{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:54:10Z","timestamp":1762253650074,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T00:00:00Z","timestamp":1609372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SIGLO-AN project from the Spanish Ministry of Science, Innovation and Universities","award":["RTI2018-101397-B-I00"],"award-info":[{"award-number":["RTI2018-101397-B-I00"]}]},{"name":"TACTIC project from GeoERA organization funded by European Union\u2019s Horizon 2020","award":["GeoE.171.008-"],"award-info":[{"award-number":["GeoE.171.008-"]}]},{"name":"Colorado State University Warner College of Natural Resources Mountain Campus Research Support Fund","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 \u00b0C2 for the application and validation, respectively.<\/jats:p>","DOI":"10.3390\/rs13010113","type":"journal-article","created":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T14:31:49Z","timestamp":1609425109000},"page":"113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5693-2048","authenticated-orcid":false,"given":"Antonio-Juan","family":"Collados-Lara","sequence":"first","affiliation":[{"name":"Instituto Geol\u00f3gico y Minero de Espa\u00f1a, R\u00edos Rosas 23, 28003 Madrid, Spain"},{"name":"ESS-Watershed Science, Colorado State University, Fort Collins, CO 80523, USA"},{"name":"Natural Resources Ecology Laboratory, Fort Collins, CO 80523, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5270-8049","authenticated-orcid":false,"given":"Steven R.","family":"Fassnacht","sequence":"additional","affiliation":[{"name":"ESS-Watershed Science, Colorado State University, Fort Collins, CO 80523, USA"},{"name":"Natural Resources Ecology Laboratory, Fort Collins, CO 80523, USA"},{"name":"Cooperative Institute for Research in the Atmosphere, Fort Collins, CO 80523, USA"}]},{"given":"Eulogio","family":"Pardo-Ig\u00fazquiza","sequence":"additional","affiliation":[{"name":"Instituto Geol\u00f3gico y Minero de Espa\u00f1a, R\u00edos Rosas 23, 28003 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7985-0769","authenticated-orcid":false,"given":"David","family":"Pulido-Velazquez","sequence":"additional","affiliation":[{"name":"Instituto Geol\u00f3gico y Minero de Espa\u00f1a, R\u00edos Rosas 23, 28003 Madrid, Spain"},{"name":"Campus de los Jer\u00f3nimos s\/n, Universidad Cat\u00f3lica de Murcia, Guadalupe, 30107 Murcia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11661","DOI":"10.1002\/2016GL070819","article-title":"Evaporation estimates using weather station data and boundary layer theory","volume":"43","author":"Gentine","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1002\/2017WR021172","article-title":"Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming","volume":"54","author":"Sexstone","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8006","DOI":"10.1002\/2016GL069690","article-title":"Snowmelt rate dictates streamflow","volume":"43","author":"Barnhart","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.2166\/nh.1975.0010","article-title":"Snowmelt-Runoff model for stream flow forecasts","volume":"6","author":"Martinec","year":"1975","journal-title":"Hydrol. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, S., Eisner, S., Magnusson, J.O., Lussana, C., Yang, X., and Beldring, S. (2019). Improvements of the spatially distributed hydrological modelling using the HBV model at 1 km resolution for Norway. J. Hydrol., 577.","DOI":"10.1016\/j.jhydrol.2019.03.051"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-S\u00e1nchez, J., Senent-Aparicio, J., Segura-M\u00e9ndez, F., Pulido-Velazquez, D., and Srinivasan, R. (2019). Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain. Sustainability.","DOI":"10.3390\/su11102872"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.ecolmodel.2004.02.019","article-title":"Modelling Spatio-temporal near surface temperature variation in High Mountain landscapes","volume":"178","author":"Pape","year":"2004","journal-title":"Ecol. Model."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Collados-Lara, A.-J., Fassnacht, S.R., Pulido-Velazquez, D., Pfohl, A.K., Mor\u00e1n-Tejeda, E., Venable, N.B., Pardo-Ig\u00fazquiza, E., and Puntenney-Desmond, K. (2020). Intra-day variability of temperature and its near-surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near-surface measurements. Int. J. Clim., 1\u201315.","DOI":"10.1002\/joc.6778"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1002\/hyp.10125","article-title":"Snow cover and runoff modelling in a high mountain catchment with scarce data: Effects of temperature and precipitation parameters","volume":"29","author":"Zhang","year":"2014","journal-title":"Hydrol. Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1010795108641","article-title":"Standardisation of Temperature Observed by Automatic Weather Stations","volume":"68","author":"Joyce","year":"2001","journal-title":"Environ. Monit. Assess."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1029\/2002EO000007","article-title":"Widespread decline in hydrological monitoring threatens Pan-Arctic Research","volume":"83","author":"Shiklomanov","year":"2002","journal-title":"Eos Trans. Am. Geophys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1175\/JHM486.1","article-title":"A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet)","volume":"7","author":"Liston","year":"2006","journal-title":"J. Hydrometeorpl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2433","DOI":"10.1007\/s11269-020-02561-0","article-title":"Effects of Digital Elevation Model Resolution on Watershed-Based Hydrologic Simulation","volume":"34","author":"Roostaee","year":"2020","journal-title":"Water Resour. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1002\/joc.3370140107","article-title":"Mapping temperature using kriging with external drift: Theory and an example from Scotland","volume":"14","author":"Hudson","year":"1994","journal-title":"Int. J. Clim."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1175\/1520-0442(2003)016<1032:SASVOA>2.0.CO;2","article-title":"Spatial and seasonal variations of air temperature lapse rates in alpine regions","volume":"16","author":"Rolland","year":"2003","journal-title":"J. Clim."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1175\/2007JAMC1565.1","article-title":"Seasonal and synoptic variations in near-surface air temperature lapse rates in a Mountainous Basin","volume":"47","author":"Blandford","year":"2008","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_18","unstructured":"Wan, Z., and Hook, S. (2015). MOD11A1 MODIS\/Terra Land Surface Temperature and the Emissivity Daily L3 Global 1km SIN Grid. NASA LP DAAC."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.gloplacha.2018.04.006","article-title":"Trends in LST over the peninsular Spain as derived from the AVHRR imagery data","volume":"166","author":"Khorchani","year":"2018","journal-title":"Glob. Planet. Chang."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1002\/joc.4766","article-title":"A statistical framework for estimating air temperature using MODIS land surface temperature data","volume":"37","author":"Janatian","year":"2017","journal-title":"Int. J. Clim."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1080\/02626667.2013.866709","article-title":"Can satellite land surface temperature data be used similarly to ground discharge measurements for distributed hydrological model calibration?","volume":"60","author":"Corbari","year":"2015","journal-title":"Hydrol. Sci. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Corbari, C., Huber, C., Yesou, H., Huang, Y., Su, Z., and Mancini, M. (2019). Multi-Satellite Data of Land Surface Temperature, Lakes Area, and Water Level for Hydrological Model Calibration and Validation in the Yangtze River Basin. Water, 11.","DOI":"10.3390\/w11122621"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1002\/hyp.1408","article-title":"Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt\u2013Verde River basin, Arizona","volume":"18","author":"Molotch","year":"2004","journal-title":"Hydrol. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2976","DOI":"10.1002\/2017WR021346","article-title":"Conditioning a hydrologic model using patterns of remotely sensed land surface temperature","volume":"54","author":"Zink","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.agrformet.2007.05.014","article-title":"Geostatistical modelling of air temperature in a mountainous region of Northern Spain","volume":"146","author":"Benavides","year":"2007","journal-title":"Agric. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jimeno-S\u00e1ez, P., Pulido-Velazquez, D., Collados-Lara, A.-J., Pardo-Ig\u00fazquiza, E., Senent-Aparicio, J., and Baena-Ruiz, L. (2020). A Preliminary Assessment of the \u201cUndercatching\u201d and the Precipitation Pattern in an Alpine Basin. Water, 12.","DOI":"10.3390\/w12041061"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s13351-015-5058-y","article-title":"New precipitation and temperature grids for northern Patagonia: Advances in relation to global climate grids","volume":"30","author":"Bianchi","year":"2016","journal-title":"J. Meteorol. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, Y.Z., Cai, W.H., and Yang, J. (2017). Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China. Remote Sens., 9.","DOI":"10.3390\/rs9050410"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1080\/01431161.2017.1395965","article-title":"Estimation of air temperature and reference evapotranspiration using MODIS land surface temperature over Greece","volume":"39","author":"Kitsara","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","unstructured":"Goble, P. (2019, October 07). Colorado Climate, Colorado Encyclopedia. Available online: https:\/\/coloradoencyclopedia.org\/article\/colorado-climate."},{"key":"ref_31","unstructured":"PRISM Climate Group (2020, November 13). 30-Year Normal Mean Temperature. Northwest Alliance for Computational Science & Engineering (NACSE) 2015, Based at Oregon State University., Available online: https:\/\/prism.oregonstate.edu\/normals\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fassnacht, S.R., Patterson, G.G., Venable, N.B., Cherry, M.L., Pfohl, A.K., Sanow, J.E., and Tedesche, M.E. (2020). How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data. Hydrology, 7.","DOI":"10.3390\/hydrology7030038"},{"key":"ref_33","unstructured":"Kampf, S.K., and Fassnacht, S.R. (2020, January 12). Snow, Colorado Encyclopedia. Available online: https:\/\/coloradoencyclopedia.org\/article\/snow."},{"key":"ref_34","unstructured":"Natural Resources Conservation Service (2020, July 26). NRCS: National Water and Climate Center SNOTEL Data Network, Available online: ww.wcc.nrcs.usda.gov\/snow\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.2113\/gsecongeo.58.8.1246","article-title":"Principles of geostatistics","volume":"58","author":"Matheron","year":"1963","journal-title":"Econ. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chiles, J.P., and Delfiner, P. (1999). Geostatistics: Modelling Spatial Uncertainty, Wiley.","DOI":"10.1002\/9780470316993"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3565","DOI":"10.1002\/joc.5517","article-title":"Precipitation fields in an alpine Mediterranean catchment: Inversion of precipitation gradient with elevation or undercatch of snowfall?","volume":"38","year":"2018","journal-title":"Int. J. Clim."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.rse.2006.02.014","article-title":"Downscaling cokriging for image sharpening","volume":"102","author":"Atkinson","year":"2006","journal-title":"Remote Sens. Envrion."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S0022-1694(99)00092-X","article-title":"Optimal areal rainfall estimation using raingauges and satellite data","volume":"222","author":"Grimes","year":"1999","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1029\/2002WR001512","article-title":"Snow water equivalent interpolation for the Colorado River Basin from snow telemetry (SNOTEL) data","volume":"39","author":"Fassnacht","year":"2003","journal-title":"Water Resour. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1002\/(SICI)1097-0088(199807)18:9<1031::AID-JOC303>3.0.CO;2-U","article-title":"Comparison of geostatistical methods for estimating the areal average climatological rainfall mean using data on precipitation and topography","volume":"18","year":"1998","journal-title":"Int. J. Clim."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1023\/A:1021761305044","article-title":"Analysis and Estimation of Natural Processes with Nonhomogeneous Spatial Variation Using Secondary Information","volume":"30","author":"Cassiani","year":"1998","journal-title":"Math. Geol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.rse.2009.10.002","article-title":"Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa","volume":"114","author":"Vancutsem","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.jhydrol.2006.12.021","article-title":"Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico","volume":"336","author":"Gaskin","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_45","unstructured":"Xiong, N., Xiao, Z., Tong, Z., Du, J., Wang, L., and Li, M. (2019). Spatial Interpolation of Monthly Mean Temperatures Based on Cokriging Method. Advances in Computational Science and Computing. ISCSC 2018. Advances in Intelligent Systems and Computing, Tlemcen, Algeria, 1\u20133 October 2018, Springer."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1179\/174327506X138896","article-title":"Kriging with an external drift versus collocated cokriging for water table mapping","volume":"115","author":"Boezio","year":"2006","journal-title":"Appl. Earth Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.2307\/2291758","article-title":"Multivariate geostatistics: An introduction with applications. Multivariate Geostatistics: An Introduction with Applications","volume":"91","author":"Wackernagel","year":"1996","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1080\/01431161.2012.716925","article-title":"Land Surface Temperature from multiple geostationary satellites","volume":"34","author":"Freitas","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"136967","DOI":"10.1016\/j.scitotenv.2020.136967","article-title":"Impact of variations in vegetation on surface air temperature change over the Chinese Loess Plateau","volume":"716","author":"Jin","year":"2020","journal-title":"Sci. Total Envrion."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1002\/met.1756","article-title":"An experimental method for evaluation of the snow albedo effect on near-surface air temperature measurements","volume":"26","author":"Musacchio","year":"2019","journal-title":"Meteorol. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8453","DOI":"10.1080\/01431161.2020.1779379","article-title":"Snow cover change and its relationship with land surface temperature and vegetation in northeastern North America from 2000 to 2017","volume":"41","author":"Thiebault","year":"2020","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/113\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:48:27Z","timestamp":1760179707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,31]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13010113"],"URL":"https:\/\/doi.org\/10.3390\/rs13010113","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,12,31]]}}}