{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:50:46Z","timestamp":1772797846357,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CaliforniaView under the USGS AmericaView grant","award":["CaliforniaView"],"award-info":[{"award-number":["CaliforniaView"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Long-term record of fine spatial resolution remote sensing datasets is critical for monitoring and understanding global environmental change, especially with regard to fine scale processes. However, existing freely available global land surface observations are limited by medium to coarse resolutions (e.g., 30 m Landsat) or short time spans (e.g., five years for 10 m Sentinel-2). Here we developed a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, to leverage the overlapping Landsat and Sentinel-2 observations during 2016\u20132019, and reconstruct 10 m Sentinel-2 like imagery from 30 m historical Landsat archives. Our tests with both simulated data and actual Landsat\/Sentinel-2 imagery showed that the GAN-based fusion method could accurately reconstruct synthetic Landsat data at an effective resolution very close to that of the real Sentinel-2 observations. We applied the GAN-based model to two dynamic systems: (1) land over dynamics including phenology change, cropping rotation, and water inundation; and (2) human landscape changes such as airport construction, coastal expansion, and urbanization, via historical reconstruction of 10 m Landsat observations from 1985 to 2018. The resulting comparison further validated the robustness and efficiency of our proposed framework. Our pilot study demonstrated the promise of transforming 30 m historical Landsat data into a 10 m Sentinel-2-like archive with advanced data fusion. This will enhance Landsat and Sentinel-2 data science, facilitate higher resolution land cover and land use monitoring, and global change research.<\/jats:p>","DOI":"10.3390\/rs13020167","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T20:45:42Z","timestamp":1609965942000},"page":"167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive"],"prefix":"10.3390","volume":"13","author":[{"given":"Bin","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0308-2120","authenticated-orcid":false,"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, Davis, CA 95616-8562, USA"}]},{"given":"Yufang","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s10021-007-9057-4","article-title":"Interactions across spatial scales among forest dieback, fire, and erosion in northern New Mexico landscapes","volume":"10","author":"Allen","year":"2007","journal-title":"Ecosystems"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1641\/0006-3568(2005)055[0115:ASOIOR]2.0.CO;2","article-title":"A synthesis of information on rapid land-cover change for the period 1981\u20132000","volume":"55","author":"Lepers","year":"2005","journal-title":"BioScience"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/j.rse.2007.07.004","article-title":"Landsat continuity: Issues and opportunities for land cover monitoring","volume":"112","author":"Wulder","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TGRS.2016.2580576","article-title":"Spatially and temporally weighted regression: A novel method to produce continuous cloud-free Landsat imagery","volume":"55","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1111\/j.1475-4959.2007.232_3.x","article-title":"Urbanization and global environmental change: Local effects of urban warming","volume":"173","author":"Grimmond","year":"2007","journal-title":"Geogr. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.5194\/essd-12-1953-2020","article-title":"Earth transformed: Detailed mapping of global human modification from 1990 to 2017","volume":"12","author":"Theobald","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2011.06.021","article-title":"Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing","volume":"117","author":"Michishita","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2016.01.003","article-title":"Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery","volume":"113","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2019.08.006","article-title":"An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations","volume":"156","author":"Chen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.compag.2016.12.006","article-title":"Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques","volume":"134","author":"Gilbertson","year":"2017","journal-title":"Comput. Electr. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.scib.2019.12.007","article-title":"Mapping essential urban land use categories in China (EULUC-China): Preliminary results for 2018","volume":"65","author":"Gong","year":"2020","journal-title":"Sci. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., and Gill, E. (2019). The first wetland inventory map of newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform. Remote Sens., 11.","DOI":"10.3390\/rs11010043"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2017.03.021","article-title":"Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index","volume":"195","author":"Korhonen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.3390\/rs70201798","article-title":"Comparison of spatiotemporal fusion models: A review","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S1566-2535(01)00037-9","article-title":"Using the discrete wavelet frame transform to merge Landsat tm and spot panchromatic images","volume":"3","author":"Li","year":"2002","journal-title":"Inf. Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/TGRS.2010.2067219","article-title":"A new pan-sharpening method using a compressed sensing technique","volume":"49","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, H.K., and Roy, D.P. (2016). Computationally inexpensive Landsat 8 operational land imager (OLI) pansharpening. Remote Sens., 8.","DOI":"10.3390\/rs8030180"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TGRS.2012.2186638","article-title":"Spatiotemporal reflectance fusion via sparse representation","volume":"50","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.rse.2018.04.042","article-title":"Stair: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-\/gap-free surface reflectance product","volume":"214","author":"Luo","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2015.11.005","article-title":"Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in china","volume":"172","author":"Shen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111425","DOI":"10.1016\/j.rse.2019.111425","article-title":"Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product","volume":"235","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1109\/TGRS.2017.2683444","article-title":"Fusion of Landsat 8 OLI and Sentinel-2 MSI data","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., and Mu Lee, K. (2016, January 27\u201330). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pouliot, D., Latifovic, R., Pasher, J., and Duffe, J. (2018). Landsat super-resolution enhancement using convolution neural networks and sentinel-2 for training. Remote Sens., 10.","DOI":"10.3390\/rs10030394"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201322). Residual dense network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016, January 8\u201316). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.isprsjprs.2018.09.018","article-title":"Super-resolution of sentinel-2 images: Learning a globally applicable deep neural network","volume":"146","author":"Lanaras","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Change Loy, C. (2018, January 8\u201314). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_43","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A universal image quality index","volume":"9","author":"Wang","year":"2002","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201ccompletely blind\u201d image quality analyzer","volume":"20","author":"Mittal","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_47","unstructured":"Chan, R.W., and Goldsmith, P.B. (2000, January 8\u201311). A psychovisually-based image quality evaluator for jpeg images. Proceedings of the 2000 IEEE International Conference on Systems, Man and Cybernetics, Nashville, TN, USA."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A critical comparison among pansharpening algorithms","volume":"53","author":"Vivone","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TGRS.2007.907604","article-title":"Optimal mmse pan sharpening of very high resolution multispectral images","volume":"46","author":"Garzelli","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.rse.2015.06.003","article-title":"Downscaling MODIS images with area-to-point regression kriging","volume":"166","author":"Wang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Masi, G., Cozzolino, D., Verdoliva, L., and Scarpa, G. (2016). Pansharpening by convolutional neural networks. Remote Sens., 8.","DOI":"10.3390\/rs8070594"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5443","DOI":"10.1109\/TGRS.2018.2817393","article-title":"Target-adaptive CNN-based pansharpening","volume":"56","author":"Scarpa","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Dataset and study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Scholkopf, B., and Hirsch, M. (2017, January 22\u201329). Enhancenet: Single image super-resolution through automated texture synthesis. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.481"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Dahl, R., Norouzi, M., and Shlens, J. (2017, January 22\u201329). Pixel recursive super resolution. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.581"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Blau, Y., and Michaeli, T. (2018, January 18\u201322). The perception-distortion tradeoff. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00652"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1080\/17538947.2016.1235621","article-title":"A hierarchical spatiotemporal adaptive fusion model using one image pair","volume":"10","author":"Chen","year":"2017","journal-title":"Int. J. Digit. Earth"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:07:37Z","timestamp":1760159257000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,6]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020167"],"URL":"https:\/\/doi.org\/10.3390\/rs13020167","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,6]]}}}