{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T10:48:42Z","timestamp":1744800522872,"version":"3.37.3"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T00:00:00Z","timestamp":1552608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771448","41571342"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of State Key Laboratory of Earth Surface Processes and Resource Ecology","award":["2017-ZY-03"]},{"name":"Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People\u2019s Republic of China","award":["UDC2017030212"]},{"name":"Opening Projects of Beijing Advanced Innovation Center for Future Urban Design","award":["UDC201650100"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the \u201caggregation-then-disaggregation\u201d strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous.<\/jats:p>","DOI":"10.3390\/rs11060634","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T08:06:55Z","timestamp":1552896415000},"page":"634","source":"Crossref","is-referenced-by-count":14,"title":["Object-Based Window Strategy in Thermal Sharpening"],"prefix":"10.3390","volume":"11","author":[{"given":"Haiping","family":"Xia","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-7303","authenticated-orcid":false,"given":"Yunhao","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Jinling","family":"Quan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1016\/j.agrformet.2009.05.016","article-title":"Advances in thermal infrared remote sensing for land surface modeling","volume":"149","author":"Kustas","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2006.11.032","article-title":"Effect of remote sensing spatial resolution on interpreting tower-based flux observations","volume":"112","author":"Li","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.rse.2004.02.020","article-title":"Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in iowa","volume":"92","author":"Kustas","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.rse.2012.06.009","article-title":"Applications of a remote sensing-based two-source energy balance algorithm for mapping surface fluxes without in situ air temperature observations","volume":"124","author":"Cammalleri","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/JSTARS.2010.2070871","article-title":"Maximum nighttime urban heat island (uhi) intensity simulation by integrating remotely sensed data and meteorological observations","volume":"4","author":"Zhou","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2014.03.037","article-title":"Multi-temporal trajectory of the urban heat island centroid in beijing, china based on a gaussian volume model","volume":"149","author":"Quan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/S0034-4257(03)00007-5","article-title":"Satellite-measured growth of the urban heat island of houston, texas","volume":"85","author":"Streutker","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"627","article-title":"A simple method based on the thermal anomaly index to detect industrial heat sources","volume":"73","author":"Xia","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rse.2015.10.002","article-title":"An initial comparison of the thermal anomaly detection products of modis and viirs in their observation of indonesian volcanic activity","volume":"171","author":"Blackett","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2012.12.014","article-title":"Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats","volume":"131","author":"Zhan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2019.02.006","article-title":"Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures","volume":"224","author":"Xia","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/TGRS.2010.2060342","article-title":"Sharpening thermal imageries: A generalized theoretical framework from an assimilation perspective","volume":"49","author":"Zhan","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.3390\/rs4113287","article-title":"A data mining approach for sharpening thermal satellite imagery over land","volume":"4","author":"Gao","year":"2012","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S0034-4257(03)00036-1","article-title":"Estimating subpixel surface temperatures and energy fluxes from the vegetation index\u2013radiometric temperature relationship","volume":"85","author":"Kustas","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.rse.2006.10.006","article-title":"A vegetation index based technique for spatial sharpening of thermal imagery","volume":"107","author":"Agam","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1029\/2008GL036544","article-title":"Disaggregation of goes land surface temperatures using surface emissivity","volume":"36","author":"Inamdar","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"547","DOI":"10.14358\/PERS.75.5.547","article-title":"An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis","volume":"75","author":"Nichol","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2009.07.017","article-title":"Downscaling avhrr land surface temperatures for improved surface urban heat island intensity estimation","volume":"113","author":"Stathopoulou","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from tm imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.rse.2011.01.004","article-title":"Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on aster and modis temperature products over a heterogeneous area","volume":"115","author":"Yang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1016\/j.rse.2011.03.008","article-title":"High-resolution urban thermal sharpener (huts)","volume":"115","author":"Dominguez","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (savi)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/LGRS.2013.2257668","article-title":"Downscaling geostationary land surface temperature imagery for urban analysis","volume":"10","author":"Keramitsoglou","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2016.03.006","article-title":"Downscaling land surface temperatures at regional scales with random forest regression","volume":"178","author":"Hutengs","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xia, H., Chen, Y., Zhao, Y., and Chen, Z. (2018). \u201cRegression-then-fusion\u201d or \u201cfusion-then-regression\u201d? A theoretical analysis for generating high spatiotemporal resolution land surface temperatures. Remote Sens., 10.","DOI":"10.3390\/rs10091382"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cao, C., Pan, X., Li, X., and Zhu, X. (2017). Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression. Remote Sens., 9.","DOI":"10.3390\/rs9080789"},{"key":"ref_27","first-page":"178","article-title":"Evaluating a thermal image sharpening model over a mixed agricultural landscape in india","volume":"13","author":"Jeganathan","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.3390\/rs6042845","article-title":"A combination of tsharp and thin plate spline interpolation for spatial sharpening of thermal imagery","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1109\/TGRS.2016.2608987","article-title":"Localization or globalization? Determination of the optimal regression window for disaggregation of land surface temperature","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, M.Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yao, J., Boben, M., Fidler, S., and Urtasun, R. (2015, January 7\u201312). Real-time coarse-to-fine topologically preserving segmentation. Proceedings of the Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298913"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"Slic superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lillo-Saavedra, M., Garc\u00eda-Pedrero, A., Merino, G., and Gonzalo-Mart\u00edn, C. (2018). Ts2urf: A new method for sharpening thermal infrared satellite imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020249"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1016\/j.sbspro.2015.06.210","article-title":"New method for image segmentation","volume":"195","author":"Lalaoui","year":"2015","journal-title":"Procedia Soc. Behav. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/LGRS.2014.2312032","article-title":"Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data","volume":"11","author":"Sobrino","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2017.12.003","article-title":"An integrated model for generating hourly landsat-like land surface temperatures over heterogeneous landscapes","volume":"206","author":"Quan","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/6\/634\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T01:57:24Z","timestamp":1736215044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/6\/634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,15]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["rs11060634"],"URL":"https:\/\/doi.org\/10.3390\/rs11060634","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,3,15]]}}}