Landsat卫星热红外数据地表温度遥感反演研究进展
Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data
- 2021年25卷第8期 页码:1591-1617
纸质出版日期: 2021-08-07
DOI: 10.11834/jrs.20211296
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段四波,茹晨,李召良,王猛猛,徐涵秋,历华,吴鹏海,占文凤,周纪,赵伟,任华忠,吴骅,唐伯惠,张霞,尚国琲,覃志豪.2021.Landsat卫星热红外数据地表温度遥感反演研究进展.遥感学报,25(8): 1591-1617
Duan S B,Ru C,Li Z L,Wang M M,Xu H Q,Li H,Wu P H,Zhan W F,Zhou J,Zhao W,Ren H Z,Wu H,Tang B H,Zhang X,Shang Guo F and Qin Z H. 2021. Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data. National Remote Sensing Bulletin, 25(8):1591-1617
作为驱动地表与大气之间能量交换的关键物理量,地表温度在众多领域中都发挥着重要作用,包括气候变化、环境监测、蒸散发估算以及地热异常勘探等。Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中。本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状,梳理了2001年—2020年20年间基于Landsat热红外数据的地表温度遥感反演与应用的相关文献发表及互引情况,系统地综述了基于Landsat热红外数据的地表温度反演算法,包括基于辐射传输方程的算法、单窗算法、普适性单通道算法、实用单通道算法和分裂窗算法等。在此基础上,进一步介绍了每种算法的参数化方案,包括地表比辐射率和大气参数的估算方法。最后针对Landsat热红外数据地表温度遥感反演提出了未来可能的发展趋势与研究方向。
Land Surface Temperature (LST) is a pivotal factor in the energy exchange procedure between the land surface and the atmosphere. It plays a critical role in various study fields
including regional and global climate change analysis
environment monitoring
evapotranspiration estimation
and geothermal anomaly exploration. How to accurately capture LST from satellites data is one of the international hot spots and frontier topics in the quantitative remote sensing of surface parameters
and numbers of algorithms and products have been developed since 1960s. Specially
due to the advantage of high-spatial resolution
temporal continuity
and data availability
Landsat thermal infrared (TIR) data is generally used for LST retrieval. Landsat sensors and related LST products are introduced in detail at this paper
involving in Landsat 4-5 TM
Landsat 7 ETM+
and Landsat 8 TIRS. By analyzing the abundant academic papers
this article reviews the related publications and citations from 2000 to 2020 about Landsat LST retrieval by dividing them into two parts: algorithm and application. Furthermore
this paper systematically describes the algorithms for LST retrieved from Landsat TIR data including the Radiative Transfer Equation (RTE)-based algorithm
the mono-window algorithm
the generalized single-channel algorithm
the practical single-channel algorithm
and the split-window algorithm. On this basis
this article introduces the methods to obtain relevant parameters of each algorithm including atmospheric parameters and land surface emissivity. Furthermore
the calculation of atmospheric parameters mainly depends on water vapor and air temperature near the surface and atmospheric profiles
which can be obtained in three ways including ground-based sounding data
satellite inversion and reanalysis data. The methods estimating land surface emissivity depend on surface classification and NDVI images. Additionally
the superiority of high-spatial resolution LST from Landsat products makes them often applied to urban heat island effect
disaster monitoring
the LST impact for land use and land cover
where the studies require high-precision satellite images to facilitate detailed topics. With the development of science and technology
high-resolution data makes current problems in LST retrieval more and more obvious. According to the analysis for academic papers in the past 20 years
the research on the algorithm and application of LST retrieval based on Landsat TIR data shows an overall upward trend
and the Landsat LST retrieval and application will continuously play the important role in the future. Therefore
the prospective research trend and directions are proposed for Landsat TIR data
and this paper pointes out 4 directions for subsequent studies
including LST retrieval at the complex terrain region
LST retrieval under the cloud cover
spatio-temporal fusion of multi-source data
and long-term serial LST products. Finally
this article indicates that the uncertainty of land surface emissivity
real complex land surface
and banding effect causing LST errors. Therefore
more scholars should pay attention to these problems and actively propose new methods to solve the current deficiency. Moreover
it is helpful to further understand the mechanism of LST retrieval from remote sensing
provide inspiration for the establishment of new methods for remote sensing retrieval of LST
and promote the research level of quantitative remote sensing of LST in China..
Landsat热红外数据地表温度地表比辐射率大气参数
Landsatthermal infrared dataland surface temperatureland surface emissivityatmospheric parameter
Baldridge A M, Hook S J, Grove C I and Rivera G. 2009. The ASTER spectral library version 2.0. Remote Sensing of Environment, 113(4): 711-715 [DOI: 10.1016/j.rse.2008.11.007http://dx.doi.org/10.1016/j.rse.2008.11.007]
Barsi J A, Barker J L and Schott J R. 2003. An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument//2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse, France: IEEE: 3014-3016 [DOI: 10.1109/IGARSS.2003.1294665http://dx.doi.org/10.1109/IGARSS.2003.1294665]
Barsi J A, Schott J R, Hook S J, Raqueno N G, Markham B L and Radocinski R G. 2014. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11): 11607-11626 [DOI: 10.3390/rs61111607http://dx.doi.org/10.3390/rs61111607]
Bendib A, Dridi H and Kalla M I. 2017. Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, Eastern Algeria. Geocarto International, 32(5): 503-513 [DOI: 10.1080/10106049.2016.1156167http://dx.doi.org/10.1080/10106049.2016.1156167]
Cao B, Guo M Z, Fan W J, Xu X R, Peng J J, Ren H Z, Du Y M, Li H, Bian Z J, Hu T, Xiao Q and Liu Q H. 2018. A new directional canopy emissivity model based on spectral invariants. IEEE Transactions on Geoscience and Remote Sensing, 56(12): 6911-6926 [DOI:10.1109/TGRS.2018.2845678http://dx.doi.org/10.1109/TGRS.2018.2845678]
Cao B, Liu Q H, Du Y M, Roujean J L, Gastellu-Etchegorry J P, Trigo I F, Zhan W F, Yu Y Y, Cheng J, Jacob F, Lagouarde J P, Bian Z J, Li H, Hu T and Xiao Q. 2019. A review of earth surface thermal radiation directionality observing and modeling: historical development, current status and perspectives. Remote Sensing of Environment, 232: 111304 [DOI: 10.1016/j.rse.2019.111304http://dx.doi.org/10.1016/j.rse.2019.111304]
Chatterjee R S, Singh N, Thapa S, Sharma D and Kumar D. 2017. Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. International Journal of Applied Earth Observation and Geoinformation, 58: 264-277 [DOI: 10.1016/j.jag.2017.02.017http://dx.doi.org/10.1016/j.jag.2017.02.017]
Chen F, Yang S, Su Z and Wang K. 2016. Effect of emissivity uncertainty on surface temperature retrieval over urban areas: investigations based on spectral libraries. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 53-65 [DOI: 10.1016/j.isprsjprs.2016.01.007http://dx.doi.org/10.1016/j.isprsjprs.2016.01.007]
Coll C, Caselles V, Valor E and Niclòs R. 2012. Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data. Remote Sensing of Environment, 117: 199-210 [DOI: 10.1016/j.rse.2011.09.018http://dx.doi.org/10.1016/j.rse.2011.09.018]
Coll C, Galve J M, Sanchez J M and Caselles V. 2010. Validation of Landsat-7/ETM+ Thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing, 48(1): 547-555 [DOI: 10.1109/TGRS.2009.2024934http://dx.doi.org/10.1109/TGRS.2009.2024934]
Coll C, Wan Z M and Galve J M. 2009. Temperature-based and radiance-based validations of the V5 MODIS land surface temperature product. Journal of Geophysical Research: Atmospheres, 114: D20102 [DOI: 10.1029/2009JD012038http://dx.doi.org/10.1029/2009JD012038]
Cristóbal J, Jiménez-Muñoz J C, Prakash A, Mattar C, Skoković D and Sobrino J A. 2018. An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, 10(3): 431 [DOI: 10.3390/rs10030431http://dx.doi.org/10.3390/rs10030431]
Cristóbal J, Jiménez-Muñoz J C, Sobrino J A, Ninyerola M and Pons X. 2009. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. Journal of Geophysical Research: Atmospheres, 114(D8): D08103 [DOI: 10.1029/2008JD010616http://dx.doi.org/10.1029/2008JD010616]
Cui S C, Zhou K F and Zhao J. 2016. Land surface temperature retrieval from Landsat-8 data. Remote Sensing Information, 31(6): 15-21
崔世超, 周可法, 赵杰. 2016. 陆地资源卫星数据地表温度反演. 遥感信息, 31(6): 15-21 [DOI: 10.3969/j.issn.1000-3177.2016.06.003http://dx.doi.org/10.3969/j.issn.1000-3177.2016.06.003]
Dang T, Yue P, Bachofer F, Wang M and Zhang M D. 2020. Monitoring land surface temperature change with Landsat images during dry seasons in Bac Binh, Vietnam. Remote Sensing, 12(24): 4067 [DOI: 10.3390/rs12244067http://dx.doi.org/10.3390/rs12244067]
Dash P, Göttsche F M, Olesen F S and Fischer H. 2002. Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. International Journal of Remote Sensing, 23(13): 2563-2594 [DOI: 10.1080/01431160110115041http://dx.doi.org/10.1080/01431160110115041]
Dhar R B, Chakraborty S, Chattopadhyay R and Sikdar P K. 2019. Impact of land-use/land-cover change on land surface temperature using satellite data: a case study of Rajarhat block, north 24-Parganas district, west Bengal. Journal of the Indian Society of Remote Sensing, 47(2): 331-348 [DOI: 10.1007/s12524-019-00939-1http://dx.doi.org/10.1007/s12524-019-00939-1]
Ding F and Xu H Q. 2006. Comparison of two new algorithms for retrieving land surface temperature from Landsat TM thermal band. Geo-Information Science, 8(3): 125-130
丁凤, 徐涵秋. 2006. TM热波段图像的地表温度反演算法与实验分析. 地球信息科学, 8(3): 125-130 [DOI: 10.3969/j.issn.1560-8999.2006.03.025http://dx.doi.org/10.3969/j.issn.1560-8999.2006.03.025]
Du C, Ren H Z, Qin Q M, Meng J J and Zhao S H. 2015. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sensing, 7(1): 647-665 [DOI: 10.3390/rs70100647http://dx.doi.org/10.3390/rs70100647]
Du J, Zhang B, Song K S, Wang Z M, Zeng L H, Jin C, Huang N and Jiang G J. 2009. A comparative study on estimated surface temperature based on Landsat 5-TM in the Honghe wet land. Remote Sensing Technology and Application, 24(3): 312-319
杜嘉, 张柏, 宋开山, 王宗明, 曾丽红, 金翠, 黄妮, 姜广甲. 2009. 基于Landsat 5-TM的洪河湿地地表温度估算方法对比研究. 遥感技术与应用, 24(3): 312-319
Duan S B, Li Z L, Cheng J and Leng P. 2017. Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 1-10 [DOI: 10.1016/j.isprsjprs.2017.02.003http://dx.doi.org/10.1016/j.isprsjprs.2017.02.003]
Duan S B, Li Z L, Li H, Göttsche F M, Wu H, Zhao W, Leng P, Zhang X and Coll C. 2019a. Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment, 225: 16-29 [DOI: 10.1016/j.rse.2019.02.020http://dx.doi.org/10.1016/j.rse.2019.02.020]
Duan S B, Li Z L, Gao C X, Zhao W, Wu H, Qian Y G, Leng P and Gao M F. 2020. Influence of adjacency effect on high-spatial-resolution thermal infrared imagery: implication for radiative transfer simulation and land surface temperature retrieval. Remote Sensing of Environment, 245: 111852 [DOI: 10.1016/j.rse.2020.111852http://dx.doi.org/10.1016/j.rse.2020.111852]
Duan S B, Li Z L, Wang C G, Zhang S T, Tang B H, Leng P and Gao M F. 2019b. Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product. International Journal of Remote Sensing, 40(5/6): 1763-1778 [DOI: 10.1080/01431161.2018.1460513http://dx.doi.org/10.1080/01431161.2018.1460513]
Duan S B, Li Z L, Zhao W, Wu P H, Huang C, Han X J, Gao M F, Leng P and Shang G F. 2021. Validation of Landsat land surface temperature product in the conterminous United States using in situ measurements from SURFRAD, ARM, and NDBC sites. International Journal of Digital Earth, 14(5): 640-660 [DOI: 10.1080/17538947.2020.1862319http://dx.doi.org/10.1080/17538947.2020.1862319]
Dwyer J L, Roy D P, Sauer B, Jenkerson C B, Zhang H K and Lymburner L. 2018. Analysis ready data: enabling analysis of the landsat archive. Remote Sensing, 10(9): 1363 [DOI: 10.3390/rs10091363http://dx.doi.org/10.3390/rs10091363]
Ermida S L, Soares P, Mantas V, Göttsche F M and Trigo I F. 2020. Google earth engine open-source code for land surface temperature estimation from the landsat series. Remote Sensing, 12(9): 1471 [DOI: 10.3390/rs12091471http://dx.doi.org/10.3390/rs12091471]
Fu P and Weng Q H. 2015. Temporal dynamics of land surface temperature from Landsat TIR time series images. IEEE Geoscience and Remote Sensing Letters, 12(10): 2175-2179 [DOI: 10.1109/LGRS.2015.2455019http://dx.doi.org/10.1109/LGRS.2015.2455019]
Fu P and Weng Q H. 2016a. Consistent land surface temperature data generation from irregularly spaced Landsat imagery. Remote Sensing of Environment, 184: 175-187 [DOI: 10.1016/j.rse.2016.06.019http://dx.doi.org/10.1016/j.rse.2016.06.019]
Fu P and Weng Q H. 2016b. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175: 205-214 [DOI: 10.1016/j.rse.2015.12.040http://dx.doi.org/10.1016/j.rse.2015.12.040]
Fu P and Weng Q H. 2018. Variability in annual temperature cycle in the urban areas of the United States as revealed by MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 65-73 [DOI: 10.1016/j.isprsjprs.2018.09.003http://dx.doi.org/10.1016/j.isprsjprs.2018.09.003]
Galve J M, Sánchez J M, Coll C and Villodre J. 2018. A new single-band pixel-by-pixel atmospheric correction method to improve the accuracy in remote sensing estimates of LST. Application to Landsat 7-ETM+. Remote Sensing, 10(6): 826 [DOI: 10.3390/rs10060826http://dx.doi.org/10.3390/rs10060826]
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218 [DOI: 10.1109/TGRS.2006.872081http://dx.doi.org/10.1109/TGRS.2006.872081]
García-Santos V, Cuxart J, Martínez-Villagrasa D, Jiménez A M and Simó G. 2018. Comparison of three methods for estimating land surface temperature from Landsat 8-TIRS sensor data. Remote Sensing, 10(9): 1450 [DOI: 10.3390/rs10091450http://dx.doi.org/10.3390/rs10091450]
Gemitzi A, Dalampakis P and Falalakis G. 2021. Detecting geothermal anomalies using Landsat 8 thermal infrared remotely sensed data. International Journal of Applied Earth Observation and Geoinformation, 96: 102283 [DOI: 10.1016/j.jag.2020.102283http://dx.doi.org/10.1016/j.jag.2020.102283]
Gerace A and Montanaro M. 2017. Derivation and validation of the stray light correction algorithm for the thermal infrared sensor onboard Landsat 8. Remote Sensing of Environment, 191: 246-257 [DOI: 10.1016/j.rse.2017.01.029http://dx.doi.org/10.1016/j.rse.2017.01.029]
Gillespie A, Rokugawa S, Matsunaga T, Cothern J S, Hook S and Kahle A B. 1998. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1113-1126 [DOI: 10.1109/36.700995http://dx.doi.org/10.1109/36.700995]
Guha S, Govil H, Dey A and Gill N. 2018. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1): 667-678 [DOI: 10.1080/22797254.2018.1474494http://dx.doi.org/10.1080/22797254.2018.1474494]
Guha S, Govil H and Diwan P. 2019. Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. Journal of Applied Remote Sensing, 13(2): 024518 [DOI: 10.1117/1.JRS.13.024518http://dx.doi.org/10.1117/1.JRS.13.024518]
Guo J X, Ren H Z, Zheng Y T, Lu S Z and Dong J J. 2020. Evaluation of land surface temperature retrieval from Landsat 8/TIRS images before and after stray light correction using the SURFRAD dataset. Remote Sensing, 12(6): 1023 [DOI: 10.3390/rs12061023http://dx.doi.org/10.3390/rs12061023]
Hais M and Kučera T. 2009. The influence of topography on the forest surface temperature retrieved from Landsat TM, ETM + and ASTER thermal channels. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6): 585-591 [DOI: 10.1016/j.isprsjprs.2009.04.003http://dx.doi.org/10.1016/j.isprsjprs.2009.04.003]
He G J, Zhang Z M, Jiao W L, Long T F, Peng Y, Wang G Z, Yin R Y, Wang W, Zhang X M, Liu H C, Cheng B and Xiang B. 2018. Generation of ready to use (RTU) products over China based on Landsat series data. Big Earth Data, 2(1): 56-64 [DOI: 10.1080/20964471.2018.1433370http://dx.doi.org/10.1080/20964471.2018.1433370]
Hofierka J, Gallay M, Onačillová K and Hofierka J. 2020. Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31: 100566 [DOI: 10.1016/j.uclim.2019.100566http://dx.doi.org/10.1016/j.uclim.2019.100566]
Hu D Y, Qiao K, Wang X L, Zhao L M and Ji G H. 2015. Land surface temperature retrieval from Landsat 8 thermal infrared data using mono-window algorithm. Journal of Remote Sensing, 19(6): 964-976
胡德勇, 乔琨, 王兴玲, 赵利民, 季国华. 2015. 单窗算法结合Landsat8热红外数据反演地表温度. 遥感学报, 19(6): 964-976 [DOI: 10.11834/jrs.20155038http://dx.doi.org/10.11834/jrs.20155038]
Hu D Y, Qiao K, Wang X L, Zhao L M and Ji G H. 2017. Comparison of three single-window algorithms for retrieving land-surface temperature with Landsat 8 TIRS data. Geomatics and Information Science of Wuhan University, 42(7): 869-876
胡德勇, 乔琨, 王兴玲, 赵利民, 季国华. 2017. 利用单窗算法反演Landsat 8 TIRS数据地表温度. 武汉大学学报, 42(7): 869-876 [DOI: 10.13203/j.whugis20150164http://dx.doi.org/10.13203/j.whugis20150164]
Hu G T, Wang G and Yang C J. 2017. Analysis of green landscape distribution and thermal environment of Yangjiang City. Remote Sensing Information, 32(2): 156-161
胡光庭, 王刚, 杨崇俊. 2017. 阳江市绿地空间分布及其热环境分析. 遥感信息, 32(2): 156-161 [DOI: 10.3969/j.issn.1000-3177.2017.02.023http://dx.doi.org/10.3969/j.issn.1000-3177.2017.02.023]
Hulley G C and Hook S J. 2011. Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research. IEEE Transactions on Geoscience and Remote Sensing, 49(4): 1304-1315 [DOI: 10.1109/TGRS.2010.2063034http://dx.doi.org/10.1109/TGRS.2010.2063034]
Januar T W, Lin T H, Huang C Y and Chang K E. 2020. Modifying an image fusion approach for high spatiotemporal LST retrieval in surface dryness and evapotranspiration estimations. Remote Sensing, 12(3): 498 [DOI: 10.3390/rs12030498http://dx.doi.org/10.3390/rs12030498]
Jiang D L, Kuang H H, Cao X F, Huang Y and Li F R. 2015. Study of land surface temperature retrieval based on Landsat 8 – with the sample of Dianchi Lake Basin. Remote Sensing Technology and Application, 30(3): 448-454
蒋大林, 匡鸿海, 曹晓峰, 黄艺, 李发荣. 2015. 基于Landsat8的地表温度反演算法研究——以滇池流域为例. 遥感技术与应用, 30(3): 448-454 [DOI: 10.11873/j.issn.1004-0323.2015.3.0448http://dx.doi.org/10.11873/j.issn.1004-0323.2015.3.0448]
Jiang W G, Jia K, Chen Z, Deng Y and Rao P Z. 2017. Using spatiotemporal remote sensing data to assess the status and effectiveness of the underground coal fire suppression efforts during 2000-2015 in Wuda, China. Journal of Cleaner Production, 142: 565-577 [DOI: 10.1016/j.jclepro.2016.03.082http://dx.doi.org/10.1016/j.jclepro.2016.03.082]
Jiao Z H, Yan G J, Zhao J, Wang T X and Chen L. 2015. Estimation of surface upward longwave radiation from MODIS and VIIRS clear-sky data in the Tibetan Plateau. Remote Sensing of Environment, 162: 221-237 [DOI: 10.1016/j.rse.2015.02.021http://dx.doi.org/10.1016/j.rse.2015.02.021]
Jiménez-Muñoz J C, Cristóbal J, Sobrino J A, Sòria G, Ninyerola M and Pons X. 2009. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Transactions on Geoscience and Remote Sensing, 47(1): 339-349 [DOI: 10.1109/TGRS.2008.2007125http://dx.doi.org/10.1109/TGRS.2008.2007125]
Jiménez-Muñoz J C and Sobrino J A. 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres, 108(D22): 4688 [DOI: 10.1029/2003JD003480http://dx.doi.org/10.1029/2003JD003480]
Jiménez-Muñoz J C, Sobrino J A, Skokovic D, Mattar C and Cristóbal J. 2014. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, 11(10): 1840-1843 [DOI: 10.1109/LGRS.2014.2312032http://dx.doi.org/10.1109/LGRS.2014.2312032]
Jin M J, Li J M, Wang C L and Shang R L. 2015. A practical split-window algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China. Remote Sensing, 7(4): 4371-4390 [DOI: 10.3390/rs70404371http://dx.doi.org/10.3390/rs70404371]
Käfer P S, Rolim S B A, Diaz L R, da Rocha N S, Iglesias M L and Rex F E. 2020. Comparative analysis of split-window and single-channel algorithms for land surface temperature retrieval of a pseudo-invariant target. Bulletin of Geodetic Sciences, 26(2): e2020008
Lai J M, Zhan W F, Huang F, Quan J L, Hu L Q, Gao L and Ju W M. 2018. Does quality control matter? Surface urban heat island intensity variations estimated by satellite-derived land surface temperature products. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 212-227 [DOI: 10.1016/j.isprsjprs.2018.03.012http://dx.doi.org/10.1016/j.isprsjprs.2018.03.012]
Laraby K G and Schott J R. 2018. Uncertainty estimation method and Landsat 7 global validation for the Landsat surface temperature product. Remote Sensing of Environment, 216: 472-481 [DOI: 10.1016/j.rse.2018.06.026http://dx.doi.org/10.1016/j.rse.2018.06.026]
Li F Q, Jackson T J, Kustas W P, Schmugge T J, French A N, Cosh M H and Bindlish R. 2004. Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX. Remote Sensing of Environment, 92(4): 521-534 [DOI: 10.1016/j.rse.2004.02.018http://dx.doi.org/10.1016/j.rse.2004.02.018]
Li S M, Yu Y Y, Sun D L, Tarpley D, Zhan X W and Chiu L. 2014. Evaluation of 10 year AQUA/MODIS land surface temperature with surfrad observations. International Journal of Remote Sensing, 35(3): 830-856 [DOI: 10.1080/01431161.2013.873149http://dx.doi.org/10.1080/01431161.2013.873149]
Li S S and Jiang G M. 2018. Land surface temperature retrieval from Landsat-8 Data with the generalized split-window algorithm. IEEE Access, 6: 18149-18162 [DOI: 10.1109/ACCESS.2018.2818741http://dx.doi.org/10.1109/ACCESS.2018.2818741]
Li T Y and Meng Q M. 2018. A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data. Landscape and Urban Planning, 179: 63-71 [DOI: 10.1016/j.landurbplan.2018.07.010http://dx.doi.org/10.1016/j.landurbplan.2018.07.010]
Li X and Strahler A H. 1992. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 276-292 [DOI: 10.1109/36.134078http://dx.doi.org/10.1109/36.134078]
Li X M, Zhou W Q, Ouyang Z Y, Xu W H and Zheng H. 2012a. Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecology, 27: 887-898 [DOI: 10.1007/s10980-012-9731-6http://dx.doi.org/10.1007/s10980-012-9731-6]
Li Y Y, Zhang H and Kainz W. 2012b. Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: using time-series of Landsat TM/ETM+ data. International Journal of Applied Earth Observation and Geoinformation, 19: 127-138 [DOI: 10.1016/j.jag.2012.05.001http://dx.doi.org/10.1016/j.jag.2012.05.001]
Li Z L, Jia L, Su Z B, Wan Z M and Zhang R H. 2003. A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area. International Journal of Remote Sensing, 24(24): 5095-5117 [DOI: 10.1080/0143116031000096014http://dx.doi.org/10.1080/0143116031000096014]
Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F and Sobrino J A. 2013. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131: 14-37 [DOI: 10.1016/j.rse.2012.12.008http://dx.doi.org/10.1016/j.rse.2012.12.008]
Liu H and Weng Q H. 2012. Enhancing temporal resolution of satellite imagery for public health studies: a case study of West Nile Virus outbreak in Los Angeles in 2007. Remote Sensing of Environment, 117: 57-71 [DOI: 10.1016/j.rse.2011.06.023http://dx.doi.org/10.1016/j.rse.2011.06.023]
Liu L Y, Zhang X, Gao Y, Chen X D, Shuai X and Mi J. 2021. Finer-resolution mapping of global land cover: recent developments, consistency analysis, and prospects. Journal of Remote Sensing, 2021: 5289697 [DOI: 10.34133/2021/5289697http://dx.doi.org/10.34133/2021/5289697]
Liu Z H, Zhan W F, Lai J M, Hong F L, Quan J L, Bechtel B, Huang F and Zou Z X. 2019. Balancing prediction accuracy and generalization ability: a hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures. ISPRS Journal of Photogrammetry and Remote Sensing, 151: 189-206 [DOI: 10.1016/j.isprsjprs.2019.03.013http://dx.doi.org/10.1016/j.isprsjprs.2019.03.013]
Lu D S and Weng Q H. 2006. Use of impervious surface in urban land-use classification. Remote Sensing of Environment, 102(1/2): 146-160 [DOI: 10.1016/j.rse.2006.02.010http://dx.doi.org/10.1016/j.rse.2006.02.010]
Lu L L, Weng Q H, Xiao D, Guo H D, Li Q T and Hui W H. 2020. Spatiotemporal variation of surface urban heat islands in relation to land cover composition and configuration: a multi-scale case study of Xi’an, China. Remote Sensing, 12(17): 2713 [DOI: 10.3390/rs12172713http://dx.doi.org/10.3390/rs12172713]
Ma Y F, Liu S M, Song L S, Xu Z W, Liu Y L, Xu T R and Zhu Z L. 2018. Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sensing of Environment, 216: 715-734 [DOI: 10.1016/j.rse.2018.07.019http://dx.doi.org/10.1016/j.rse.2018.07.019]
Mahato S and Pal S. 2019. Influence of land surface parameters on the spatio-seasonal land surface temperature regime in rural West Bengal, India. Advances in Space Research, 63(1): 172-189 [DOI: 10.1016/j.asr.2018.09.014http://dx.doi.org/10.1016/j.asr.2018.09.014]
Maimaitiyiming M, Ghulam A, Tiyip T, Pla F, Latorre-Carmona P, Halik Ü, Sawut M and Caetano M. 2014. Effects of green space spatial pattern on land surface temperature: implications for sustainable urban planning and climate change adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, 89: 59-66 [DOI: 10.1016/j.isprsjprs.2013.12.010http://dx.doi.org/10.1016/j.isprsjprs.2013.12.010]
Malakar N K, Hulley G C, Hook S J, Laraby K, Cook M and Schott J R. 2018. An operational land surface temperature product for Landsat thermal data: methodology and Validation. IEEE Transactions on Geoscience and Remote Sensing, 56(10): 5717-5735 [DOI: 10.1109/TGRS.2018.2824828http://dx.doi.org/10.1109/TGRS.2018.2824828]
Mallick J, Singh C K, Shashtri S, Rahman A and Mukherjee S. 2012. Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city. International Journal of Applied Earth Observation and Geoinformation, 19: 348-358 [DOI: 10.1016/j.jag.2012.06.002http://dx.doi.org/10.1016/j.jag.2012.06.002]
Mao K B, Tang H J, Chen Z X, Qiu Y B, Qin Z H and Li M C. 2006. A split-window algorithm for retrieving land-surface temperature from ASTER data. Remote Sensing Information, (5): 7-11
毛克彪, 唐华俊, 陈仲新, 邱玉宝, 覃志豪, 李满春. 2006. 一个从ASTER数据中反演地表温度的劈窗算法. 遥感信息, (5): 7-11 [DOI: 10.3969/j.issn.1000-3177.2006.05.004]
Mbuh M J, Wheeler R and Cook A. 2021. Spatiotemporal analysis of urban heat island intensification in the city of Minneapolis-St. Paul and Chicago metropolitan areas using Landsat data from 1984 to 2016. Geocarto International, 36(14): 1565-1590. [DOI: 10.1080/10106049.2019.1655802http://dx.doi.org/10.1080/10106049.2019.1655802]
McMillin L M. 1975. Estimation of sea surface temperatures from two infrared window measurements with different absorption. Journal of Geophysical Research, 80(36): 5113-5117 [DOI: 10.1029/JC080i036p05113http://dx.doi.org/10.1029/JC080i036p05113]
Meng X C and Cheng J. 2018. Evaluating eight global reanalysis products for atmospheric correction of thermal infrared sensor-application to Landsat 8 TIRS10 data. Remote Sensing, 10(3): 474 [DOI: 10.3390/rs10030474http://dx.doi.org/10.3390/rs10030474]
Meng X C, Li H, Du Y M, Cao B, Liu Q H and Li B. 2018. Retrieval and validation of the land surface temperature derived from Landsat 8 data: a case study of the Heihe River Basin. Journal of Remote Sensing, 22(5): 857-871
孟翔晨, 历华, 杜永明, 曹彪, 柳钦火, 李彬. 2018. Landsat 8地表温度反演及验证——以黑河流域为例. 遥感学报, 22(5): 857-871 [DOI: 10.11834/jrs.20187411http://dx.doi.org/10.11834/jrs.20187411]
Meng X C, Li H, Du Y M, Cao B, Liu Q H, Sun L and Zhu J S. 2016. Estimating land surface emissivity from ASTER GED products. Journal of Remote Sensing, 20(3): 382-396
孟翔晨, 历华, 杜永明, 曹彪, 柳钦火, 孙林, 朱金山. 2016. 基于ASTER GED产品的地表发射率估算. 遥感学报, 20(3): 382-396 [DOI: 10.11834/jrs.20165230http://dx.doi.org/10.11834/jrs.20165230]
Mercury M, Green R, Hook S, Oaida B, Wu W, Gunderson A and Chodas M. 2012. Global cloud cover for assessment of optical satellite observation opportunities: a HyspIRI case study. Remote Sensing of Environment, 126: 62-71 [DOI: 10.1016/j.rse.2012.08.007http://dx.doi.org/10.1016/j.rse.2012.08.007]
Mia M B, Fujimitsu Y and Nishijima J. 2018. Monitoring of thermal activity at the Hatchobaru–Otake geothermal area in Japan using multi-source satellite images—with comparisons of methods, and solar and seasonal effects. Remote Sensing, 10(9): 1430 [DOI: 10.3390/rs10091430http://dx.doi.org/10.3390/rs10091430]
Mia M B, Nishijima J and Fujimitsu Y. 2015. Monitoring heat flow before and after eruption of Kuju fumaroles in 1995 using Landsat TIR images. Acta Geodaetica et Geophysica, 50: 295-305 [DOI: 10.1007/s40328-014-0075-3http://dx.doi.org/10.1007/s40328-014-0075-3]
Miller J, Gerace A, Eon R, Montanaro M, Kremens R and Wehle J. 2020. Low-cost radiometer for Landsat land surface temperature validation. Remote Sensing, 12(3): 416 [DOI: 10.3390/rs12030416http://dx.doi.org/10.3390/rs12030416]
Montanaro M, Gerace A, Lunsford A and Reuter D. 2014. Stray light artifacts in imagery from the Landsat 8 thermal infrared sensor. Remote Sensing, 6(11): 10435-10456 [DOI: 10.3390/rs61110435http://dx.doi.org/10.3390/rs61110435]
Montanaro M, Gerace A and Rohrbach S. 2015. Toward an operational stray light correction for the Landsat 8 thermal infrared sensor. Applied Optics, 54(13): 3963-3978 [DOI: 10.1364/AO.54.003963http://dx.doi.org/10.1364/AO.54.003963]
Neinavaz E, Skidmore A K and Darvishzadeh R. 2020. Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method. International Journal of Applied Earth Observation and Geoinformation, 85: 101984 [DOI: 10.1016/j.jag.2019.101984http://dx.doi.org/10.1016/j.jag.2019.101984]
Nill L, Ullmann T, Kneisel C, Sobiech-Wolf J and Baumhauer R. 2019. Assessing spatiotemporal variations of Landsat land surface temperature and multispectral indices in the Arctic Mackenzie delta region between 1985 and 2018. Remote Sensing, 11(19): 2329 [DOI: 10.3390/rs11192329http://dx.doi.org/10.3390/rs11192329]
Parastatidis D, Mitraka Z, Chrysoulakis N and Abrams M. 2017. Online global land surface temperature estimation from landsat. Remote Sensing, 9(12): 1208 [DOI: 10.3390/rs9121208http://dx.doi.org/10.3390/rs9121208]
Peng X X, Wu W Y, Zheng Y Y, Sun J Y, Hu T G and Wang P. 2020. Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Scientific Reports, 10: 10451 [DOI: 10.1038/s41598-020-67423-6http://dx.doi.org/10.1038/s41598-020-67423-6]
Peres L F and DaCamara C C. 2005. Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI. IEEE Transactions on Geoscience and Remote Sensing, 43(8): 1834-1844 [DOI: 10.1109/TGRS.2005.851172http://dx.doi.org/10.1109/TGRS.2005.851172]
Qin Q M, Zhang N, Nan P and Chai L L. 2011. Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis-A case study in Tengchong, China. International Journal of Applied Earth Observation and Geoinformation, 13(4): 552-559 [DOI: 10.1016/j.jag.2011.02.005http://dx.doi.org/10.1016/j.jag.2011.02.005]
Qin Z H, Dall’Olmo G, Karnieli A and Berliner P. 2001a. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. Journal of Geophysical Research: Atmospheres, 106(D19): 22655-22670 [DOI: 10.1029/2000JD900452http://dx.doi.org/10.1029/2000JD900452]
Qin Z H, Li W J, Xu B, Chen Z X and Liu J. 2004. The estimation of land surface emissivity for Landsat TM 6. Remote Sensing for Land & Resources, (3): 28-32, 36, 41
覃志豪, 李文娟, 徐斌, 陈仲新, 刘佳. 2004. 陆地卫星TM6波段范围内地表比辐射率的估计. 国土资源遥感, (3): 28-32, 36, 41 [DOI: 10.3969/j.issn.1001-070X.2004.03.007http://dx.doi.org/10.3969/j.issn.1001-070X.2004.03.007]
Qin Z H, Li W J, Zhang M H, Karnieli A and Berliner P. 2003. Estimating of the essential atmospheric parameters of mono-window algorithm for land surface temperature retrieval from Landsat TM 6. Remote Sensing for Land & Resources, (2): 37-43
覃志豪, 李文娟, 张明华, Karnieli A, Berliner P. 2003. 单窗算法的大气参数估计方法, 国土资源遥感, (2): 37-43 [DOI: 10.3969/j.issn.1001-070X.2003.02.010]
Qin Z H, Karnieli A and Berliner P. 2001b. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(8): 3719-3746 [DOI: 10.1080/01431160010006971http://dx.doi.org/10.1080/01431160010006971]
Qin Z H, Zhang M H, Karnieli A and Berliner P. 2001. Mono-window algorithm for retrieving land surface temperature from Landsat TM 6 data. Acta Geographica Sinica, 56(4): 456-466
覃志豪, 张明华, Karnieli A and Berliner P. 2001. 用陆地卫星TM6数据演算地表温度的单窗算法. 地理学报, 56(4): 456-466 [DOI: 10.11821/xb200104009http://dx.doi.org/10.11821/xb200104009]
Quan J L, Zhan W F, Ma T, Du Y Y, Guo Z and Qin B Y. 2018. An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes. Remote Sensing of Environment, 206: 403-423 [DOI: 10.1016/j.rse.2017.12.003http://dx.doi.org/10.1016/j.rse.2017.12.003]
Ren H Z, Du C, Liu R Y, Qin Q M, Meng J J, Li Z L and Yan G J. 2014. Evaluation of radiometric performance for the thermal infrared sensor onboard Landsat 8. Remote Sensing, 6(12): 12776-12788 [DOI: 10.3390/rs61212776http://dx.doi.org/10.3390/rs61212776]
Ren H Z, Du C, Liu R Y, Qin Q M, Yan G J, Li Z L and Meng J J. 2015. Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. Journal of Geophysical Research: Atmospheres, 120(5): 1723-1738 [DOI: 10.1002/2014JD022619http://dx.doi.org/10.1002/2014JD022619]
Ren H Z, Liu R Y, Qin Q M, Fan W J, Yu L and Du C. 2017. Mapping finer-resolution land surface emissivity using Landsat images in China. Journal of Geophysical Research: Atmospheres, 122(13): 6764-6781 [DOI: 10.1002/2017JD026910http://dx.doi.org/10.1002/2017JD026910]
Rodriguez-Galiano V, Pardo-Iguzquiza E, Sanchez-Castillo M, Chica-Olmo M and Chica-Rivas M. 2012. Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images. International Journal of Applied Earth Observation and Geoinformation, 18: 515-527 [DOI: 10.1016/j.jag.2011.10.002http://dx.doi.org/10.1016/j.jag.2011.10.002]
Rosas J, Houborg R and McCabe M F. 2017. Sensitivity of Landsat 8 surface temperature estimates to atmospheric profile data: a study using MODTRAN in dryland irrigated systems. Remote Sensing, 9(10): 988 [DOI: 10.3390/rs9100988http://dx.doi.org/10.3390/rs9100988]
Roujean J L, Leroy M and Deschamps P Y. 1992. A bidirectional reflectance model of the Earth’s surface for the correction of remote sensing data. Journal of Geophysics Research: Atmospheres, 97(D18): 20455-20468 [DOI: 10.1029/92JD01411http://dx.doi.org/10.1029/92JD01411]
Roy S, Pandit S, Eva E A, Bagmar S H, Papia M, Banik L, Dube T, Rahman F and Razi M A. 2020. Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan Area of Bangladesh using long term Landsat series data. Urban Climate, 32: 100593 [DOI: 10.1016/j.uclim.2020.100593http://dx.doi.org/10.1016/j.uclim.2020.100593]
Rozenstein O, Qin Z H, Derimian Y and Karnieli A. 2014. Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors, 14(4): 5768-5780 [DOI: 10.3390/s140405768http://dx.doi.org/10.3390/s140405768]
Sajib Q U and Wang T. 2020. Estimation of land surface temperature in an agricultural region of Bangladesh from Landsat 8: intercomparison of four algorithms. Sensors, 20(6): 1778 [DOI: 10.3390/s20061778http://dx.doi.org/10.3390/s20061778]
Scarano M and Sobrino J A. 2015. On the relationship between the sky view factor and the land surface temperature derived by Landsat-8 images in Bari, Italy. International Journal of Remote Sensing, 36(19/20): 4820-4835 [DOI: 10.1080/01431161.2015.1070325http://dx.doi.org/10.1080/01431161.2015.1070325]
Schaeffer B A, Iiames J, Dwyer J, Urquhart E, Salls W, Rover J and Seegers B. 2018. An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries. International Journal of Remote Sensing, 39(22): 7789-7805 [DOI: 10.1080/01431161.2018.1471545http://dx.doi.org/10.1080/01431161.2018.1471545]
Sekertekin A and Arslan N. 2019. Monitoring thermal anomaly and radiative heat flux using thermal infrared satellite imagery-A case study at Tuzla geothermal region. Geothermics, 78: 243-254 [DOI: 10.1016/j.geothermics.2018.12.014http://dx.doi.org/10.1016/j.geothermics.2018.12.014]
Sekertekin A and Bonafoni S. 2020a. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2): 294 [DOI: 10.3390/rs12020294http://dx.doi.org/10.3390/rs12020294]
Sekertekin A and Bonafoni S. 2020b. Sensitivity analysis and validation of daytime and nighttime land surface temperature retrievals from Landsat 8 using different algorithms and emissivity models. Remote Sensing, 12(17): 2776 [DOI: 10.3390/rs12172776http://dx.doi.org/10.3390/rs12172776]
Shivers S W, Roberts D A and McFadden J P. 2019. Using paired thermal and hyperspectral aerial imagery to quantify land surface temperature variability and assess crop stress within California orchards. Remote Sensing of Environment, 222: 215-231 [DOI: 10.1016/j.rse.2018.12.030http://dx.doi.org/10.1016/j.rse.2018.12.030]
Skoković D, Sobrino J A and Jiménez-Muñoz J C. 2017. Vicarious calibration of the Landsat 7 thermal infrared band and LST algorithm validation of the ETM+ instrument using three global atmospheric profiles. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1804-1811 [DOI: 10.1109/TGRS.2016.2633810http://dx.doi.org/10.1109/TGRS.2016.2633810]
Snyder W C, Wan Z, Zhang Y and Feng Y Z. 1998. Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19(14): 2753-2774 [DOI: 10.1080/014311698214497http://dx.doi.org/10.1080/014311698214497]
Sobrino J A, Coll C and Caselles V. 1991. Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment, 38(1): 19-34 [DOI: 10.1016/0034-4257(91)90069-Ihttp://dx.doi.org/10.1016/0034-4257(91)90069-I]
Sobrino J A and Jiménez-Muñoz J C. 2014. Minimum configuration of thermal infrared bands for land surface temperature and emissivity estimation in the context of potential future missions. Remote Sensing of Environment, 148: 158-167 [DOI: 10.1016/j.rse.2014.03.027http://dx.doi.org/10.1016/j.rse.2014.03.027]
Sobrino J A, Jiménez-Muñoz J C and Paolini L. 2004. Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment, 90(4): 434-440 [DOI: 10.1016/j.rse.2004.02.003http://dx.doi.org/10.1016/j.rse.2004.02.003]
Sobrino J A, Jiménez-Muñoz J C, Soria G, Romaguera M, Guanter L, Moreno J, Plaza A and Martinez P. 2008. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2): 316-327 [DOI: 10.1109/TGRS.2007.904834http://dx.doi.org/10.1109/TGRS.2007.904834]
Sobrino J A, Li Z L, Stoll M P and Becker F. 1996. Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. International Journal of Remote Sensing, 17(11): 2089-2114 [DOI: 10.1080/01431169608948760http://dx.doi.org/10.1080/01431169608948760]
Sobrino J A and Raissouni N. 2000. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, 21(2): 353-366 [DOI: 10.1080/014311600210876http://dx.doi.org/10.1080/014311600210876]
Son N T, Chen C F, Chen C R and Recinos L E. 2019. Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador. Geocarto International, 34(12): 1363-1383 [DOI: 10.1080/10106049.2018.1489421http://dx.doi.org/10.1080/10106049.2018.1489421]
Song L S, Zhao Z Z, Xu J B, Liu S M, Peng K J and Zhao K. 2014. Improvements in land surface temperature retrieval based on atmospheric water vapour content and atmospheric temperature. International Journal of Remote Sensing, 35(13): 4881-4904 [DOI: 10.1080/01431161.2014.930200http://dx.doi.org/10.1080/01431161.2014.930200]
Song T, Duan Z, Liu J Z, Shi J Z, Yan F, Sheng S J, Huang J and Wu W. 2015. Comparison of four algorithms to retrieve land surface temperature using Landsat 8 Satellite. Journal of Remote Sensing, 19(3): 451-464
宋挺, 段峥, 刘军志, 石浚哲, 严飞, 盛世杰, 黄君, 吴蔚. 2015. Landsat 8数据地表温度反演算法对比. 遥感学报, 19(3): 451-464 [DOI: 10.11834/jrs.20154180http://dx.doi.org/10.11834/jrs.20154180]
Sun D L and Pinker R T. 2003. Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8). Journal of Geophysical Research: Atmospheres, 108: 4326 [DOI: 10.1029/2002JD002422http://dx.doi.org/10.1029/2002JD002422]
Terfa B K, Chen N C, Zhang X and Niyogi D. 2020. Spatial configuration and extent explains the urban heat mitigation potential due to green spaces: analysis over Addis Ababa, Ethiopia. Remote Sensing, 12(18): 2876 [DOI: 10.3390/rs12182876http://dx.doi.org/10.3390/rs12182876]
Urbanski J A, Wochna A, Bubak I, Grzybowski W, Lukawska-matuszewska K, Łącka M, Śliwińska S, Wojtasiewicz B and Zajączkowski M. 2016. Application of Landsat 8 imagery to regional-scale assessment of lake water quality. International Journal of Applied Earth Observation and Geoinformation, 51: 28-36 [DOI: 10.1016/j.jag.2016.04.004http://dx.doi.org/10.1016/j.jag.2016.04.004]
USGS. 2013. Landsat: a global land-imaging mission[EB/OL].http://pubs.usgs.gov/fs/2012/3072/fs2012-3072.pdfhttp://pubs.usgs.gov/fs/2012/3072/fs2012-3072.pdf
USGS. 2018a. Landsat 8 calibration parameter file data format control book[EB/OL].https://www.usgs.gov/media/files/landsat-8-calibration-parameter-file-data-format-control-bookhttps://www.usgs.gov/media/files/landsat-8-calibration-parameter-file-data-format-control-book
USGS. 2018b. Landsat 7 calibration parameter file definition[EB/OL].https://www.usgs.gov/media/files/landsat-7-calibration-parameter-file-definitionhttps://www.usgs.gov/media/files/landsat-7-calibration-parameter-file-definition
USGS. 2018c. Landsat 4-5 TM calibration parameter file definition[EB/OL].https://www.usgs.gov/media/files/landsat-4-5-TM-calibration-parameter-file-definitionhttps://www.usgs.gov/media/files/landsat-4-5-TM-calibration-parameter-file-definition
Valor E and Caselles V. 1996. Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sensing of Environment, 57(3): 167-184 [DOI: 10.1016/0034-4257(96)00039-9http://dx.doi.org/10.1016/0034-4257(96)00039-9]
Van de Griend A A and Owe M. 1993. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing, 14(6): 1119-1131 [DOI: 10.1080/01431169308904400http://dx.doi.org/10.1080/01431169308904400]
Vanhellemont Q. 2020. Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS. ISPRS Journal of Photogrammetry and Remote Sensing, 166: 390-402 [DOI: 10.1016/j.isprsjprs.2020.06.007http://dx.doi.org/10.1016/j.isprsjprs.2020.06.007]
Varade D and Dikshit O. 2020. Assessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using landsat-8 data. Geocarto International, 35(6): 641-662 [DOI: 10.1080/10106049.2018.1520928http://dx.doi.org/10.1080/10106049.2018.1520928]
Voogt J A and Oke T R. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370-384 [DOI: 10.1016/S0034-4257(03)00079-8http://dx.doi.org/10.1016/S0034-4257(03)00079-8]
Vu T D and Nguyen T T. 2018. Spatio-temporal changes of underground coal fires during 2008-2016 in Khanh Hoa coal field (North-east of Viet Nam) using Landsat time-series data. Journal of Mountain Science, 15(12): 2703-2720 [DOI: 10.1007/s11629-018-4997-zhttp://dx.doi.org/10.1007/s11629-018-4997-z]
Wan Z M and Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 892-905 [DOI: 10.1109/36.508406http://dx.doi.org/10.1109/36.508406]
Wan Z M and Li Z L. 1997. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 980-996 [DOI: 10.1109/36.602541http://dx.doi.org/10.1109/36.602541]
Wan Z M, Zhang Y L, Zhang Q C and Li Z L. 2002. Validation of the land-surface temperature products retrieved from TERRA moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 83(1/2): 163-180 [DOI: 10.1016/S0034-4257(02)00093-7http://dx.doi.org/10.1016/S0034-4257(02)00093-7]
Wang F, Qin Z H, Song C Y, Tu L L, Karnieli A and Zhao S H. 2015a. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sensing, 7(4): 4268-4289 [DOI: 10.3390/rs70404268http://dx.doi.org/10.3390/rs70404268]
Wang K, Jiang Q G, Yu D H, Yang Q L, Wang L, Han T C and Xu X Y. 2019a. Detecting daytime and nighttime land surface temperature anomalies using thermal infrared remote sensing in Dandong geothermal prospect. International Journal of Applied Earth Observation and Geoinformation, 80: 196-205 [DOI: 10.1016/j.jag.2019.03.016http://dx.doi.org/10.1016/j.jag.2019.03.016]
Wang L, Lu Y and Yao Y L. 2019b. Comparison of three algorithms for the retrieval of land surface temperature from Landsat 8 images. Sensors, 19(22): 5049 [DOI: 10.3390/s19225049http://dx.doi.org/10.3390/s19225049]
Wang L T, Gao W and Zhuang C X. 2020. Quantitative research on the relationship between urban heat island effect and land use in Jizhou district of Tianjin based on Landsat-8 data. Geomatics & Spatial Information Technology, 43(12): 90-92
王力涛, 高伟, 庄春晓. 2020. 基于Landsat-8数据的天津市蓟州区城市热岛效应与土地利用的定量研究. 测绘与空间地理信息, 43(12): 90-92 [DOI: 10.3969/j.issn.1672-5867.2020.12.025http://dx.doi.org/10.3969/j.issn.1672-5867.2020.12.025]
Wang L X, Sun J H, Liu Z, Zhang S C and Yang Y. 2019. Comparison of several different algorithms to retrieve land surface emissivity using Landsat 8 data. Journal of Xi’an University of Science and Technology, 39(2): 327-333
王丽霞, 孙津花, 刘招, 张双成, 杨耘. 2019. 基于Landsat 8数据反演地表发射率的几种不同算法对比分析. 西安科技大学学报, 39(2): 327-333 [DOI: 10.13800/j.cnki.xakjdxxb.2019.0220http://dx.doi.org/10.13800/j.cnki.xakjdxxb.2019.0220]
Wang M M, He G J, Zhang Z M, Wang G Z and Long T F. 2015b. NDVI-based split-window algorithm for precipitable water vapour retrieval from Landsat-8 TIRS data over land area. Remote Sensing Letters, 6(11): 904-913 [DOI: 10.1080/2150704X.2015.1089363http://dx.doi.org/10.1080/2150704X.2015.1089363]
Wang M M, He G J, Zang Z M, Wang G Z, Yin R Y and Long T F. 2017. Atmospheric water vapor retrieval from Landsat-8 TIRS data using split-window algorithm. Remote Sensing Technology and Application, 32(1): 166-172
王猛猛, 何国金, 张兆明, 王桂周, 尹然宇, 龙腾飞. 2017. 基于Landsat 8 TIRS数据的大气水汽含量反演劈窗算法.遥感技术与应用,32(1):166-172 [DOI: 10.11873/j.issn.1004-0323.2017.1.0166http://dx.doi.org/10.11873/j.issn.1004-0323.2017.1.0166]
Wang M M, Zhang Z M, He G J, Wang G Z, Long T F and Peng Y. 2016. An enhanced single-channel algorithm for retrieving land surface temperature from Landsat series data. Journal of Geophysical Research: Atmospheres, 121(19): 11712-11722 [DOI: 10.1002/2016JD025270http://dx.doi.org/10.1002/2016JD025270]
Wang M M, Zhang Z J, Hu T and Liu X G. 2019c. A practical single-channel algorithm for land surface temperature retrieval: application to Landsat series data. Journal of Geophysical Research: Atmospheres, 124(1): 299-316 [DOI: 10.1029/2018JD029330http://dx.doi.org/10.1029/2018JD029330]
Wang M M, Zhang Z J, Hu T, Wang G Z, He G J, Zhang Z M, Li H, Wu Z J and Liu X G. 2020. An efficient framework for producing Landsat-based land surface temperature data using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 4689-4701 [DOI: 10.1109/JSTARS.2020.3014586http://dx.doi.org/10.1109/JSTARS.2020.3014586]
Wang T X, Shi J C, Ma Y, Husi L, Comyn-Platt E, Ji D B, Zhao T J and Xiong C. 2019d. Recovering land surface temperature under cloudy skies considering the solar-cloud-satellite geometry: application to MODIS and Landsat-8 data. Journal of Geophysical Research: Atmospheres, 124(6): 3401-3416 [DOI: 10.1029/2018JD028976http://dx.doi.org/10.1029/2018JD028976]
Weng Q H and Fu P. 2014. Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data. Remote Sensing of Environment, 140: 267-278 [DOI: 10.1016/j.rse.2013.09.002http://dx.doi.org/10.1016/j.rse.2013.09.002]
Weng Q H, Fu P and Gao F. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment, 145: 55-67 [DOI: 10.1016/j.rse.2014.02.003http://dx.doi.org/10.1016/j.rse.2014.02.003]
Wu P H, Shen H F, Ai T H and Liu Y L. 2013. Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion. International Journal of Digital Earth, 6(S1): 113-133 [DOI: 10.1080/17538947.2013.783131http://dx.doi.org/10.1080/17538947.2013.783131]
Wu P H, Shen H F, Zhang L P and Göttsche F M. 2015a. Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sensing of Environment, 156: 169-181 [DOI: 10.1016/j.rse.2014.09.013http://dx.doi.org/10.1016/j.rse.2014.09.013]
Wu Y W, Wang N L, He J Q and Jiang X. 2015b. Estimating mountain glacier surface temperatures from Landsat-ETM+ thermal infrared data: a case study of Qiyi glacier, China. Remote Sensing of Environment, 163: 286-295 [DOI: 10.1016/j.rse.2015.03.026http://dx.doi.org/10.1016/j.rse.2015.03.026]
Wu Y W, Wang N L, Li Z, Chen A, Guo Z M and Qie Y F. 2019. The effect of thermal radiation from surrounding terrain on glacier surface temperatures retrieved from remote sensing data: a case study from Qiyi glacier, China. Remote Sensing of Environment, 231: 111267 [DOI: 10.1016/j.rse.2019.111267http://dx.doi.org/10.1016/j.rse.2019.111267]
Wulder M A, Loveland T R, Roy D P, Crawford C J, Masek J G, Woodcock C E, Allen R G, Anderson M C, Belward A S, Cohen W B, Dwyer J, Erb A, Gao F, Griffiths P, Helder D, Hermosilla T, Hipple J D, Hostert P, Hughes M J, Huntington J, Johnson D M, Kennedy R, Kilic A, Li Z, Lymburner L, McCorkel J, Pahlevan N, Scambos T A, Schaaf C, Schott J R, Sheng Y W, Storey J, Vermote E, Vogelmann J, White J C, Wynne R H and Zhu Z. 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225: 127-147 [DOI: 10.1016/j.rse.2019.02.015http://dx.doi.org/10.1016/j.rse.2019.02.015]
Xu H Q. 2010. Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI). Photogrammetric Engineering and Remote Sensing, (5): 557-565 [DOI: 10.14358/PERS.76.5.557http://dx.doi.org/10.14358/PERS.76.5.557]
Xu H Q. 2015. Retrieval of the reflectance and land surface temperature of the newly-launched Landsat 8 satellite. Chinese Journal of Geophysics, 58(3): 741-747
徐涵秋. 2015. 新型Landsat8卫星影像的反射率和地表温度反演. 地球物理学报, 58(3): 741-747 [DOI: 10.6038/cjg20150304http://dx.doi.org/10.6038/cjg20150304]
Xu H Q. 2016. Change of Landsat 8 TIRS calibration parameters and its effect on land surface temperature retrieval. Journal of Remote Sensing, 20(2): 229-235
徐涵秋. 2016. Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响. 遥感学报, 20(2): 229-235 [DOI: 10.11834/jrs.20165165http://dx.doi.org/10.11834/jrs.20165165]
Xu H Q, Lin Z L and Pan W H. 2015. Some issue in land surface temperature retrieval of Landsat thermal data with the single-channel algorithm. Geomatics and Information Science of Wuhan University, 40(4): 487-492
徐涵秋, 林中立, 潘卫华. 2015. 单通道算法地表温度反演的若干问题讨论—以Landsat系列数据为例. 武汉大学学报, 40(4): 487-492 [DOI: 10.13203/j.whugis20130733http://dx.doi.org/10.13203/j.whugis20130733]
Yan G J, Wang T X, Jiao Z H, Mu X H, Zhao J and Chen L. 2016. Topographic radiation modeling and spatial scaling of clear-sky land surface longwave radiation over rugged terrain. Remote Sensing of Environment, 172: 15-27 [DOI: 10.1016/j.rse.2015.10.026http://dx.doi.org/10.1016/j.rse.2015.10.026]
Yan L and Roy D P. 2020. Spatially and temporally complete Landsat reflectance time series modelling: the fill-and-fit approach. Remote Sensing of Environment, 241: 111718 [DOI: 10.1016/j.rse.2020.111718http://dx.doi.org/10.1016/j.rse.2020.111718]
Yang J J, Duan S B, Zhang X Y, Wu P H, Huang C, Leng P and Gao M F. 2020. Evaluation of seven atmospheric profiles from reanalysis and satellite-derived products: implication for single-channel land surface temperature retrieval. Remote Sensing, 12(5): 791 [DOI: 10.3390/rs12050791http://dx.doi.org/10.3390/rs12050791]
Yang J X, Wong M S, Menenti M and Nichol J. 2015a. Modeling the effective emissivity of the urban canopy using sky view factor. ISPRS Journal of Photogrammetry and Remote Sensing, 105: 211-219 [DOI: 10.1016/j.isprsjprs.2015.04.006http://dx.doi.org/10.1016/j.isprsjprs.2015.04.006]
Yang J X, Wong M S, Menenti M and Nichol J. 2015b. Study of the geometry effect on land surface temperature retrieval in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 109: 77-87 [DOI: 10.1016/j.isprsjprs.2015.09.001http://dx.doi.org/10.1016/j.isprsjprs.2015.09.001]
Yang J X, Wong M S, Menenti M, Nichol J, Voogt J, Krayenhoff E S and Chan P W. 2016. Development of an improved urban emissivity model based on sky view factor for retrieving effective emissivity and surface temperature over urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 122: 30-40 [DOI: 10.1016/j.isprsjprs.2016.09.007http://dx.doi.org/10.1016/j.isprsjprs.2016.09.007]
Yin C L, Meng F and Yu Q R. 2020. Calculation of land surface emissivity and retrieval of land surface temperature based on a spectral mixing model. Infrared Physics and Technology, 108: 103333 [DOI: 10.1016/j.infrared.2020.103333http://dx.doi.org/10.1016/j.infrared.2020.103333]
Yin Z X, Wu P H, Foody G M, Wu Y L, Liu Z H, Du Y and Ling F. 2021. Spatiotemporal fusion of land surface temperature based on a convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 59(2): 1808-1822 [DOI: 10.1109/TGRS.2020.2999943http://dx.doi.org/10.1109/TGRS.2020.2999943]
Yu X L, Guo X L and Wu Z C. 2014. Land surface temperature retrieval from Landsat 8 TIRS-comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10): 9829-9852 [DOI: 10.3390/rs6109829http://dx.doi.org/10.3390/rs6109829]
Zareie S, Rangzan K, Khosravi H and Sherbakov V M. 2018. Comparison of split window algorithms to derive land surface temperature from satellite TIRS data. Arabian Journal of Geosciences, 11: 391 [DOI: 10.1007/s12517-018-3732-yhttp://dx.doi.org/10.1007/s12517-018-3732-y]
Zeng C, Long D, Shen H F, Wu P H, Cui Y K and Hong Y. 2018. A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud. ISPRS Journal of Photogrammetry and Remote Sensing, 141: 30-45 [DOI: 10.1016/j.isprsjprs.2018.04.005http://dx.doi.org/10.1016/j.isprsjprs.2018.04.005]
Zhang Y S, Balzter H, Liu B and Chen Y J. 2017. Analyzing the impacts of urbanization and seasonal variation on land surface temperature based on subpixel fractional covers using Landsat images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4): 1344-1356 [DOI: 10.1109/JSTARS.2016.2608390http://dx.doi.org/10.1109/JSTARS.2016.2608390]
Zhang Z M and He G J. 2013. Generation of Landsat surface temperature product for China, 2000-2010. International Journal of Remote Sensing, 34(20): 7369-7375 [DOI: 10.1080/01431161.2013.820368http://dx.doi.org/10.1080/01431161.2013.820368]
Zhang Z M, He G J, Peng Y, Long T F, Wang M M and Wei M Y. 2020. Landsat surface temperature products over china. China Science Data, 5(4): 74-82
张兆明, 何国金, 彭燕, 龙腾飞, 王猛猛, 魏明月. 2020. 中国区域Landsat地表温度产品. 中国科学数据(中英文网络版), 5(4): 74-82 [DOI: 10.11922/csdata.2020.0030.zhhttp://dx.doi.org/10.11922/csdata.2020.0030.zh]
Zhao W, Li A N, Jin H A, Zhang Z J, Bian J H and Yin G F. 2017. Performance evaluation of the triangle-based empirical soil moisture relationship models based on Landsat-5 TM data and in situ measurements. IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2632-2645 [DOI: 10.1109/TGRS.2017.2649522http://dx.doi.org/10.1109/TGRS.2017.2649522]
Zhao W, Li A N, Zhang Z Z, Bian J H, Jin H A, Yin G F, Nan X and Lei G B. 2016. A study on land surface temperature terrain effect over mountainous area based on Landsat 8 thermal infrared data. Remote Sensing Technology and Application, 31(1): 63-73
赵伟, 李爱农, 张正健, 边金虎, 靳华安, 尹高飞, 南希, 雷光斌. 2016. 基于Landsat 8热红外遥感数据的山地地表温度地形效应研究. 遥感技术与应用, 31(1): 63-73 [DOI: 10.11873/j.issn.1004-0323.2016.1.0063http://dx.doi.org/10.11873/j.issn.1004-0323.2016.1.0063]
Zheng X P, Gao M F, Li Z L, Chen K S, Zhang X and Shang G F. 2020. Impact of 3-D structures and their radiation on thermal infrared measurements in urban areas. IEEE Transactions on Geoscience and Remote Sensing, 58(12): 8412-8426 [DOI: 10.1109/TGRS.2020.2987880http://dx.doi.org/10.1109/TGRS.2020.2987880]
Zhou J, Li J, Zhang L X, Hu D Y and Zhan W F. 2012. Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere. International Journal of Remote Sensing, 33(8): 2582-2602 [DOI: 10.1080/01431161.2011.617396http://dx.doi.org/10.1080/01431161.2011.617396]
Zhou Y, Qin Z H and Bao G. 2014. Progress in retrieving land surface temperature for the cloud-covered pixels from thermal infrared remote sensing data. Spectroscopy and Spectral Analysis, 34(2): 364-369
周义, 覃志豪, 包刚. 2014. 热红外遥感图像中云覆盖像元地表温度估算研究进展. 光谱学与光谱分析, 34(2): 364-369) [DOI: 10.3964/j.issn.1000-0593(201402-0364-06http://dx.doi.org/10.3964/j.issn.1000-0593(2014)02-0364-06]
Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623 [DOI: 10.1016/j.rse.2010.05.032http://dx.doi.org/10.1016/j.rse.2010.05.032]
Zhu X L, Duan S-B, Li Z-L, Zhao W, Wu H, Leng P, Gao M-F and Zhou X M. 2021. Retrieval of land surface temperature with topographic effect correction from Landsat 8 thermal infrared data in mountainous areas. IEEE Transactions on Geoscience and Remote Sensing, 59(8): 6674-6687. [DOI: 10.1109/TGRS.2020.3030900http://dx.doi.org/10.1109/TGRS.2020.3030900]
Zhu Z and Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118: 83-94 [DOI: 10.1016/j.rse.2011.10.028http://dx.doi.org/10.1016/j.rse.2011.10.028]
Zhu Z, Wang S X and Woodcock C E. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159: 269-277 [DOI: 10.1016/j.rse.2014.12.014http://dx.doi.org/10.1016/j.rse.2014.12.014]
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