Abowarda, A. S., Bai, L., Zhang, C., Long, D., Li, X., Huang, Q., and Sun, Z.: Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale, Remote Sens. Environ., 255, 112301, https://doi.org/10.1016/j.rse.2021.112301, 2021.
Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Marais Sicre, C., Le Dantec, V., and Demarez, V.: Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data, Remote Sens. Environ., 184, 668–681, https://doi.org/10.1016/j.rse.2016.07.030, 2016.
Bauer-Marschallinger, B. and Falkner, K.: Wasting petabytes: A survey of the Sentinel-2 UTM tiling grid and its spatial overhead, ISPRS J. Photogramm., 202, 682–690, https://doi.org/10.1016/j.isprsjprs.2023.07.015, 2023.
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and Friedl, M. A.: Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685, https://doi.org/10.1016/j.rse.2020.111685, 2020.
Brooks, E. B., Thomas, V. A., Wynne, R. H., and Coulston, J. W.: Fitting the Multitemporal Curve: A Fourier Series Approach to the Missing Data Problem in Remote Sensing Analysis, IEEE T. Geosci. Remote, 50, 3340–3353, https://doi.org/10.1109/TGRS.2012.2183137, 2012.
Cao, Z., Chen, S., Gao, F., and Li, X.: Improving phenological monitoring of winter wheat by considering sensor spectral response in spatiotemporal image fusion, Phys. Chem. Earth Pt. A/B/C, 116, 102859, https://doi.org/10.1016/j.pce.2020.102859, 2020.
Carrasco, L., O'Neil, A., Morton, R., and Rowland, C.: Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine, Remote Sens.-Basel, 11, 288, https://doi.org/10.3390/rs11030288, 2019.
Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K.: Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States, Remote Sens. Environ., 221, 274–285, https://doi.org/10.1016/j.rse.2018.11.012, 2019.
Che, X., Zhang, H. K., Li, Z. B., Wang, Y., Sun, Q., Luo, D., and Wang, H.: Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks, ISPRS J. Photogramm., 212, 73–95, https://doi.org/10.1016/j.isprsjprs.2024.04.021, 2024.
Chen, B., Huang, B., and Xu, B.: Multi-source remotely sensed data fusion for improving land cover classification, ISPRS J. Photogramm., 124, 27–39, https://doi.org/10.1016/j.isprsjprs.2016.12.008, 2017.
Chen, B., Chen, L., Huang, B., Michishita, R., and Xu, B.: Dynamic monitoring of the Poyang Lake wetland by integrating Landsat and MODIS observations, ISPRS J. Photogramm., 139, 75–87, https://doi.org/10.1016/j.isprsjprs.2018.02.021, 2018.
Chen, J., Zhu, X., Vogelmann, J. E., Gao, F., and Jin, S.: A simple and effective method for filling gaps in Landsat ETM+ SLC-off images, Remote Sens. Environ., 115, 1053–1064, https://doi.org/10.1016/j.rse.2010.12.010, 2011.
Chen, S., Wang, J., and Gong, P.: ROBOT: A spatiotemporal fusion model toward seamless data cube for global remote sensing applications, Remote Sens. Environ., 294, 113616, https://doi.org/10.1016/j.rse.2023.113616, 2023.
Chen, S., Wang, J., Liu, Q., Liang, X., Liu, R., Qin, P., Yuan, J., Wei, J., Yuan, S., Huang, H., and Gong, P.: Global 30 m seamless data cube (2000–2022) of land surface reflectance generated from Landsat-5,7,8,9 and MODIS Terra constellations, Pengcheng Laboratory [data set], https://doi.org/10.12436/SDC30.26.20240506, 2024.
Chen, Y., Cao, R., Chen, J., Liu, L., and Matsushita, B.: A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter, ISPRS J. Photogramm., 180, 174–190, https://doi.org/10.1016/j.isprsjprs.2021.08.015, 2021.
Chuvieco, E., Ventura, G., Martín, M. P., and Gómez, I.: Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping, Remote Sens. Environ., 94, 450–462, https://doi.org/10.1016/j.rse.2004.11.006, 2005.
Cihlar, J., Manak, D., and D'Iorio, M.: Evaluation of compositing algorithms for AVHRR data over land, IEEE T. Geosci. Remote, 32, 427–437, https://doi.org/10.1109/36.295057, 1994.
Claverie, M.: Evaluation of surface reflectance bandpass adjustment techniques, ISPRS J. Photogramm., 198, 210–222, https://doi.org/10.1016/j.isprsjprs.2023.03.011, 2023.
Claverie, M., Vermote, E., Franch, B., He, T., Hagolle, O., Kadiri, M., and Masek, J.: Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions, Remote Sens.-Basel, 7, 12057–12075, https://doi.org/10.3390/rs70912057, 2015.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., and Justice, C.: The Harmonized Landsat and Sentinel-2 surface reflectance data set, Remote Sens. Environ., 219, 145–161, https://doi.org/10.1016/j.rse.2018.09.002, 2018.
Crawford, C. J., Roy, D. P., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Micijevic, E., Schmidt, G., Anderson, C., Anderson, M., Bouchard, M., Cook, B., Dittmeier, R., Howard, D., Jenkerson, C., Kim, M., Kleyians, T., Maiersperger, T., Mueller, C., Neigh, C., Owen, L., Page, B., Pahlevan, N., Rengarajan, R., Roger, J.-C., Sayler, K., Scaramuzza, P., Skakun, S., Yan, L., Zhang, H. K., Zhu, Z., and Zahn, S.: The 50-year Landsat collection 2 archive, Science of Remote Sensing, 8, 100103, https://doi.org/10.1016/j.srs.2023.100103, 2023.
Dash, J., Jeganathan, C., and Atkinson, P. M.: The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India, Remote Sens. Environ., 114, 1388–1402, https://doi.org/10.1016/j.rse.2010.01.021, 2010.
Defourny, P., Bontemps, S., Bellemans, N., Cara, C., Dedieu, G., Guzzonato, E., Hagolle, O., Inglada, J., Nicola, L., Rabaute, T., Savinaud, M., Udroiu, C., Valero, S., Bégué, A., Dejoux, J.-F., El Harti, A., Ezzahar, J., Kussul, N., Labbassi, K., Lebourgeois, V., Miao, Z., Newby, T., Nyamugama, A., Salh, N., Shelestov, A., Simonneaux, V., Traore, P. S., Traore, S. S., and Koetz, B.: Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world, Remote Sens. Environ., 221, 551–568, https://doi.org/10.1016/j.rse.2018.11.007, 2019.
Dwyer, J. L., Roy, D. P., Sauer, B., Jenkerson, C. B., Zhang, H. K., and Lymburner, L.: Analysis Ready Data: Enabling Analysis of the Landsat Archive, Remote Sens.-Basel, 10, 1363, https://doi.org/10.3390/rs10091363, 2018.
Frantz, D., Röder, A., Stellmes, M., and Hill, J.: Phenology-adaptive pixel-based compositing using optical earth observation imagery, Remote Sens. Environ., 190, 331–347, https://doi.org/10.1016/j.rse.2017.01.002, 2017.
Gao, F., Masek, J., Schwaller, M., and Hall, F.: On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance, IEEE T. Geosci. Remote, 44, 2207–2218, https://doi.org/10.1109/TGRS.2006.872081, 2006.
Gao, H., Zhu, X., Guan, Q., Yang, X., Yao, Y., Zeng, W., and Peng, X.: cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units, IEEE T. Geosci. Remote, 60, 1–16, https://doi.org/10.1109/TGRS.2021.3080384, 2022.
Gervais, N., Buyantuev, A., and Gao, F.: Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data, Remote Sens.-Basel, 9, 99, https://doi.org/10.3390/rs9010099, 2017.
Gevaert, C. M. and García-Haro, F. J.: A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion, Remote Sens. Environ., 156, 34–44, https://doi.org/10.1016/j.rse.2014.09.012, 2015.
Gong, P., Liang, S., Carlton, E. J., Jiang, Q., Wu, J., Wang, L., and Remais, J. V.: Urbanisation and health in China, Lancet, 379, 843–852, https://doi.org/10.1016/S0140-6736(11)61878-3, 2012.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data, Int. J. Remote Sens., 34, 2607–2654, https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji, L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H., Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu, L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song, L.: Stable classification with limited sample: transferring a 30 m resolution sample set collected in 2015 to mapping 10 m resolution global land cover in 2017, Sci. Bull., 64, 370–373, https://doi.org/10.1016/j.scib.2019.03.002, 2019.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang, J., Zhang, W., and Zhou, Y.: Annual maps of global artificial impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Gong, P., Guo, H., Chen, B., Chen, F., He, G., Liang, D., Liu, Z., Sun, Z., Wu, J., Xu, Z., Yan, D., and Zhang, H.: iEarth: an interdisciplinary framework in the era of big data and AI for sustainable development, Natl. Sci. Rev., 10, nwad178, https://doi.org/10.1093/nsr/nwad178, 2023.
Goyena, H., Pérez-Goya, U., Montesino-SanMartin, M., Militino, A. F., Wang, Q., Atkinson, P. M., and Ugarte, M. D.: Unpaired spatio-temporal fusion of image patches (USTFIP) from cloud covered images, Remote Sens. Environ., 295, 113709, https://doi.org/10.1016/j.rse.2023.113709, 2023.
Griffiths, P., Nendel, C., and Hostert, P.: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping, Remote Sens. Environ., 220, 135–151, https://doi.org/10.1016/j.rse.2018.10.031, 2019.
Guo, D., Shi, W., Hao, M., and Zhu, X.: FSDAF 2.0: Improving the performance of retrieving land cover changes and preserving spatial details, Remote Sens. Environ., 248, 111973, https://doi.org/10.1016/j.rse.2020.111973, 2020.
Hansen, M. C., Roy, D. P., Lindquist, E., Adusei, B., Justice, C. O., and Altstatt, A.: A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin, Remote Sens. Environ., 112, 2495–2513, https://doi.org/10.1016/j.rse.2007.11.012, 2008.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G.: High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Hilker, T., Wulder, M. A., Coops, N. C., Linke, J., McDermid, G., Masek, J. G., Gao, F., and White, J. C.: A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS, Remote Sens. Environ., 113, 1613–1627, https://doi.org/10.1016/j.rse.2009.03.007, 2009a.
Hilker, T., Wulder, M. A., Coops, N. C., Seitz, N., White, J. C., Gao, F., Masek, J. G., and Stenhouse, G.: Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model, Remote Sens. Environ., 113, 1988–1999, https://doi.org/10.1016/j.rse.2009.05.011, 2009b.
Holben, B. N.: Characteristics of maximum-value composite images from temporal AVHRR data, Int. J. Remote Sens., 7, 1417–1434, https://doi.org/10.1080/01431168608948945, 1986.
Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., and Zhu, Z.: Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine, Remote Sens. Environ., 202, 166–176, https://doi.org/10.1016/j.rse.2017.02.021, 2017.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195–213, https://doi.org/10.1016/S0034-4257(02)00096-2, 2002.
Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I.: Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series, Remote Sens.-Basel, 9, 95, https://doi.org/10.3390/rs9010095, 2017.
Ji, L., Gong, P., Wang, J., Shi, J., and Zhu, Z.: Construction of the 500 m Resolution Daily Global Surface Water Change Database (2001–2016), Water Resour. Res., 54, 10270–10292, https://doi.org/10.1029/2018WR023060, 2018.
Jin, S., Dewitz, J., Danielson, P., Granneman, B., Costello, C., Smith, K., and Zhu, Z.: National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images, J. Remote Sens., 3, 0022, https://doi.org/10.34133/remotesensing.0022, 2023.
Khan, A., Potapov, P., Hansen, M. C., Pickens, A. H., Tyukavina, A., Serna, A. H., Uddin, K., and Ahmad, J.: Perennial snow and ice cover change from 2001 to 2021 in the Hindu-Kush Himalayan region derived from the Landsat analysis-ready data, Remote Sensing Applications: Society and Environment, 34, 101192, https://doi.org/10.1016/j.rsase.2024.101192, 2024.
Li, C., Gong, P., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang, H., Wang, Q., Li, X., Liu, X., Xu, Y., Guo, J., Liu, C., Hackman, K. O., Zhang, M., Cheng, Y., Yu, L., Yang, J., Huang, H., and Clinton, N.: The first all-season sample set for mapping global land cover with Landsat-8 data, Sci. Bull., 62, 508–515, https://doi.org/10.1016/j.scib.2017.03.011, 2017.
Li, H., Song, X.-P., Hansen, M. C., Becker-Reshef, I., Adusei, B., Pickering, J., Wang, L., Wang, L., Lin, Z., Zalles, V., Potapov, P., Stehman, S. V., and Justice, C.: Development of a 10 m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation, Remote Sens. Environ., 294, 113623, https://doi.org/10.1016/j.rse.2023.113623, 2023.
Li, J. and Roy, D.: A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring, Remote Sens.-Basel, 9, 902, https://doi.org/10.3390/rs9090902, 2017.
Li, Y., Huang, C., Hou, J., Gu, J., Zhu, G., and Li, X.: Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China, Agr. Forest Meteorol., 244–245, 82–97, https://doi.org/10.1016/j.agrformet.2017.05.023, 2017.
Liang, X., Liu, Q., Wang, J., Chen, S., and Gong, P.: Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products, Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, 2024.
Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, 2020.
Liu, H., Gong, P., Wang, J., Wang, X., Ning, G., and Xu, B.: Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 – iMap World 1.0, Remote Sens. Environ., 258, 112364, https://doi.org/10.1016/j.rse.2021.112364, 2021.
Liu, M., Yang, W., Zhu, X., Chen, J., Chen, X., Yang, L., and Helmer, E. H.: An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series, Remote Sens. Environ., 227, 74–89, https://doi.org/10.1016/j.rse.2019.03.012, 2019.
Liu, S., Zhou, J., Qiu, Y., Chen, J., Zhu, X., and Chen, H.: The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation, Remote Sens. Environ., 279, 113111, https://doi.org/10.1016/j.rse.2022.113111, 2022.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K., Ziegler, A. D., Chen, A., Gong, P., Chen, J., Hu, G., Chen, Y., Wang, S., Wu, Q., Huang, K., Estes, L., and Zeng, Z.: High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015, Nat. Sustain., 3, 564–570, https://doi.org/10.1038/s41893-020-0521-x, 2020.
Malambo, L. and Heatwole, C. D.: A Multitemporal Profile-Based Interpolation Method for Gap Filling Nonstationary Data, IEEE T. Geosci. Remote, 54, 252–261, https://doi.org/10.1109/TGRS.2015.2453955, 2016.
Markham, B. L. and Helder, D. L.: Forty-year calibrated record of earth-reflected radiance from Landsat: A review, Remote Sens. Environ., 122, 30–40, https://doi.org/10.1016/j.rse.2011.06.026, 2012.
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., Gao, F., Kutler, J., and Lim, T.-K.: A Landsat Surface Reflectance Dataset for North America, 1990–2000, IEEE Geosci. Remote S., 3, 68–72, https://doi.org/10.1109/LGRS.2005.857030, 2006.
Masek, J. G., Wulder, M. A., Markham, B., McCorkel, J., Crawford, C. J., Storey, J., and Jenstrom, D. T.: Landsat 9: Empowering open science and applications through continuity, Remote Sens. Environ., 248, 111968, https://doi.org/10.1016/j.rse.2020.111968, 2020.
Mizuochi, H., Hiyama, T., Ohta, T., Fujioka, Y., Kambatuku, J. R., Iijima, M., and Nasahara, K. N.: Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: An integrated use of AMSR series, MODIS, and Landsat, Remote Sens. Environ., 199, 370–388, https://doi.org/10.1016/j.rse.2017.07.026, 2017.
Morisette, J. T., Privette, J. L., and Justice, C. O.: A framework for the validation of MODIS Land products, Remote Sens. Environ., 83, 77–96, https://doi.org/10.1016/S0034-4257(02)00088-3, 2002.
Nelson, K. J. and Steinwand, D.: A Landsat Data Tiling and Compositing Approach Optimized for Change Detection in the Conterminous United States, Photogramm. Eng. Rem. S., 81, 573–586, https://doi.org/10.14358/PERS.81.7.573, 2015.
Olthof, I. and Fraser, R. H.: Mapping surface water dynamics (1985–2021) in the Hudson Bay Lowlands, Canada using sub-pixel Landsat analysis, Remote Sens. Environ., 300, 113895, https://doi.org/10.1016/j.rse.2023.113895, 2024.
Pahlevan, N., Sarkar, S., Devadiga, S., Wolfe, R. E., Roman, M., Vermote, E., Lin, G., and Xiong, X.: Impact of Spatial Sampling on Continuity of MODIS–VIIRS Land Surface Reflectance Products: A Simulation Approach, IEEE T. Geosci. Remote, 55, 183–196, https://doi.org/10.1109/TGRS.2016.2604214, 2017.
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, https://doi.org/10.1111/gcb.14619, 2019.
Pickens, A. H., Hansen, M. C., Stehman, S. V., Tyukavina, A., Potapov, P., Zalles, V., and Higgins, J.: Global seasonal dynamics of inland open water and ice, Remote Sens. Environ., 272, 112963, https://doi.org/10.1016/j.rse.2022.112963, 2022.
Potapov, P., Hansen, M. C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina, A., and Ying, Q.: Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping, Remote Sens.-Basel, 12, 426, https://doi.org/10.3390/rs12030426, 2020.
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2021a.
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nat. Food, 3, 19–28, https://doi.org/10.1038/s43016-021-00429-z, 2021b.
Potapov, P., Hansen, M. C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X.-P., Baggett, A., Kommareddy, I., and Kommareddy, A.: The Global 2000–2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results, Front. Remote Sens., 3, 856903, https://doi.org/10.3389/frsen.2022.856903, 2022a.
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nat. Food, 3, 19–28, https://doi.org/10.1038/s43016-021-00429-z, 2022b.
Qiu, S., Zhu, Z., Olofsson, P., Woodcock, C. E., and Jin, S.: Evaluation of Landsat image compositing algorithms, Remote Sens. Environ., 285, 113375, https://doi.org/10.1016/j.rse.2022.113375, 2023.
Roy, D. P., Ju, J., Kline, K., Scaramuzza, P. L., Kovalskyy, V., Hansen, M., Loveland, T. R., Vermote, E., and Zhang, C.: Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States, Remote Sens. Environ., 114, 35–49, https://doi.org/10.1016/j.rse.2009.08.011, 2010.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., and Egorov, A.: Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57–70, https://doi.org/10.1016/j.rse.2015.12.024, 2016a.
Roy, D. P., Zhang, H. K., Ju, J., Gomez-Dans, J. L., Lewis, P. E., Schaaf, C. B., Sun, Q., Li, J., Huang, H., and Kovalskyy, V.: A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance, Remote Sens. Environ., 176, 255–271, https://doi.org/10.1016/j.rse.2016.01.023, 2016b.
Sagan, V., Peterson, K. T., Maimaitijiang, M., Sidike, P., Sloan, J., Greeling, B. A., Maalouf, S., and Adams, C.: Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing, Earth-Sci. Rev., 205, 103187, https://doi.org/10.1016/j.earscirev.2020.103187, 2020.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d'Entremont, R. P., Hu, B., Liang, S., Privette, J. L., and Roy, D.: First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83, 135–148, https://doi.org/10.1016/S0034-4257(02)00091-3, 2002.
Senf, C., Leitão, P. J., Pflugmacher, D., van der Linden, S., and Hostert, P.: Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery, Remote Sens. Environ., 156, 527–536, https://doi.org/10.1016/j.rse.2014.10.018, 2015.
Shang, R. and Zhu, Z.: Harmonizing Landsat 8 and Sentinel-2: A time-series-based reflectance adjustment approach, Remote Sens. Environ., 235, 111439, https://doi.org/10.1016/j.rse.2019.111439, 2019.
Shen, H., Wu, P., Liu, Y., Ai, T., Wang, Y., and Liu, X.: A spatial and temporal reflectance fusion model considering sensor observation differences, Int. J. Remote Sens., 34, 4367–4383, https://doi.org/10.1080/01431161.2013.777488, 2013.
Shen, H., Li, X., Cheng, Q., Zeng, C., Yang, G., Li, H., and Zhang, L.: Missing Information Reconstruction of Remote Sensing Data: A Technical Review, IEEE Geoscience and Remote Sensing Magazine, 3, 61–85, https://doi.org/10.1109/MGRS.2015.2441912, 2015.
Shi, W., Guo, D., and Zhang, H.: A reliable and adaptive spatiotemporal data fusion method for blending multi-spatiotemporal-resolution satellite images, Remote Sens. Environ., 268, 112770, https://doi.org/10.1016/j.rse.2021.112770, 2022.
Singh, D.: Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data, Int. J. Appl. Earth Obs., 13, 59–69, https://doi.org/10.1016/j.jag.2010.06.007, 2011.
Song, X.-P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A., Vermote, E. F., and Townshend, J. R.: Global land change from 1982 to 2016, Nature, 560, 639–643, https://doi.org/10.1038/s41586-018-0411-9, 2018.
Song, X.-P., Hansen, M. C., Potapov, P., Adusei, B., Pickering, J., Adami, M., Lima, A., Zalles, V., Stehman, S. V., Di Bella, C. M., Conde, M. C., Copati, E. J., Fernandes, L. B., Hernandez-Serna, A., Jantz, S. M., Pickens, A. H., Turubanova, S., and Tyukavina, A.: Massive soybean expansion in South America since 2000 and implications for conservation, Nat. Sustain., 4, 784–792, https://doi.org/10.1038/s41893-021-00729-z, 2021.
Tian, F., Wang, Y., Fensholt, R., Wang, K., Zhang, L., and Huang, Y.: Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau, Remote Sens.-Basel, 5, 4255–4279, https://doi.org/10.3390/rs5094255, 2013.
Tran, K. H., Zhang, H. K., McMaine, J. T., Zhang, X., and Luo, D.: 10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product, Int. J. Appl. Earth Obs., 107, 102692, https://doi.org/10.1016/j.jag.2022.102692, 2022.
Turubanova, S., Potapov, P., Hansen, M. C., Li, X., Tyukavina, A., Pickens, A. H., Hernandez-Serna, A., Arranz, A. P., Guerra-Hernandez, J., Senf, C., Häme, T., Valbuena, R., Eklundh, L., Brovkina, O., Navrátilová, B., Novotný, J., Harris, N., and Stolle, F.: Tree canopy extent and height change in Europe, 2001–2021, quantified using Landsat data archive, Remote Sens. Environ., 298, 113797, https://doi.org/10.1016/j.rse.2023.113797, 2023.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, Remote Sens. Environ., 185, 46–56, https://doi.org/10.1016/j.rse.2016.04.008, 2016.
Vermote, E., Roger, J. C., Franch, B., and Skakun, S.: LaSRC (Land Surface Reflectance Code): Overview, application and validation using MODIS, VIIRS, LANDSAT and Sentinel 2 data's, in: IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27July 2018, IEEE, 8173–8176, https://doi.org/10.1109/IGARSS.2018.8517622, 2018.
Walker, J. J., de Beurs, K. M., Wynne, R. H., and Gao, F.: Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology, Remote Sens. Environ., 117, 381–393, https://doi.org/10.1016/j.rse.2011.10.014, 2012.
Wang, Q., Zhang, Y., Onojeghuo, A. O., Zhu, X., and Atkinson, P. M.: Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data, IEEE J. Sel. Top. Appl., 10, 4116–4123, https://doi.org/10.1109/JSTARS.2017.2701643, 2017.
Wang, Q., Tang, Y., Tong, X., and Atkinson, P. M.: Virtual image pair-based spatio-temporal fusion, Remote Sens. Environ., 249, 112009, https://doi.org/10.1016/j.rse.2020.112009, 2020.
Watts, J. D., Powell, S. L., Lawrence, R. L., and Hilker, T.: Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery, Remote Sens. Environ., 115, 66–75, https://doi.org/10.1016/j.rse.2010.08.005, 2011.
White, J. C., Wulder, M. A., Hobart, G. W., Luther, J. E., Hermosilla, T., Griffiths, P., Coops, N. C., Hall, R. J., Hostert, P., Dyk, A., and Guindon, L.: Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science, Can. J. Remote Sens., 40, 192–212, https://doi.org/10.1080/07038992.2014.945827, 2014.
Wolfe, R. E., Roy, D. P., and Vermote, E.: MODIS land data storage, gridding, and compositing methodology: Level 2 grid, IEEE T. Geosci. Remote, 36, 1324–1338, https://doi.org/10.1109/36.701082, 1998.
Wu, L., Liu, X., Liu, M., Yang, J., Zhu, L., and Zhou, B.: Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series, IEEE T. Geosci. Remote, 60, 1–14, https://doi.org/10.1109/TGRS.2022.3145675, 2022.
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., Huntington, J. L., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J.-F., Strobl, P., and Cook, B. D.: Fifty years of Landsat science and impacts, Remote Sens. Environ., 280, 113195, https://doi.org/10.1016/j.rse.2022.113195, 2022.
Yan, L. and Roy, D.: Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS), Remote Sens.-Basel, 10, 609, https://doi.org/10.3390/rs10040609, 2018.
Yan, L. and Roy, D. P.: Spatially and temporally complete Landsat reflectance time series modelling: The fill-and-fit approach, Remote Sens. Environ., 241, 111718, https://doi.org/10.1016/j.rse.2020.111718, 2020.
Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., and Dickinson, R.: The role of satellite remote sensing in climate change studies, Nat. Clim. Change, 3, 875–883, https://doi.org/10.1038/nclimate1908, 2013.
Yang, J., Yao, Y., Wei, Y., Zhang, Y., Jia, K., Zhang, X., Shang, K., Bei, X., and Guo, X.: A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data, Remote Sens.-Basel, 12, 2312, https://doi.org/10.3390/rs12142312, 2020.
Yin, Q., Liu, M., Cheng, J., Ke, Y., and Chen, X.: Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method, Remote Sens.-Basel, 11, 1699, https://doi.org/10.3390/rs11141699, 2019.
Zhang, H. K., Luo, D., and Li, Z.: Classifying raw irregular time series (CRIT) for large area land cover mapping by adapting transformer model, Science of Remote Sensing, 9, 100123, https://doi.org/10.1016/j.srs.2024.100123, 2024.
Zhang, W., Li, A., Jin, H., Bian, J., Zhang, Z., Lei, G., Qin, Z., and Huang, C.: An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data, Remote Sens.-Basel, 5, 5346–5368, https://doi.org/10.3390/rs5105346, 2013.
Zhou, Q., Zhu, Z., Xian, G., and Li, C.: A novel regression method for harmonic analysis of time series, ISPRS J. Photogramm., 185, 48–61, https://doi.org/10.1016/j.isprsjprs.2022.01.006, 2022.
Zhu, X., Chen, J., Gao, F., Chen, X., and Masek, J. G.: An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sens. Environ., 114, 2610–2623, https://doi.org/10.1016/j.rse.2010.05.032, 2010.
Zhu, X., Helmer, E. H., Gao, F., Liu, D., Chen, J., and Lefsky, M. A.: A flexible spatiotemporal method for fusing satellite images with different resolutions, Remote Sens. Environ., 172, 165–177, https://doi.org/10.1016/j.rse.2015.11.016, 2016.
Zhu, X., Cai, F., Tian, J., and Williams, T.: Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions, Remote Sens.-Basel, 10, 527, https://doi.org/10.3390/rs10040527, 2018.
Zhu, X., Zhan, W., Zhou, J., Chen, X., Liang, Z., Xu, S., and Chen, J.: A novel framework to assess all-round performances of spatiotemporal fusion models, Remote Sens. Environ., 274, 113002, https://doi.org/10.1016/j.rse.2022.113002, 2022.
Zhu, Z. and Woodcock, C. E.: Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sens. Environ., 118, 83–94, https://doi.org/10.1016/j.rse.2011.10.028, 2012.
Zhu, Z., Wang, S., and Woodcock, C. E.: Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images, Remote Sens. Environ., 159, 269–277, https://doi.org/10.1016/j.rse.2014.12.014, 2015a.
Zhu, Z., Woodcock, C. E., Holden, C., and Yang, Z.: Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time, Remote Sens. Environ., 162, 67–83, https://doi.org/10.1016/j.rse.2015.02.009, 2015b.
Zurita-Milla, R., Clevers, J. G. P. W., and Schaepman, M. E.: Unmixing-Based Landsat TM and MERIS FR Data Fusion, IEEE Geosci. Remote S., 5, 453–457, https://doi.org/10.1109/LGRS.2008.919685, 2008.
