Li, Z., Zhang, H.K., Roy, D.P., Yan, L., Huang, H., Li, J., 2017. Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m reflective wavelength bands to Sentinel-2 20-m resolution. Rem. Sens. 9, 755. https://doi.org/10.3390/rs9070755.

Li, Z., Shen, H., Weng, Q., Zhang, Y., Dou, P., Zhang, L., 2022. Cloud and cloud shadow detection for optical satellite imagery: features, algorithms, validation, and prospects. ISPRS J. Photogrammetry Remote Sens. 188, 89–108. https://doi.org/10.1016/j.isprsjprs.2022.03.020.

Liang, S., Fang, H., Chen, M., 2001. Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods. IEEE Trans. Geosci. Remote Sens. 39, 2490–2498. https://doi.org/10.1109/36.964986.

Liu, H., Gong, P., Wang, J., Wang, X., Ning, G., Xu, B., 2021. 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.

Lucchesi, R., 2017. File Specification for GEOS-5 FP. GMAO Office Note No. 4. NASA Goddard Spaceflight Center, Greenbelt, MD. Version 1.1).

Loveland, T.R., Dwyer, J.L., 2012. Landsat-building a strong future. Remote Sens. Environ. 122, 22–29. https://doi.org/10.1016/j.rse.2011.09.022.

Maiersperger, T.K., Scaramuzza, P.L., Leigh, L., Shrestha, S., Gallo, K.P., Jenkerson, C.B., Dwyer, J.L., 2013. Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data. Remote Sens. Environ. 136, 1–13. https://doi.org/10.1016/j.rse.2013.04.007.

Malakar, N.K., Hulley, G.C., Hook, S.J., Laraby, K., Cook, M., Schott, J.R., 2018. An operational land surface temperature product for Landsat thermal data: methodology and validation. IEEE Trans. Geosci. Rem. Sens. 56, 5717–5735. https://doi.org/10.1109/tgrs.2018.2824828.

Markham, B.L., Storey, J.C., Williams, D.L., Irons, J.R., 2004a. Landsat sensor performance: history and current status. IEEE Trans. Geosci. Rem. Sens. 42 (12), 2691–2694.

Markham, B.L., Barsi, J.A., Kaita, E., Ong, L., Morfitt, R.A., Haque, M.O., 2015. Radiometric calibration and stability of the landsat-8 operational land imager (OLI). In: Proc. SPIE 9607, Earth Observing Systems XX. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 96070N. https://doi.org/10.1117/12.2188412.

Markham, B., Barsi, J., Kvaran, G., Ong, L., Kaita, E., Biggar, S., Czapla-Myers, J., Mishra, N., Helder, D., 2014. Landsat-8 operational land imager radiometric calibration and stability. Rem. Sens. 6, 12275–12308. https://doi.org/10.3390/rs61212275.

Markham, B.L., Thome, K.J., Barsi, J.A., Kaita, E., Helder, D.L., Barker, J.L., Scaramuzza, P.L., 2004b. Landsat-7 ETM+ on-orbit reflective-band radiometric stability and absolute calibration. IEEE Trans. Geosci. Rem. Sens. 42, 2810–2820. https://doi.org/10.1109/tgrs.2004.836389.

Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemrich, K.F., Gao, F., Kutler, J., Lim, T.K., 2006. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Trans. Geosci. Remote Sens. Lett. 3, 68–72. https://doi.org/10.1109/lgrs.2005.857030.

Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C.J., Storey, J., Jenstrom, D.T., 2020. Landsat 9: empowering open science and applications through continuity. Remote Sens. Environ. 248, 111968 https://doi.org/10.1016/j.rse.2020.111968.

Masuoka, E., Roy, D.P., Wolfe, R., Morisette, J., Sinno, S., Teague, M., Saleous, N., Devadiga, S., Justice, C.O., Nickeson, J., 2011. MODIS land surface products—generation, quality assurance and validation. In: Ramachandran, B., Justice, C.O., Abrams, M.J. (Eds.), Land Remote Sensing and Global Environmental Change. Springer, New York, NY, pp. 509–531. https://doi.org/10.1007/978-1-4419-6749-7_22.

Micijevic, E., Barsi, J., Haque, M.O., Levy, R., Anderson, C., Thome, K., Czapla-Myers, J., Helder, D., 2022. Radiometric performance of the Landsat 9 operational land imager over the first 8 months on orbit. In: Proc. SPIE 12232, Earth Observing Systems XXVII. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, p. 122320w. https://doi.org/10.1117/12.2634301.

Micijevic, E., Haque, M.O., Barsi, J.A., Anderson, C., Markham, B.L., Rengarajan, R., 2020. Landsat Collection 2 radiometric calibration updates. In: Proc. SPIE 11501, Earth Observing Systems XXV. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 115010M. https://doi.org/10.1117/12.2570026.

Micijevic, E., Haque, M.O., Mishra, N., 2017. Radiometric characterization of Landsat collection 1 products. In: Proc. SPIE 10402, Earth Observing Systems XXII. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 104021D. https://doi.org/10.1117/12.2276065.

Micijevic, E., Haque, M.O., Scaramuzza, P., Storey, J., Anderson, C., Markham, B., 2019. Landsat 9 pre-launch sensor characterization and comparison with Landsat 8 results. In: Proc. SPIE 11151, Sensors, Systems, and Next-Generation Satellites XXIII. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, p. 111511F. https://doi.org/10.1117/12.2533102.

Mishra, N., Helder, D., Barsi, J., Markham, B., 2016. Continuous calibration improvement. Landsat 5 through Landsat 8. Remote Sens. Environ. 185, 7–15. https://doi.org/10.1016/j.rse.2016.07.032.

Montanaro, M., Gerace, A., Lunsford, A., Reuter, D., 2014. Stray light artifacts in imagery from the Landsat 8 thermal infrared sensor. Rem. Sens. 6, 10435–10456. https://doi.org/10.3390/rs61110435.

Nazeer, M., Nichol, J.E., Yung, Y.-K., 2014. Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. Int. J. Rem. Sens. 35, 6271–6291. https://doi.org/10.1080/01431161.2014.951742.

Ouaiedrari, H., Vermote, E.F., 1999. Operational atmospheric correction of Landsat TM data. Remote Sens. Environ. 70, 4–15. https://doi.org/10.1016/S0034-4257(99)00054-1.

Pahlevan, N., Schott, J.R., Franz, B.A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, M., Greb, S., Strait, C.M., 2017. Landsat 8 remote sensing reflectance (Rrs) products: evaluations, intercomparisons, and enhancements. Remote Sens. Environ. 190, 289–301. https://doi.org/10.1016/j.rse.2016.12.030.

Qiu, S., Lin, Y., Shang, R., Zhang, J., Ma, L., Zhu, Z., 2018. Making Landsat time series consistent: evaluating and improving Landsat analysis ready data. Rem. Sens. 11, 51. https://doi.org/10.3390/rs11010051.

Qiu, S., Zhu, Z., He, B., 2019. Fmask 4.0: improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery. Remote Sens. Environ. 231, 111205 https://doi.org/10.1016/j.rse.2019.05.024.

Qiu, S., Zhu, Z., Woodcock, C.E., 2020. Cirrus clouds that adversely affect Landsat 8 images: what are they and how to detect them? Remote Sens. Environ. 246, 11184 https://doi.org/10.1016/j.rse.2020.111884.

Rengarajan, R., Storey, J.C., Choate, M.J., 2020. Harmonizing the Landsat ground reference with the Sentinel-2 global reference image using space-based bundle adjustment. Rem. Sens. 12, 3132. https://doi.org/10.3390/rs12193132.

Reuter, D., Richardson, C., Pellerano, F., Irons, J., Allen, R., Anderson, M., Jabvala, M., Lunsford, A., Montanaro, M., Smith, R., Tesfaye, Z., Thome, K., 2015. The thermal infrared sensor (TIRS) on Landsat 8: design overview and pre-launch characterization. Rem. Sens. 7, 1135–1153. https://doi.org/10.3390/rs70101135.

Roger, J.C., Vermote, E., Skakun, S., Murphy, E., Dubovik, O., Kalecinski, N., Korgo, B., Holben, B., 2022. Aerosol models from the AERONET database—application to surface reflectance validation. Atmos. Meas. Tech. 15, 1123–1144. https://doi.org/10.5194/amt-15-1123-2022.

Roy, D.P., Borak, J.S., Devadiga, S., Wolfe, R.E., Zheng, M., Desclaitoux, J., 2002. The MODIS Land Product quality assessment approach. Remote Sens. Environ. 83, 62–76. https://doi.org/10.1016/s0034-4257(02)00087-1.

Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., Zhang, C., 2010. Web-enabled Landsat data (WELD): Landsat ETM+ composed mosaics of the conterminous United States. Remote Sens. Environ. 114, 35–49. https://doi.org/10.1016/j.rse.2009.08.011.

Roy, D.P., Wulder, M.A., Loveland, T.R., C.E, W., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hippie, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z. P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H., Zhu, Z., 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001.

Roy, D.P., Zhang, H.K., Ju, J., Gomez-Dans, J.L., Lewis, P.E., Schaaf, C.B., Sun, Q., Li, J., Huang, H., Kovalskyy, V., 2016. 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.

Roy, D.P., Huang, H., Schetti, L., Giglio, L., Yan, L., Zhang, H.H., Li, Z., 2019. Landsat-8 and Sentinel-2 burned area mapping-A combined sensor multi-temporal change detection approach. Rem. Sens. Environ. 231, 111125.

Saunier, S., Pflug, B., Lobos, I.M., Franch, B., Louis, J., De Los Reyes, R., et al., 2022. Sen2Like: paving the way towards harmonization and fusion of optical data. Rem. Sens. 14 (16), 3855.

Scambos, T.A., Campbell, G.G., Pope, A., Haran, T., Muto, A., Lazzara, M., Reijmer, C.H., Van den Broeke, M.R., 2018. Ultralow surface temperatures in East Antarctica from satellite thermal infrared mapping: the coldest places on Earth. Geophys. Res. Lett. 45, 6124–6133. https://doi.org/10.1029/2018GL078133.

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., Dunderele, M., Doll, C., d'Entremont, R.P., Hu, B., Liang, S., Privette, J. L., Roy, D., 2002. 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.

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Schott, J.R., Hook, S.J., Barsi, J.A., Markham, B.L., Miller, J., Padula, F.P., Raqueno, N. G., 2012. Thermal infrared radiometric calibration of the entire Landsat 4, 5, and 7 archive (1982–2010). Remote Sens. Environ. 122, 41–49. https://doi.org/10.1016/j.rse.2011.07.022.

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Skakun, S., Wevers, J., Brockmann, C., Doxani, G., Aleksandrov, M., Batic, M., Frantz, D., Gascon, F., Gómez-Chova, L., Hagolle, O., López-Puigdollers, D., Louis, J., Lubej, M., Mateo-García, G., Osman, J., Peressuti, D., Pflug, B., Puc, J., Richter, R., Roger, J.-C., Scaramuzza, P., Vermote, E., Vesel, N., Zupanc, A., Zust, L., 2022. Cloud Mask Intercomparison eExercise (CMIX): an evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2. Remote Sens. Environ. 274, 112990 https://doi.org/10.1016/j.rse.2022.112990.
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Li, Z., Shen, H., Weng, Q., Zhang, Y., Dou, P., Zhang, L., 2022. Cloud and cloud shadow detection for optical satellite imagery: features, algorithms, validation, and prospects. ISPRS J. Photogrammetry Remote Sens. 188, 89–108. https://doi.org/10.1016/j.isprsjprs.2022.03.020.

Liang, S., Fang, H., Chen, M., 2001. Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods. IEEE Trans. Geosci. Remote Sens. 39, 2490–2498. https://doi.org/10.1109/36.964986.

Liu, H., Gong, P., Wang, J., Wang, X., Ning, G., Xu, B., 2021. 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.

Lucchesi, R., 2017. File Specification for GEOS-5 FP. GMAO Office Note No. 4. NASA Goddard Spaceflight Center, Greenbelt, MD. Version 1.1).

Loveland, T.R., Dwyer, J.L., 2012. Landsat-building a strong future. Remote Sens. Environ. 122, 22–29. https://doi.org/10.1016/j.rse.2011.09.022.

Maiersperger, T.K., Scaramuzza, P.L., Leigh, L., Shrestha, S., Gallo, K.P., Jenkerson, C.B., Dwyer, J.L., 2013. Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data. Remote Sens. Environ. 136, 1–13. https://doi.org/10.1016/j.rse.2013.04.007.

Malakar, N.K., Hulley, G.C., Hook, S.J., Laraby, K., Cook, M., Schott, J.R., 2018. An operational land surface temperature product for Landsat thermal data: methodology and validation. IEEE Trans. Geosci. Rem. Sens. 56, 5717–5735. https://doi.org/10.1109/tgrs.2018.2824828.

Markham, B.L., Storey, J.C., Williams, D.L., Irons, J.R., 2004a. Landsat sensor performance: history and current status. IEEE Trans. Geosci. Rem. Sens. 42 (12), 2691–2694.

Markham, B.L., Barsi, J.A., Kaita, E., Ong, L., Morfitt, R.A., Haque, M.O., 2015. Radiometric calibration and stability of the landsat-8 operational land imager (OLI). In: Proc. SPIE 9607, Earth Observing Systems XX. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 96070N. https://doi.org/10.1117/12.2188412.

Markham, B., Barsi, J., Kvaran, G., Ong, L., Kaita, E., Biggar, S., Czapla-Myers, J., Mishra, N., Helder, D., 2014. Landsat-8 operational land imager radiometric calibration and stability. Rem. Sens. 6, 12275–12308. https://doi.org/10.3390/rs61212275.

Markham, B.L., Thome, K.J., Barsi, J.A., Kaita, E., Helder, D.L., Barker, J.L., Scaramuzza, P.L., 2004b. Landsat-7 ETM+ on-orbit reflective-band radiometric stability and absolute calibration. IEEE Trans. Geosci. Rem. Sens. 42, 2810–2820. https://doi.org/10.1109/tgrs.2004.836389.

Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemrich, K.F., Gao, F., Kutler, J., Lim, T.K., 2006. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Trans. Geosci. Remote Sens. Lett. 3, 68–72. https://doi.org/10.1109/lgrs.2005.857030.

Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C.J., Storey, J., Jenstrom, D.T., 2020. Landsat 9: empowering open science and applications through continuity. Remote Sens. Environ. 248, 1111968 https://doi.org/10.1016/j.rse.2020.111968.

Masuoka, E., Roy, D.P., Wolfe, R., Morisette, J., Sinno, S., Teague, M., Saleous, N., Devadiga, S., Justice, C.O., Nickeson, J., 2011. MODIS land surface products—generation, quality assurance and validation. In: Ramachandran, B., Justice, C.O., Abrams, M.J. (Eds.), Land Remote Sensing and Global Environmental Change. Springer, New York, NY, pp. 509–531. https://doi.org/10.1007/978-1-4419-6749-7_22.

Micijevic, E., Barsi, J., Haque, M.O., Levy, R., Anderson, C., Thome, K., Czapla-Myers, J., Helder, D., 2022. Radiometric performance of the Landsat 9 operational land imager over the first 8 months on orbit. In: Proc. SPIE 12232, Earth Observing Systems XXVII. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, p. 122320w. https://doi.org/10.1117/12.2634301.

Micijevic, E., Haque, M.O., Barsi, J.A., Anderson, C., Markham, B.L., Rengarajan, R., 2020. Landsat Collection 2 radiometric calibration updates. In: Proc. SPIE 11501, Earth Observing Systems XXV. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 115010M. https://doi.org/10.1117/12.2570026.

Micijevic, E., Haque, M.O., Mishra, N., 2017. Radiometric characterization of Landsat collection 1 products. In: Proc. SPIE 10402, Earth Observing Systems XXII. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 104021D. https://doi.org/10.1117/12.2276065.

Micijevic, E., Haque, M.O., Scaramuzza, P., Storey, J., Anderson, C., Markham, B., 2019. Landsat 9 pre-launch sensor characterization and comparison with Landsat 8 results. In: Proc. SPIE 11151, Sensors, Systems, and Next-Generation Satellites XXIII. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, p. 111511F. https://doi.org/10.1117/12.2533102.

Mishra, N., Helder, D., Barsi, J., Markham, B., 2016. Continuous calibration improvement. Landsat 5 through Landsat 8. Remote Sens. Environ. 185, 7–15. https://doi.org/10.1016/j.rse.2016.07.032.

Montanaro, M., Gerace, A., Lunsford, A., Reuter, D., 2014. Stray light artifacts in imagery from the Landsat 8 thermal infrared sensor. Rem. Sens. 6, 10435–10456. https://doi.org/10.3390/rs61110435.

Nazeer, M., Nichol, J.E., Yung, Y.-K., 2014. Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. Int. J. Rem. Sens. 35, 6271–6291. https://doi.org/10.1080/01431161.2014.951742.

Ouaiedrari, H., Vermote, E.F., 1999. Operational atmospheric correction of Landsat TM data. Remote Sens. Environ. 70, 4–15. https://doi.org/10.1016/S0034-4257(99)00054-1.

Pahlevan, N., Schott, J.R., Franz, B.A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, M., Greb, S., Strait, C.M., 2017. Landsat 8 remote sensing reflectance (Rrs) products: evaluations, intercomparisons, and enhancements. Remote Sens. Environ. 190, 289–301. https://doi.org/10.1016/j.rse.2016.12.030.

Qiu, S., Lin, Y., Shang, R., Zhang, J., Ma, L., Zhu, Z., 2018. Making Landsat time series consistent: evaluating and improving Landsat analysis ready data. Rem. Sens. 11, 51. https://doi.org/10.3390/rs11010051.

Qiu, S., Zhu, Z., He, B., 2019. Fmask 4.0: improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery. Remote Sens. Environ. 231, 111205 https://doi.org/10.1016/j.rse.2019.05.024.

Qiu, S., Zhu, Z., Woodcock, C.E., 2020. Cirrus clouds that adversely affect Landsat 8 images: what are they and how to detect them? Remote Sens. Environ. 246, 111884 https://doi.org/10.1016/j.rse.2020.111884.

Rengarajan, R., Storey, J.C., Choate, M.J., 2020. Harmonizing the Landsat ground reference with the Sentinel-2 global reference image using space-based bundle adjustment. Rem. Sens. 12, 3132. https://doi.org/10.3390/rs12193132.

Reuter, D., Richardson, C., Pellerano, F., Irons, J., Allen, R., Anderson, M., Jabvala, M., Lunsford, A., Montanaro, M., Smith, R., Tesfaye, Z., Thome, K., 2015. The thermal infrared sensor (TIRS) on Landsat 8: design overview and pre-launch characterization. Rem. Sens. 7, 1135–1153. https://doi.org/10.3390/rs70101135.

Roger, J.C., Vermote, E., Skakun, S., Murphy, E., Dubovik, O., Kalecinski, N., Korgo, B., Holben, B., 2022. Aerosol models from the AERONET database—application to surface reflectance validation. Atmos. Meas. Tech. 15, 1123–1144. https://doi.org/10.5194/amt-15-1123-2022.

Roy, D.P., Borak, J.S., Devadiga, S., Wolfe, R.E., Zheng, M., Desclaitoux, J., 2002. The MODIS Land Product quality assessment approach. Remote Sens. Environ. 83, 62–76. https://doi.org/10.1016/s0034-4257(02)00087-1.

Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., Zhang, C., 2010. Web-enabled Landsat data (WELD): Landsat ETM+ composed mosaics of the conterminous United States. Remote Sens. Environ. 114, 35–49. https://doi.org/10.1016/j.rse.2009.08.011.

Roy, D.P., Wulder, M.A., Loveland, T.R., C.E, W., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hippie, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z. P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H., Zhu, Z., 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001.

Roy, D.P., Zhang, H.K., Ju, J., Gomez-Dans, J.L., Lewis, P.E., Schaaf, C.B., Sun, Q., Li, J., Huang, H., Kovalskyy, V., 2016. 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.

Roy, D.P., Huang, H., Schetti, L., Giglio, L., Yan, L., Zhang, H.H., Li, Z., 2019. Landsat-8 and Sentinel-2 burned area mapping-A combined sensor multi-temporal change detection approach. Rem. Sens. Environ. 231, 111125.

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Vermote, E., Justice, C., Claverie, M., Franch, B., 2016. 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.

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Vuolo, F., Mattiuzzi, M., Atzberger, C., 2015. Comparison of the Landsat Surface Reflectance Climate Data Record (CDR) and manually atmospherically corrected data in a semi-arid European study area. Int. J. Appl. Earth Obs. Geoinf. 42, 1–10. https://doi.org/10.1016/j.jag.2015.05.003.

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Wang, Z., Schaaf, C.B., Sun, Q., Shuai, Y., Román, M.O., 2018. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sens. Environ. 207, 50–64. https://doi.org/10.1016/j.rse.2018.02.001.

Wang, Q., Wang, L., Li, Z., Tong, X., Atkinson, P.M., 2020. Spatial-spectral radial basis function-based interpolation for Landsat ETM+ SLC-off image gap filling. IEEE Trans. Geosci. Rem. Sens. 59 (9), 7901–7917.

Wickham, J., Stehman, S.V., Sorenson, D.G., Gass, L., Dewitz, J.A., 2021. Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States. Remote Sens. Environ. 257, 112357 https://doi.org/10.1016/j.rse.2021.112357.

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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., Cook, B.D., 2022. Fifty years of Landsat science and impacts. Remote Sens. Environ. 280, 113195 https://doi.org/10.1016/j.rse.2022.113195.

Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., Roy, D.P., 2016. The global Landsat archive—status, consolidation, and direction. Remote Sens. Environ. 185, 271–283. https://doi.org/10.1016/j.rse.2015.11.032.

Yan, L., Roy, D.P., 2021. Improving Landsat Multispectral Scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration. Remote Sens. Environ. 252, 112181 https://doi.org/10.1016/j.rse.2020.112181.

Yan, L., Roy, D.P., 2020. Spatially and temporally complete Landsat reflectance time series modelling: the fill-and-fit approach. Rem. Sens. Environ. 241, 111718.

Zhai, Y., Roy, D.P., Martins, V.S., Zhang, H.K., Yan, L., Li, Z., 2022. Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients. Remote Sens. Environ. 274, 112992 https://doi.org/10.1016/j.rse.2022.112992.

Zhang, H.K., Roy, D.P., 2016. Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change. Remote Sens. Environ. 186, 217–233. https://doi.org/10.1016/j.rse.2016.08.022.

Zhang, H.K., Roy, D.P., Yan, L., Li, Z., Huang, H., Vermote, E., Skakun, S., Roger, J.-C., 2018. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sens. Environ. 215, 482–494. https://doi.org/10.1016/j.rse.2018.04.031.

Zhang, H.K., Roy, D.P., Luo, D., 2023. Demonstration of large area land cover classification with a one dimensional convolutional neural network applied to single pixel temporal metric percentiles. Remote Sens. Environ. 295, 113653 https://doi.org/10.1016/j.rse.2023.113653.

Zhu, Z., Wang, S., Woodcock, C.E., 2015. 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.

Zhu, Z., Woodcock, C.E., 2012. 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.

Zhu, Z., Wulder, M.A., Roy, D.P., Woodcock, C.E., Hansen, M.C., Radeloff, V.C., Healey, S.P., Schaaf, C., Hostert, P., Strobl, P., Pekel, J.-F., Lymburner, L., Pahlevan, N., Scambos, T.A., 2019. Benefits of the free and open Landsat data policy. Remote Sens. Environ. 224, 382–385. https://doi.org/10.1016/j.rse.2019.02.016.
Storey, J., Roy, D.P., Masek, J., Gascon, F., Dwyer, J., Choate, M., 2016. A note on the temporary misregistration of landsat-8 operational land imager (OLI) and sentinel-2 multi spectral instrument (MSI) imagery. Remote Sens. Environ. 186, 121–122. https://doi.org/10.1016/j.rse.2016.08.025.

Storey, J.C., Choate, M.J., 2004. Landsat-5 bumper-mode geometric correction. IEEE Trans. Geosci. Rem. Sens. 42, 2695–2703. https://doi.org/10.1109/tgrs.2004.836390.

Storey, J.C., Rengarajan, R., Choate, M.J., 2019. Bundle adjustment using space-based triangulation method for improving the Landsat global ground reference. Rem. Sens. 11, 1640. https://doi.org/10.3390/rs11141640.

Tanré, D., Deroo, C., Duhaut, P., Herman, M., Morcrette, J.J., Perbos, J., Deschamps, P. Y., 1990. Technical note Description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code. Int. J. Rem. Sens. 11, 659–668. https://doi.org/10.1080/01431169008955048.

Toivonen, T., Kalliola, R., Ruokolainen, K., Naseem Malik, R., 2006. Across-path DN gradient in Landsat TM imagery of Amazonian forests: a challenge for image interpretation and mosaicking. Remote Sens. Environ. 100, 550–562. https://doi.org/10.1016/j.rse.2005.11.006.

Tonooka, H., 2005. Accurate atmospheric correction of ASTER thermal infrared imagery using the WVS method. IEEE Trans. Geosci. Rem. Sens. 43, 2778–2792. https://doi.org/10.1109/tgrs.2005.857886.

Vermote, E., Justice, C., Claverie, M., Franch, B., 2016. 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.

Vermote, E., McCorkel, J., Rountree, W.H., Santamaria-Artigas, A., Skakun, S., Franch, B., Roger, J.C., 2022. Validation of high spatial resolution surface reflectance using a camera system (CAMSIS). In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Kuala Lumpur, Malaysia, pp. 7729–7732. https://doi.org/10.1109/IGARSS46834.2022.9883241.

Vuolo, F., Mattiuzzi, M., Atzberger, C., 2015. Comparison of the Landsat Surface Reflectance Climate Data Record (CDR) and manually atmospherically corrected data in a semi-arid European study area. Int. J. Appl. Earth Obs. Geoinf. 42, 1–10. https://doi.org/10.1016/j.jag.2015.05.003.

Wan, Z., Dozier, J., 1996. A generalized split-window algorithm for retrieving land- surface temperature from space. IEEE Trans. Geosci. Rem. Sens. 34, 892–905. https://doi.org/10.1109/36.508406.

Wang, Z., Schaaf, C.B., Sun, Q., Shuai, Y., Román, M.O., 2018. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sens. Environ. 207, 50–64. https://doi.org/10.1016/j.rse.2018.02.001.

Wang, Q., Wang, L., Li, Z., Tong, X., Atkinson, P.M., 2020. Spatial-spectral radial basis function-based interpolation for Landsat ETM+ SLC-off image gap filling. IEEE Trans. Geosci. Rem. Sens. 59 (9), 7901–7917.

Wickham, J., Stehman, S.V., Sorenson, D.G., Gass, L., Dewitz, J.A., 2021. Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States. Remote Sens. Environ. 257, 112357 https://doi.org/10.1016/j.rse.2021.112357.

Williams, R.S., Carter, W.D., 1976. ERTS-1, a New Window on Our Planet. U.S. Geological Survey, Reston, VA. https://doi.org/10.3133/pp929. Professional Paper 929.

Wolfe, R.E., Roy, D.P., Vermote, E., 1998. MODIS land data storage, gridding, and compositing methodology: level 2 grid. IEEE Trans. Geosci. Rem. Sens. 36, 1324–1338. https://doi.org/10.1109/36.701082.

Wulder, M.A., Loveland, T.R., Roy, D.P., Crawford, C.J., Masek, J.G., Woodcock, C.E., Allen, R.G., Anderson, M.C., Belward, A.S., Cohen, W.B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J.D., Hostert, P., Hughes, M.J., Huntington, J., Johnson, D.M., Kennedy, R., Kilic, A., Li, Z., Lymburner, L., McCorkel, J., Pahlevan, N., Scambos, T.A., Schaaf, C., Schott, J.R., Sheng, Y., Storey, J., Vermote, E., Vogelmann, J., White, J.C., Wynne, R.H., Zhu, Z., 2019. Current status of Landsat program, science, and applications. Remote Sens. Environ. 225, 127–147. https://doi.org/10.1016/j.rse.2019.02.015.

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., Cook, B.D., 2022. Fifty years of Landsat science and impacts. Remote Sens. Environ. 280, 113195 https://doi.org/10.1016/j.rse.2022.113195.

Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., Roy, D.P., 2016. The global Landsat archive—status, consolidation, and direction. Remote Sens. Environ. 185, 271–283. https://doi.org/10.1016/j.rse.2015.11.032.

Yan, L., Roy, D.P., 2021. Improving Landsat Multispectral Scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration. Remote Sens. Environ. 252, 112181 https://doi.org/10.1016/j.rse.2020.112181.

Yan, L., Roy, D.P., 2020. Spatially and temporally complete Landsat reflectance time series modelling: the fill-and-fit approach. Rem. Sens. Environ. 241, 111718.

Zhai, Y., Roy, D.P., Martins, V.S., Zhang, H.K., Yan, L., Li, Z., 2022. Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients. Remote Sens. Environ. 274, 112992 https://doi.org/10.1016/j.rse.2022.112992.

Zhang, H.K., Roy, D.P., 2016. Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change. Remote Sens. Environ. 186, 217–233. https://doi.org/10.1016/j.rse.2016.08.022.

Zhang, H.K., Roy, D.P., Yan, L., Li, Z., Huang, H., Vermote, E., Skakun, S., Roger, J.-C., 2018. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sens. Environ. 215, 482–494. https://doi.org/10.1016/j.rse.2018.04.031.

Zhang, H.K., Roy, D.P., Luo, D., 2023. Demonstration of large area land cover classification with a one dimensional convolutional neural network applied to single pixel temporal metric percentiles. Remote Sens. Environ. 295, 113653 https://doi.org/10.1016/j.rse.2023.113653.

Zhu, Z., Wang, S., Woodcock, C.E., 2015. 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.

Zhu, Z., Woodcock, C.E., 2012. 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.

Zhu, Z., Wulder, M.A., Roy, D.P., Woodcock, C.E., Hansen, M.C., Radeloff, V.C., Healey, S.P., Schaaf, C., Hostert, P., Strobl, P., Pekel, J.-F., Lymburner, L., Pahlevan, N., Scambos, T.A., 2019. Benefits of the free and open Landsat data policy. Remote Sens. Environ. 224, 382–385. https://doi.org/10.1016/j.rse.2019.02.016.