{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:35:14Z","timestamp":1777484114874,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,12]],"date-time":"2018-07-12T00:00:00Z","timestamp":1531353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran\u2019s I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.<\/jats:p>","DOI":"10.3390\/rs10071112","type":"journal-article","created":{"date-parts":[[2018,7,12]],"date-time":"2018-07-12T06:39:59Z","timestamp":1531377599000},"page":"1112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3109-5631","authenticated-orcid":false,"given":"Jian","family":"Kang","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9710-1868","authenticated-orcid":false,"given":"Junlei","family":"Tan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Jin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100049, China"},{"name":"Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1175\/JHM-D-12-0173.1","article-title":"Calibration and validation of a distributed energy\u2013water balance model using satellite data of land surface temperature and ground discharge measurements","volume":"15","author":"Corbari","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/nclimate2605","article-title":"Decadal modulation of global surface temperature by internal climate variability","volume":"5","author":"Dai","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1835487","DOI":"10.1155\/2016\/1835487","article-title":"Relationship between evapotranspiration and land surface temperature under energy-and water-limited conditions in dry and cold climates","volume":"2016","author":"Sun","year":"2016","journal-title":"Adv. Meteorol."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 modis land-surface temperature\/emissivity product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/MSP.2013.2273004","article-title":"Image inpainting: Overview and recent advances","volume":"31","author":"Guillemot","year":"2014","journal-title":"IEEE Signal Proc. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3390\/rs1020333","article-title":"Estimating daily land surface temperatures in mountainous environments by reconstructed modis lst data","volume":"2","author":"Neteler","year":"2010","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7857","DOI":"10.1080\/01431161.2014.978036","article-title":"Reconstruction of modis land-surface temperature in a flat terrain and fragmented landscape","volume":"35","author":"Fan","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/LGRS.2013.2263553","article-title":"Reconstruction of time-series modis lst in central qinghai-tibet plateau using geostatistical approach","volume":"10","author":"Ke","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, Oxford University Press.","DOI":"10.1093\/oso\/9780195115383.001.0001"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.cageo.2013.08.009","article-title":"Reconstruction of the land surface temperature time series using harmonic analysis","volume":"61","author":"Xu","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0273-1177(93)90550-U","article-title":"Mapping agroecological zones and time lag in vegetation growth by means of fourier analysis of time series of ndvi images","volume":"13","author":"Menenti","year":"1993","journal-title":"Adv. Space Res."},{"key":"ref_13","first-page":"253","article-title":"Mapping air temperature in the lancang river basin using the reconstructed modis lst data","volume":"5","author":"Na","year":"2014","journal-title":"J. Res. Ecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4539","DOI":"10.1109\/JSTARS.2015.2464094","article-title":"An effective interpolation method for modis land surface temperature on the qinghai\u2013tibet plateau","volume":"8","author":"Yu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/LGRS.2014.2348651","article-title":"Reconstructing modis lst based on multitemporal classification and robust regression","volume":"12","author":"Zeng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cageo.2017.04.007","article-title":"Reconstructing daily clear-sky land surface temperature for cloudy regions from modis data","volume":"105","author":"Sun","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4061","DOI":"10.1029\/1999JD901088","article-title":"Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 2. Cloudy-pixel treatment","volume":"105","author":"Jin","year":"2000","journal-title":"J. Geophys. Res."},{"key":"ref_19","first-page":"265","article-title":"Estimating land-surface temperature under clouds using msg\/seviri observations","volume":"13","author":"Lu","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"905","DOI":"10.3390\/rs70100905","article-title":"Estimation of land surface temperature under cloudy skies using combined diurnal solar radiation and surface temperature evolution","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1175\/JAMC-D-17-0213.1","article-title":"Estimation of consistent global microwave land surface emissivity from amsr-e and amsr2 observations","volume":"57","author":"Prakash","year":"2018","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1109\/LGRS.2016.2581140","article-title":"Global land surface emissivity estimation from amsr2 observations","volume":"13","author":"Prakash","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","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_24","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_25","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_26","unstructured":"Burrough, P.A., and McDonell, R.A. (1998). Principles of Geographical Information Systems, Oxford University Press."},{"key":"ref_27","unstructured":"Liang, S. (2005). Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1016\/j.rse.2009.03.009","article-title":"Evaluation of aster and modis land surface temperature and emissivity products using long-term surface longwave radiation observations at surfrad sites","volume":"113","author":"Wang","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1029\/2002GL016354","article-title":"Estimation of land surface window (8\u201312 \u03bcm) emissivity from multi-spectral thermal infrared remote sensing\u2014A case study in a part of sahara desert","volume":"30","author":"Ogawa","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kang, J., Jin, R., Li, X., Zhang, Y., and Zhu, Z. (2018). Spatial upscaling of sparse soil moisture observations based on ridge regression. Remote Sens., 10.","DOI":"10.3390\/rs10020192"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11494","DOI":"10.3390\/rs61111494","article-title":"Evaluation of modis lst products using longwave radiation ground measurements in the northern arid region of china","volume":"6","author":"Yu","year":"2014","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:11:47Z","timestamp":1760195507000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,12]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10071112"],"URL":"https:\/\/doi.org\/10.3390\/rs10071112","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,12]]}}}