-asserted-by":"crossref","unstructured":"Wang, J., and Huang, B. (2017). A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis. Remote Sens., 9.","key":"ref_9","DOI":"10.3390\/rs9100990"},{"doi-asserted-by":"crossref","unstructured":"Zhong, D., and Zhou, F. (2018). A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images. Remote Sens., 10.","key":"ref_10","DOI":"10.3390\/rs10091371"},{"doi-asserted-by":"crossref","unstructured":"Sekrecka, A., and Kedzierski, M. (2018). Integration of Satellite Data with High resolution Ratio: Improvement od Spectral Quality with Preserving Spatial Details. Sensors, 13.","key":"ref_11","DOI":"10.3390\/s18124418"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2017.10.046","article-title":"Spatio-temporal fusion for daily Sentinel-2 images","volume":"204","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2014.09.012","article-title":"A comparison of STARFM and an unmixing based algorithm for Landsat and MODIS data fusion","volume":"156","author":"Gevaert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat\u2013MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ."},{"unstructured":"Hirvonen, R.A. (1971). Adjustments by Least Squares in Geodesy and Photogrammetry, Ungar.","key":"ref_15"},{"doi-asserted-by":"crossref","unstructured":"Wolf, P.R. (1995). Survey Measurement Adjustments by Least Squares. The Surveying Handbook, Springer.","key":"ref_16","DOI":"10.1007\/978-1-4615-2067-2_16"},{"doi-asserted-by":"crossref","unstructured":"Wu, M., Niu, Z., Wang, C., Wu, C., and Wang, L. (2012). Use of modis and landsat time series data to generate high-resolution temporal synthetic landsat data using a spatial and temporal reflectance fusion model. J. Appl. Remote Sens., 6.","key":"ref_17","DOI":"10.1117\/1.JRS.6.063507"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"24002","DOI":"10.3390\/s150924002","article-title":"Generating daily synthetic landsat imagery by combining landsat and modis data","volume":"15","author":"Wu","year":"2015","journal-title":"Sensors"},{"key":"ref_19","first-page":"198","article-title":"Hyperspectral Image Classification Using Unsupervised Algorithms","volume":"7","year":"2016","journal-title":"Int. J. Adv. Computer Sci. Appl."},{"unstructured":"Emelyanova, I., McVicar, T., Van Niel, T., Li, L., and Van Dijk, A. (2013). Landsat and MODIS Data for the Lower Gwydir Catchment Study Site, CSIRO. v3.","key":"ref_20"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.aqpro.2015.02.019","article-title":"A Review of Quality Metrics for Fused Image","volume":"4","author":"Pa","year":"2015","journal-title":"Aquat. Procedia"},{"key":"ref_23","first-page":"1101","article-title":"Methods for Image Fusion Quality Assessment\u2014A Review, Comparison and Analysis","volume":"XXXVII","author":"Zhang","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:09:55Z","timestamp":1760188195000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,26]]},"references-count":23,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11151759"],"URL":"https:\/\/doi.org\/10.3390\/rs11151759","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,7,26]]}}}