{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:44:25Z","timestamp":1762253065310,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T00:00:00Z","timestamp":1563408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013408","name":"National Marine Fisheries Service, National Oceanic and Atmospheric Administration","doi-asserted-by":"publisher","award":["NA12NES4400006"],"award-info":[{"award-number":["NA12NES4400006"]}],"id":[{"id":"10.13039\/100013408","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is an important input to the Atmosphere\u2013Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth\u2019s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments\u2019 advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring.<\/jats:p>","DOI":"10.3390\/rs11141704","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T03:14:41Z","timestamp":1563506081000},"page":"1704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E\/AMSR-2 and MODIS\/GOES Observations"],"prefix":"10.3390","volume":"11","author":[{"given":"Donglian","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6178-7976","authenticated-orcid":false,"given":"Xiwu","family":"Zhan","sequence":"additional","affiliation":[{"name":"NOAA\/NESDIS\/STAR, College Park, MD 20740, USA"}]},{"given":"Paul","family":"Houser","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7768-4066","authenticated-orcid":false,"given":"Chaowei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Long","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Ruixin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1016\/j.agrformet.2009.05.016","article-title":"Advances in thermal infrared remote sensing for land surface modeling","volume":"149","author":"Kustas","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4227","DOI":"10.1016\/j.rse.2008.07.009","article-title":"A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales","volume":"112","author":"Anderson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(96)00215-5","article-title":"A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing","volume":"60","author":"Anderson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00079-8","article-title":"Thermal remote sensing of urban climates","volume":"86","author":"Voogt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.isprsjprs.2017.09.008","article-title":"Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987\u20132015)","volume":"133","author":"Estoque","year":"2017","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2009.07.017","article-title":"Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation","volume":"113","author":"Stathopoulou","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2013.02.022","article-title":"The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring","volume":"134","author":"Hu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1080\/01431161.2013.875237","article-title":"Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data","volume":"35","author":"Leng","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.3390\/s7081612","article-title":"An overview of the \u2018triangle method\u2019 for estimating surface evapotranspiration and soil moisture from satellite imagery","volume":"7","author":"Carlson","year":"2007","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4599","DOI":"10.1080\/0143116031000156837","article-title":"Spaceborne soil moisture estimation at high resolution: A microwave-optical\/IR synergistic approach","volume":"24","author":"Chauhan","year":"2003","journal-title":"Int. J. Remote Sens"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5369","DOI":"10.3390\/rs5105369","article-title":"Derivation of daily evaporative fraction based on temporal variations in surface temperature, air temperature, and net radiation","volume":"5","author":"Lu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.advwatres.2012.06.005","article-title":"Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX\u201908 field campaign","volume":"50","author":"Anderson","year":"2012","journal-title":"Adv. Water Resour."},{"key":"ref_13","first-page":"D11","article-title":"A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology","volume":"112","author":"Anderson","year":"2007","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_14","first-page":"D10","article-title":"A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation","volume":"112","author":"Anderson","year":"2007","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_15","unstructured":"Wang, P.X., Li, X.W., Gong, J.Y., and Song, C. (2001, January 9\u201313). Vegetation temperature condition index and its application for drought monitoring. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2001, Sydney, Australia."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/0143116031000115328","article-title":"Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA","volume":"25","author":"Wan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1175\/JHM-D-16-0067.1","article-title":"Predicting the US Drought Monitor (USDM) using precipitation, soil moisture, and evapotranspiration anomalies, part II: Intraseasonal drought intensification forecasts","volume":"18","author":"Lorenz","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2006.03.009","article-title":"Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa","volume":"103","author":"Pinheiro","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/36.17668","article-title":"Land-surface temperature measurement from space: Physical principles and inverse modeling","volume":"27","author":"Wan","year":"1989","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A generalized split-window algorithm for retrieving land-surface temperature from space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/36.602541","article-title":"A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS\/MODIS data","volume":"35","author":"Wan","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.rse.2013.11.014","article-title":"Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China","volume":"142","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"D11","DOI":"10.1029\/2002JD002422","article-title":"Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8)","volume":"108","author":"Sun","year":"2003","journal-title":"J. Geophys. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1175\/1520-0450(2004)043<0363:LSTEFT>2.0.CO;2","article-title":"Land surface temperature estimation from the next generation of Geostationary Operational Environmental Satellites: GOES M-Q","volume":"43","author":"Sun","year":"2004","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1080\/17538947.2014.906509","article-title":"Inter-comparison of land surface temperature retrieved from GOES-East, GOES-West, and MODIS","volume":"8","author":"Sun","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1175\/JAMC-D-12-0132.1","article-title":"Toward an operational land surface temperature algorithm for GOES","volume":"52","author":"Sun","year":"2013","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/36.58971","article-title":"Land surface temperature derived from the SSM\/I passive microwave brightness temperatures","volume":"28","author":"McFarland","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/36.469482","article-title":"Solving inverse problems by Bayesian iterative inversion of a forward model with applications to parameter mapping using SMMR remote sensing data","volume":"33","author":"Davis","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Njoku, E.G. (1993). Surface Temperature Estimation over Land Using Satellite Microwave Radiometry."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1175\/1520-0450(1998)037<0888:UTSSMI>2.0.CO;2","article-title":"Using the special sensor microwave\/imager to monitor land surface temperatures, wetness, and snow cover","volume":"37","author":"Basist","year":"1998","journal-title":"J. Appl. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/36.739125","article-title":"Retrieval of land surface parameters using passive microwave measurements at 6 to 18 GHz","volume":"37","author":"Njoku","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Holmes, T.R.H., de Jeu, R.A.M., Owe, M., and Dolman, A.J. (2009). Land surface temperature from Ka band (37 GHz) passive microwave observations. J. Geophys. Res., 114.","DOI":"10.1029\/2008JD010257"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.028","article-title":"Land surface temperature retrieval over circumpolar Arctic using SSM\/I\u2013SSMIS and MODIS data","volume":"162","author":"Royer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5699","DOI":"10.1002\/2015JD024402","article-title":"Toward \u201call weather,\u201d long record, and real-time land surface temperature retrievals from microwave satellite observations","volume":"121","author":"Prigent","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.rse.2016.12.008","article-title":"Modelling directional effects on remotely sensed land surface temperature","volume":"190","author":"Ermida","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s11430-007-2053-x","article-title":"A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data","volume":"50","author":"Mao","year":"2007","journal-title":"Sci. China Ser. D Earth Sci."},{"key":"ref_37","unstructured":"Mao, K., Shi, J., Tang, H., Guo, Y., and Qiu, Y. (2007, January 23\u201327). A neural-network technique for retrieving land surface temperature from AMSR-E passive microwave data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2007, Barcelona, Spain."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/07900627.2013.847710","article-title":"The 2011\u20132012 drought in the United States: New lessons from a record event","volume":"30","author":"Grigg","year":"2014","journal-title":"Int. J. Water Resour. Dev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.agrformet.2015.12.065","article-title":"Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought","volume":"218","author":"Otkin","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1175\/BAMS-D-13-00055.1","article-title":"Causes and predictability of the 2012 great plains drought","volume":"95","author":"Hoerling","year":"2014","journal-title":"Bul. Am. Meteorol. Soc."},{"key":"ref_41","first-page":"140","article-title":"A simple retrieval method of land surface temperature from AMSR-E passive microwave data\u2014A case study over Southern China during the strong snow disaster of 2008","volume":"13","author":"Chen","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kou, X., Jiang, L., Bo, Y., Yan, S., and Chai, L. (2016). Estimation of land surface temperature through blending MODIS and AMSR-E data with the Bayesian maximum entropy method. Remote Sens., 8.","DOI":"10.3390\/rs8020105"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TGRS.2002.808331","article-title":"The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA\u2019s contribution to the EOS for global energy and water cycle studies","volume":"41","author":"Kawanishi","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"L06403","DOI":"10.1029\/2004GL021222","article-title":"Initial soil moisture retrievals from AMSR-E: Large scale comparisons with SMEX02 field observations and rainfall patterns over Iowa","volume":"32","author":"McCabe","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1016\/j.rse.2009.02.018","article-title":"Inter-comparison of versions 4, 4.1 and 5 of the MODIS land surface temperature and emissivity products and validation with laboratory measurements of sand samples from the Namib desert, Namibia","volume":"133","author":"Hulley","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_46","unstructured":"Huete, A., Justice, C., and van Leeuwen, W. (2019, July 01). MODIS Vegetation Index (MOD13), Available online: https:\/\/modis.gsfc.nasa.gov\/data\/atbd\/atbd_mod13.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2008.2005977","article-title":"Towards a generalized approach for correction of the BRDF effect in MODIS directional reflectances","volume":"47","author":"Vermote","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"58","DOI":"10.4031\/002533201788058062","article-title":"Development of a seamless multisource topographic bathymetric elevation model of Tampa Bay","volume":"35","author":"Gesch","year":"2002","journal-title":"Mar. Technol. Soc. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1175\/1520-0477(2000)081<2341:SANSRB>2.3.CO;2","article-title":"SURFRAD\u2014A national surface radiation budget network for atmospheric research","volume":"81","author":"Augustine","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1175\/JCLI3720.1","article-title":"Improving land surface emissivity parameter of land surface model in GCM","volume":"19","author":"Jin","year":"2006","journal-title":"J. Clim."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/S0034-4257(03)00011-7","article-title":"A simple retrieval method for land surface temperature and afraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas","volume":"85","author":"Fily","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/TGRS.2007.906478","article-title":"A practical method for retrieving land surface temperature from AMSR-E over the Amazon forest","volume":"46","author":"Gao","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","unstructured":"Quinlan, R.J. (1992, January 16\u201318). Learning with Continuous Classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Tasmania."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1890\/0012-9658(2000)081[3178:CARTAP]2.0.CO;2","article-title":"Classification and regression trees: A powerful yet simple technique for ecological data analysis","volume":"81","author":"Fabriciusm","year":"2000","journal-title":"Ecology"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6458","DOI":"10.1109\/TGRS.2016.2585198","article-title":"Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: A case study in northern China","volume":"54","author":"Duan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","unstructured":"Li, Y., Sun, D., Zhan, X., Houser, P., and Yang, C. Mapping high resolution land surface temperature with the super resolution reconstruction method, In preparation and to be submitted to Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1704\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:07:06Z","timestamp":1760188026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,18]]},"references-count":56,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141704"],"URL":"https:\/\/doi.org\/10.3390\/rs11141704","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,7,18]]}}}