{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T02:37:38Z","timestamp":1775356658538,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:00:00Z","timestamp":1657152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["42071326"],"award-info":[{"award-number":["42071326"]}]},{"name":"National Science Foundation of China","award":["41871275"],"award-info":[{"award-number":["41871275"]}]},{"name":"National Science Foundation of China","award":["202003250102"],"award-info":[{"award-number":["202003250102"]}]},{"name":"China Scholar Council","award":["42071326"],"award-info":[{"award-number":["42071326"]}]},{"name":"China Scholar Council","award":["41871275"],"award-info":[{"award-number":["41871275"]}]},{"name":"China Scholar Council","award":["202003250102"],"award-info":[{"award-number":["202003250102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003\u20132018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics.<\/jats:p>","DOI":"10.3390\/rs14143280","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T22:11:47Z","timestamp":1657231907000},"page":"3280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003\u20132018 in China Using Convergent Cross Mapping Method"],"prefix":"10.3390","volume":"14","author":[{"given":"Yanru","family":"Yu","sequence":"first","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"ICube Laboratory, UMR 7357, CNRS-University of Strasbourg, 300 bd S\u2019ebastien Brant, CS 10413, F-67412 Illkirch, France"}]},{"given":"Guofei","family":"Shang","sequence":"additional","affiliation":[{"name":"Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, School of Land Science and Space Planning, Hebei GEO University, Shijiazhuang 050031, China"}]},{"given":"Sibo","family":"Duan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Wenping","family":"Yu","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China"}]},{"given":"J\u00e9lila","family":"Labed","sequence":"additional","affiliation":[{"name":"ICube Laboratory, UMR 7357, CNRS-University of Strasbourg, 300 bd S\u2019ebastien Brant, CS 10413, F-67412 Illkirch, France"}]},{"given":"Zhaoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"ICube Laboratory, UMR 7357, CNRS-University of Strasbourg, 300 bd S\u2019ebastien Brant, CS 10413, F-67412 Illkirch, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"ref_1","unstructured":"GCOS (2016). GCOS 2016 Implementation Plan, World Meteorological Agency (WMO). Available online: https:\/\/library.wmo.int\/doc_num.php?explnum_id=3417."},{"key":"ref_2","first-page":"1","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_3","first-page":"119","article-title":"Elevation-dependent warming of land surface temperatures in the Andes assessed using MODIS LST time series (2000\u20132017)","volume":"77","author":"Espinoza","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1029\/2020JD033446","article-title":"Land surface temperature trend and its drivers in East Africa","volume":"125","author":"Abera","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"124054","DOI":"10.1088\/1748-9326\/abca65","article-title":"Empirical dynamic modeling reveals climatic drivers in dynamics of bacillary dysentery epidemics in China","volume":"15","author":"Wu","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102818","DOI":"10.1016\/j.scs.2021.102818","article-title":"Understanding land surface temperature impact factors based on local climate zones","volume":"69","author":"Yang","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.landurbplan.2014.11.007","article-title":"Impacts of urban biophysical composition on land surface temperature in urban heat island clusters","volume":"135","author":"Guo","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/nclimate2196","article-title":"Land management and land-cover change have impacts of similar magnitude on surface temperature","volume":"4","author":"Luyssaert","year":"2014","journal-title":"Nat. Clim. Change"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2915","DOI":"10.1073\/pnas.1315126111","article-title":"Afforestation in China cools local land surface temperature","volume":"111","author":"Peng","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/s41598-017-19088-x","article-title":"Relationship among land surface temperature and LUCC, NDVI in typical karst area","volume":"8","author":"Deng","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1109\/JSTARS.2020.3048823","article-title":"Interannual spatiotemporal variations of land surface temperature in China from 2003 to 2018","volume":"14","author":"Yu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112585","DOI":"10.1016\/j.rse.2021.112585","article-title":"Croplands intensify regional and global warming according to satellite observations","volume":"264","author":"Zhou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105458","DOI":"10.1016\/j.atmosres.2021.105458","article-title":"Identifying the dominant driving factors of heat waves in the North China Plain","volume":"252","author":"Wu","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1080\/10106049.2016.1188167","article-title":"Land surface temperature and its impact factors in Western Sichuan Plateau, China","volume":"32","author":"Peng","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, H., Ren, Z., and Tan, J. (2018). The spatial patterns of land surface temperature and its impact factors: Spatial non-stationarity and scale effects based on a Geographically-Weighted Regression Model. Sustainability, 10.","DOI":"10.3390\/su10072242"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104415","DOI":"10.1016\/j.jaridenv.2020.104415","article-title":"Quantifying the influences of land surface parameters on LST variations based on GeoDetector model in Syr Darya Basin, Central Asia","volume":"186","author":"Wang","year":"2021","journal-title":"J. Arid Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1126\/science.1227079","article-title":"Detecting causality in complex ecosystems","volume":"338","author":"Sugihara","year":"2012","journal-title":"Science"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.envpol.2018.10.117","article-title":"Understanding long-term variations of meteorological influences on ground ozone concentrations in Beijing During 2006\u20132016","volume":"245","author":"Chen","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1890\/14-1479.1","article-title":"Spatial convergent cross mapping to detect causal relationships from short time series","volume":"96","author":"Clark","year":"2015","journal-title":"Ecology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"13081","DOI":"10.1073\/pnas.1607747113","article-title":"Global environmental drivers of influenza","volume":"113","author":"Deyle","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12171","DOI":"10.1038\/s41598-018-30669-2","article-title":"Detecting the causal effect of soil moisture on precipitation using Convergent Cross Mapping","volume":"8","author":"Wang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.conengprac.2018.10.005","article-title":"Improved CCM for variable causality detection in complex systems","volume":"83","author":"Wang","year":"2019","journal-title":"Control. Eng. Pract."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105558","DOI":"10.1016\/j.envint.2020.105558","article-title":"Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism","volume":"139","author":"Chen","year":"2020","journal-title":"Environ. Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"118498","DOI":"10.1016\/j.jclepro.2019.118498","article-title":"Understanding the causal influence of major meteorological factors on ground ozone concentrations across China","volume":"242","author":"Chen","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s12080-020-00482-7","article-title":"Inferring species interactions using Granger causality and convergent cross mapping","volume":"14","author":"Barraquand","year":"2020","journal-title":"Theor. Ecol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating Causal Relations by Econometric Models and Cross-spectral Methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econometrica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ecolind.2016.02.052","article-title":"A measure of spatial stratified heterogeneity","volume":"67","author":"Wang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, H., Lei, M., Zhang, N., and Du, G. (2019). The causal nexus between energy consumption, carbon emissions and economic growth: New evidence from China, India and G7 countries using convergent cross mapping. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0217319"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14750","DOI":"10.1038\/srep14750","article-title":"Distinguishing time-delayed causal interactions using convergent cross mapping","volume":"5","author":"Ye","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6430","DOI":"10.1073\/pnas.1215506110","article-title":"Predicting climate effects on Pacific sardine","volume":"110","author":"Deyle","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"S20","DOI":"10.21035\/ijcnmh.2014.1(Suppl.1).S20","article-title":"Convergent cross mapping: A promising technique for cerebral autoregulation estimation","volume":"1","author":"Heskamp","year":"2014","journal-title":"Int. J. Clin. Neurosci. Ment. Health"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compchemeng.2017.03.026","article-title":"Refined convergent cross-mapping for disturbance propagation analysis of chemical processes","volume":"106","author":"Luo","year":"2017","journal-title":"Comput. Chem. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1002\/ldr.2985","article-title":"Effects of seasonal variability of climatic factors on vegetation coverage across drylands in northern China","volume":"29","author":"Zhang","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.1038\/s41467-020-16456-6","article-title":"Causal effects of population dynamics and environmental changes on spatial variability of marine fishes","volume":"11","author":"Wang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0034-4257(02)00093-7","article-title":"Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data","volume":"83","author":"Wan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5373","DOI":"10.1080\/01431160802036565","article-title":"Radiance-based validation of the V5 MODIS land-surface temperature product","volume":"29","author":"Wan","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wan, Z., and Li, Z.-L. (2010). MODIS land surface temperature and emissivity. Land Remote Sensing and Global Environmental Change, Springer.","DOI":"10.1007\/978-1-4419-6749-7_25"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.rse.2013.08.045","article-title":"Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China","volume":"140","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2017.07.001","article-title":"ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions","volume":"203","author":"Dorigo","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.03.014","article-title":"Trend-preserving blending of passive and active microwave soil moisture retrievals","volume":"123","author":"Liu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"28","article-title":"Validation of the ESA CCI soil moisture product in China","volume":"48","author":"An","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1175\/JHM-D-12-0161.1","article-title":"Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing","volume":"14","author":"Albergel","year":"2013","journal-title":"J. Hydrometeorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1038\/s41597-020-0453-3","article-title":"Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset","volume":"7","author":"Harris","year":"2020","journal-title":"Sci. Data"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1038\/344734a0","article-title":"Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series","volume":"344","author":"Sugihara","year":"1990","journal-title":"Nature"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Nonparametric tests against trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econometrica"},{"key":"ref_48","unstructured":"Kendall, M.G. (1975). Rank Correlation Method, Charless Griffin."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"E2272","DOI":"10.1073\/pnas.1700998114","article-title":"Reply to Baskerville and Cobey: Misconceptions about causation with synchrony and seasonal drivers","volume":"114","author":"Sugihara","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"20152258","DOI":"10.1098\/rspb.2015.2258","article-title":"Tracking and forecasting ecosystem interactions in real time","volume":"283","author":"Deyle","year":"2016","journal-title":"Proc. R. Soc. B Boil. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/S0022-1694(01)00594-7","article-title":"Power of the Mann\u2013Kendall and Spearman\u2019s rho tests for detecting monotonic trends in hydrological series","volume":"259","author":"Yue","year":"2002","journal-title":"J. Hydrol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1016\/j.rse.2009.07.021","article-title":"Spatial\u2013temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use\/cover in the Tabriz urban area, Iran","volume":"113","author":"Amiri","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1175\/2010JAMC2460.1","article-title":"Evaluation of the Relationship between Air and Land Surface Temperature under Clear- and Cloudy-Sky Conditions","volume":"50","author":"Gallo","year":"2011","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"103979","DOI":"10.1016\/j.landurbplan.2020.103979","article-title":"Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature","volume":"206","author":"Cao","year":"2021","journal-title":"Landsc. Urban Plan."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1175\/2009JCLI2900.1","article-title":"Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations","volume":"23","author":"Karnieli","year":"2010","journal-title":"J. Clim."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4762","DOI":"10.1109\/JSTARS.2015.2468594","article-title":"Land Surface Temperature and Surface Air Temperature in Complex Terrain","volume":"8","author":"Mutiibwa","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","first-page":"7","article-title":"Aerosols and their relation to global climate and climate sensitivity","volume":"4","author":"Myhre","year":"2013","journal-title":"Nat. Educ. Knowl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3280\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:44:08Z","timestamp":1760139848000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,7]]},"references-count":57,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143280"],"URL":"https:\/\/doi.org\/10.3390\/rs14143280","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,7]]}}}