Coulston, J. W., Moisen, G. G., Wilson, B. T., Finco, M. V., Cohen, W. B., and Brewer, C. K.: Modeling Percent Tree Canopy Cover: A Pilot Study, Photogramm. Eng. Rem. S., 78, 715–727, 2012.
Currier, W. R., Pflug, J., Mazzotti, G., Jonas, T., Deems, J. S., Bormann, K. J., Painter, T. H., Hiemstra, C. A., Gelvin, A., Uhlmann, Z., Spaete, L., Glenn, N. F., and Lundquist, J. D.: Comparing Aerial Lidar Observations With Terrestrial Lidar and Snow-Probe Transects From NASA's 2017 SnowEx Campaign, Water Resour. Res., 55, 6285–6294, https://doi.org/10.1029/2018WR024533, 2019.
Deering, D. W., Eck, T. F., and Banerjee, B.: Characterization of the Reflectance Anisotropy of Three Boreal Forest Canopies in Spring–Summer, Remote Sens. Environ., 67, 205–229, https://doi.org/10.1016/S0034-4257(98)00087-X, 1999.
Dozier, J.: A method for satellite identification of surface temperature fields of subpixel resolution, Remote Sens. Environ., 11, 221–229, https://doi.org/10.1016/0034-4257(81)90021-3, 1981.
Dozier, J. and Warren, S. G.: Effect of viewing angle on the infrared brightness temperature of snow, Water Resour. Res., 18, 1424–1434, https://doi.org/10.1029/WR018i005p01424, 1982.
Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of 1701 snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013.
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo evolution, J. Geophys. Res.-Atmos., 111, 1–12, https://doi.org/10.1029/2005JD006834, 2006.
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in Snowpack Simulations – Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance, Water Resour. Res., 55, 2779–2800, https://doi.org/10.1029/2018WR023403, 2019.
Hall, D., Box, J., Casey, K., Hook, S., Shuman, C., and Steffen, K.: Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland, Remote Sens. Environ., 112, 3739–3749, https://doi.org/10.1016/j.rse.2008.05.007, 2008.
Hall, D. K., Foster, J. L., Irons, J. R., and Dabney, P. W.: Airborne bidirectional radiances of snow-covered surfaces in Montana, U. S. A., Ann. Glaciol., 17, 35–40, https://doi.org/10.3189/S0260305500012581, 1993.
Hapke, B., DiMucci, D., Nelson, R., and Smythe, W.: The cause of the hot spot in vegetation canopies and soils: Shadow-hiding versus coherent backscatter, Remote Sens. Environ., 58, 63–68, https://doi.org/10.1016/0034-4257(95)00257-X, 1996.
Henderson, B. G., Balick, L. K., Rodger, A. P., and Pope, P. A.: Concurrent measurements of directional reflectance and temperature of a wintertime coniferous forest from space, Optical Science and Technology, SPIE's 48th Annual Meeting, San Diego, California, USA, 21, 21–33, https://doi.org/10.1117/12.506296, 2003.
Hinkelman, L. M., Lapo, K. E., Cristea, N. C., and Lundquist, J. D.: Using CERES SYN Surface Irradiance Data as Forcing for Snowmelt Simulation in Complex Terrain, J. Hydrometeorol., 16, 2133–2152, https://doi.org/10.1175/JHM-D-14-0179.1, 2015.
Hori, M., Aoki, T., Tanikawa, T., Motoyoshi, H., Hachikubo, A., Sugiura, K., Yasunari, T. J., Eide, H., Storvold, R., Nakajima, Y., and Takahashi, F.: In-situ measured spectral directional emissivity of snow and ice in the 8–14
µm atmospheric window, Remote Sens. Environ., 100, 486–502, https://doi.org/10.1016/j.rse.2005.11.001, 2006.
Inamdar, A. K. and French, A.: Disaggregation of GOES land surface temperatures using surface emissivity, Geophys. Res. Lett., 36, https://doi.org/10.1029/2008GL036544, 2009.
Kim, Y., Still, C. J., Roberts, D. A., and Goulden, M. L.: Thermal infrared imaging of conifer leaf temperatures: Comparison to thermocouple measurements and assessment of environmental influences, Agr. Forest Meteorol., 248, 361–371, https://doi.org/10.1016/j.agrformet.2017.10.010, 2018.
Kochanski, K., Anderson, R. S., and Tucker, G. E.: The evolution of snow bedforms in the Colorado Front Range and the processes that shape them, The Cryosphere, 13, 1267–1281, https://doi.org/10.5194/tc-13-1267-2019, 2019.
Kustas, W. P., Norman, J. M., Anderson, M. C., and French, A. N.: Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship, Remote Sens. Environ., 85, 429–440, https://doi.org/10.1016/S0034-4257(03)00036-1, 2003.
Lapo, K. E., Hinkelman, L. M., Raleigh, M. S., and Lundquist, J. D.: Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance, Water Resour. Res., 51, 1649–1670, https://doi.org/10.1002/2014WR016259, 2015.
Lundquist, J. D., Chickadel, C., Cristea, N., Currier, W. R., Henn, B., Keenan, E., and Dozier, J.: Separating snow and forest temperatures with thermal infrared remote sensing, Remote Sens. Environ., 209, 764–779, https://doi.org/10.1016/j.rse.2018.03.001, 2018.
Meyer, D., Siemonsma, D., Brooks, B., and Johnson, L.: Advanced Spaceborne Thermal Emission and Reflection Radiometer Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Product, algorithm theoretical basis document, U. S. Geological Survey, Reston, VA, https://doi.org/10.3133/ofr20151171, 2015.
Mizukami, N., Clark, M. P., Slater, A. G., Brekke, L. D., Elsner, M. M., Arnold, J. R., and Gangopadhyay, S.: Hydrologic Implications of Different Large-Scale Meteorological Model Forcing Datasets in Mountainous Regions, J. Hydrometeorol., 15, 474–488, https://doi.org/10.1175/JHM-D-13-036.1, 2014.
NASA LP DAAC: ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance V003, NASA EOSDI
S Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/ASTER/AST_L1T.003, 2015.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Pepin, N. C., Maeda, E. E., and Williams, R.: Use of remotely sensed land surface temperature as a proxy for air temperatures at high elevations: Findings from a 5000 m elevational transect across Kilimanjaro, J. Geophys. Res.-Atmos., 121, 9998, https://doi.org/10.1002/2016JD025497, 2016.
Pestana, S.: spestana/snowex2020: Initial release, Zenodo [code], https://doi.org/10.5281/zenodo.8209719, 2023.
Pestana, S. J. and Lundquist, J. D.: SnowEx20 Raw Near Surface Snow Temperature Profile Time Series, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/9HYQMFZP4ALB, 2021.
Pestana, S. and Lundquist, J. D.: Evaluating GOES-16 ABI surface brightness temperature observation biases over the central Sierra Nevada of California, Remote Sens. Environ., 281, 113221, https://doi.org/10.1016/j.rse.2022.113221, 2022.
Pestana, S., Chickadel, C. C., Harpold, A., Kostadinov, T. S., Pai, H., Tyler, S., Webster, C., and Lundquist, J. D.: Bias Correction of Airborne Thermal Infrared Observations Over Forests Using Melting Snow, Water Resour. Res., 55, 11331–11343, https://doi.org/10.1029/2019WR025699, 2019.
Pestana, S., Bair, E. H., Dozier, J., and Lundquist, J. D.: Observations of Diurnal Midwave Infrared Anisotropy over Snow and Forests with GOES-R ABI, IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 5–8, https://doi.org/10.1109/IGARSS52108.2023.10282266, 2023.
Quan, J., Zhan, W., Ma, T., Du, Y., Guo, Z., and Qin, B.: An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes, Remote Sens. Environ., 206, 403–423, https://doi.org/10.1016/j.rse.2017.12.003, 2018.
Raleigh, M. S., Landry, C. C., Hayashi, M., Quinton, W. L., and Lundquist, J. D.: Approximating snow surface temperature from standard temperature and humidity data: New possibilities for snow model and remote sensing evaluation: Snow Surface Temperature Approximation, Water Resour. Res., 49, 8053–8069, https://doi.org/10.1002/2013WR013958, 2013.
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015.
Raleigh, M. S., Livneh, B., Lapo, K., and Lundquist, J. D.: How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations?*, J. Hydrometeorol., 17, 99–120, https://doi.org/10.1175/JHM-D-14-0235.1, 2016.
Rittger, K., Raleigh, M. S., Dozier, J., Hill, A. F., Lutz, J. A., and Painter, T. H.: Canopy adjustment and improved cloud detection for remotely sensed snow cover mapping, Water Resour. Res., 55, e2019WR024914, https://doi.org/10.1029/2019WR024914, 2020.
Schmit, T. J., Griffith, P., Gunshor, M. M., Daniels, J. M., Goodman, S. J., and Lebair, W. J.: A Closer Look at the ABI on the GOES-R Series, B. Am. Meteorol. Soc., 98, 681–698, https://doi.org/10.1175/BAMS-D-15-00230.1, 2017.
Schmit, T. J., Lindstrom, S. S., Gerth, J. J., and Gunshor, M. M.: Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI), J. Oper. Meteorol., 6, 33–46, https://doi.org/10.15191/nwajom.2018.0604, 2018.
Selkowitz, D., Forster, R., and Caldwell, M.: Prevalence of Pure Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine Environments, Remote Sens.-Basel, 6, 12478–12508, https://doi.org/10.3390/rs61212478, 2014.
Shamir, E. and Georgakakos, K. P.: MODIS Land Surface Temperature as an index of surface air temperature for operational snowpack estimation, Remote Sens. Environ., 152, 83–98, https://doi.org/10.1016/j.rse.2014.06.001, 2014.
Stillinger, T., Roberts, D. A., Collar, N. M., and Dozier, J.: Cloud Masking for Landsat 8 and MODIS Terra Over Snow-Covered Terrain: Error Analysis and Spectral Similarity Between Snow and Cloud, Water Resour. Res., 55, 6169–6184, https://doi.org/10.1029/2019WR024932, 2019.
Tan, B., Dellomo, J. J., Wolfe, R. E., and Reth, A. D.: GOES-16 and GOES-17 ABI INR assessment, in: Earth Observing Systems XXIV, Earth Observing Systems XXIV, San Diego, United States, 49, https://doi.org/10.1117/12.2529336, 2019.
Thome, K. J.: Validation plan for MODIS level 1 at-sensor radiance, Remote Sensing Group of the Optical Sciences Center, University of Arizona, Tucson, AZ, USA, 23–28, 1999.
Tomasi, E., Giovannini, L., Zardi, D., and de Franceschi, M.: Optimization of Noah and Noah_MP WRF Land Surface Schemes in Snow-Melting Conditions over Complex Terrain, Mon. Weather Rev., 145, 4727–4745, https://doi.org/10.1175/MWR-D-16-0408.1, 2017.
United States Geological Survey: United States Geological Survey 3D Elevation Program 1 arc-second Digital Elevation Model, OpenTopography [data set], https://doi.org/10.5069/G98K778D, 2021.
Vuyovich, C. M., Marshall, H., Elder, K., Hiemstra, C., Brucker, L., and McCormick, M.: SnowEx20 Grand Mesa Intensive Observation Period Snow Pit Measurements, Version 1, https://doi.org/10.5067/DUD2VZEVBJ7S, 2021.
Walters, R. D.: Transfer of Snow Information across the Macro-to-Hillslope-Scale Gap Using a Physiographic Downscaling Approach: Implications for Hydrologic Modeling in Semiarid, Seasonally Snow-Dominated Watersheds, Boise State University Theses and Dissertations, 590,
https://scholarworks.boisestate.edu/td/590 (last access: 29 May 2022), 2013.
Wan, Z., Zhang, Y., Zhang, Q., and Li, Z.: Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data, Remote Sens. Environ., 83, 163–180, https://doi.org/10.1016/S0034-4257(02)00093-7, 2002.
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67, https://doi.org/10.1029/RG020i001p00067, 1982.
Warren, S. G.: Optical properties of ice and snow, Philos. T. Roy. Soc. A, 377, 20180161, https://doi.org/10.1098/rsta.2018.0161, 2019.
Warren, S. G., Brandt, R. E., and O'Rawe Hinton, P.: Effect of surface roughness on bidirectional reflectance of Antarctic snow, J. Geophys. Res.-Planets, 103, 25789–25807, https://doi.org/10.1029/98JE01898, 1998.
Weng, Q. and Fu, P.: Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery, ISPRS J. Photogramm., 97, 78–88, https://doi.org/10.1016/j.isprsjprs.2014.08.009, 2014.
