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Jiaheng ZHU, Shanyou ZHU, Fachuan YU, et al. A downscaling method for ERA5 reanalysis land surface temperature over urban and mountain areas. [J]. National Remote Sensing Bulletin 25(8):1778-1791(2021)
地表温度是陆面过程动态模拟、区域和全球变化分析等研究领域的重要参数，如何获得高时空分辨率地表温度数据一直是研究热点问题。选择河北省张家口市城郊区、山区两个区域作为试验研究区，在ERA5 0.1°分辨率地表温度订正基础上，构建随机森林降尺度模型以实现多层级分辨率的地表温度降尺度，并与Landsat 8 TIRS反演地表温度进行对比分析，以探讨再分析地表温度在不同分辨率、不同下垫面类型上的降尺度效果。结果表明：不同分辨率的降尺度结果都能够准确表达地表温度相对高低的空间分布特征，纹理精度显著提高，但降尺度误差随着空间分辨率提高而逐渐增大，城郊区和山区的降尺度均方根误差变化范围分别为1.16—1.79 ℃、1.61—2.49 ℃，地表温度高值与低值区域分别存在着低估和高估现象；随机森林降尺度模型中的参数重要性随尺度变化不大，总体表现为两个区域中植被指数NDVI的重要性都比较大，而海拔高度在山区区域对降尺度模型的影响更大。
Land Surface Temperature (LST) is an important parameter in many research fields such as dynamic simulation of land surface processes, regional and global change analysis. It has always been a hot research topic on how to obtain land surface temperature with higher spatio-temporal resolution. Due to the limitation in the availability of satellite imagery data with high spatial and temporal resolution simultaneously, LST downscaling from coarse spatial resolution data is an effective method. Besides LST retrieved from microwave channels or thermal infrared bands, reanalysis dataset can provide long time series of hourly land surface temperature. If the reanalysis LST can be downscaled to produce reliable products with higher spatial resolution or not needs to be further studied.,To compare downscaling results at various resolutions from raw reanalysis LST, two different regions in Zhangjiakou, Hebei province were selected as the test areas that represents the urban-rural and the mountainous characteristics respectively. LST at 100 m spatial resolution was retrieved by using Landsat 8 OLI/TIRS data through Mono-Window (MW) algorithm, which was then upscaled to different resolutions of 200 m, 500 m, 1000 m, 2000 m, 5000 m and 10000 m respectively. ERA5 LST data at the resolution of 10000 m is corrected based on Landsat 8 LST, which is downscaled to 6 resolutions by constructing and applying the random forest model. Elevation and six remotely sensed indices including NDVI, MASVI, MNDWI, NMDI, NDBI, NDBSI calculating from the corresponding OLI spectral reflectance were taken as the random forest model parameters. LST downscaling precisions at different spatial resolution within various landcover regions were then evaluated and discussed by using the derived Landsat 8 LST as the reference, and the feature importance of seven land surface parameters in random forest models changed with scales were also analyzed by comparing Gini index.,The maximum, minimum and average values of the corrected ERA5 LST and the reference Landsat 8 LST are close at the resolution of 10000 m, but the standard deviation is lower than that of reference LST. The downscaling results of different resolutions can accurately express the LST spatial distribution characteristics for the two experiment areas. As the spatial resolution changes from 5000 m to 100 m, the downscaled LST texture accuracy is significantly improved, however Root Mean Square Error (RMSE) gradually increases and the correlation between the downscaled and the reference LST decreases. RMSE grows from 1.16 to 1.79 ℃ and from 1.61 to 2.49 ℃ for the urban-rural area and the mountains area respectively. For the random forest downscaling model, features importance have no significant change at different resolutions, which to some degree indicates that the random forest model has a relatively stable scale invariant characteristic. NDVI shows higher importance affecting LST distribution in two test areas, and elevation is the most important parameter in mountainous area.,The research results show that ERA5 LST and Landsat 8 LST have good consistency in the spatial distribution, which means ERA5 LST has the potentiality to be downscaled to express detailed land surface temperature at higher resolutions. While downscaling ERA5 LST to higher spatial resolution, larger underestimation and overestimation errors will occur in the high temperature area and low temperature area respectively.
ERA5再分析资料地表温度降尺度随机森林模型空间尺度
ERA5 reanalysis datasetsland surface temperaturedownscalerandom forestspatial scale
Agam N, Kustas W P, Anderson M C, Li F Q and Neale C M U. 2007. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment, 107(4): 545-558 [DOI: 10.1016/j.rse.2006.10.006http://dx.doi.org/10.1016/j.rse.2006.10.006]
Anderson M C, Allen R G, Morse A and Kustas W P. 2012. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment, 122: 50-65 [DOI: 10.1016/j.rse.2011.08.025http://dx.doi.org/10.1016/j.rse.2011.08.025]
Bindhu V M, Narasimhan B and Sudheer K P. 2013. Development and verification of a non-linear disaggregation method (NL-DisTrad) to downscale MODIS land surface temperature to the spatial scale of Landsat thermal data to estimate evapotranspiration. Remote Sensing of Environment, 135: 118-129 [DOI: 10.1016/j.rse.2013.03.023http://dx.doi.org/10.1016/j.rse.2013.03.023]
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324]
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]
Duan J K. 2019. Comparative analysis of land surface temperature retrieval algorithm based on Landsat8 Data——A case study in Beijing city. Anhui Agricultural Science Bulletin, 25(17): 148-150
段金馈. 2019. 基于Landsat8数据的地表温度反演算法对比分析——以北京市为例. 安徽农学通报, 25(17): 148-150 [DOI: 10.16377/j.cnki.issn1007-7731.2019.17.057http://dx.doi.org/10.16377/j.cnki.issn1007-7731.2019.17.057]
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
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 [DOI: 10.11834/jrs.20211296http://dx.doi.org/10.11834/jrs.20211296]
Fasbender D, Tuia D, Bogaert P and Kanevski M. 2008. Support-based implementation of Bayesian data fusion for spatial enhancement: applications to ASTER thermal images. IEEE Geoscience and Remote Sensing Letters, 5(4): 598-602 [DOI: 10.1109/LGRS.2008.2000739http://dx.doi.org/10.1109/LGRS.2008.2000739]
Fassnacht K S, Gower S T, MacKenzie M D, Nordheim E V and Lillesand T M. 1997. Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper. Remote Sensing of Environment, 61(2): 229-245 [DOI: 10.1016/S0034-4257(97)00005-9http://dx.doi.org/10.1016/S0034-4257(97)00005-9]
Gillies R R and Carlson T N. 1995. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology and Climatology, 34(4): 745-756 [DOI: 10.1175/1520-0450(1995)034<0745:TRSOSS>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1995)034<0745:TRSOSS>2.0.CO;2]
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 C Y, Shi P J, Xie D Y and Zhao Y Y. 2010. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sensing Letters, 1(4): 213-221 [DOI: 10.1080/01431161.2010.481681http://dx.doi.org/10.1080/01431161.2010.481681]
Hua J W, Zhu S Y and Zhang G X. 2018. Downscaling land surface temperature based on random forest algorithm. Remote Sensing for Land and Resources, 30(1): 78-86
华俊玮, 祝善友, 张桂欣. 2018. 基于随机森林算法的地表温度降尺度研究. 国土资源遥感, 30(1): 78-86 [DOI: 10.6046/gtzyyg.2018.01.11http://dx.doi.org/10.6046/gtzyyg.2018.01.11]
Hutengs C and Vohland M. 2016. Downscaling land surface temperatures at regional scales with random forest regression. Remote Sensing of Environment, 178: 127-141 [DOI: 10.1016/j.rse.2016.03.006http://dx.doi.org/10.1016/j.rse.2016.03.006]
Johannsen F, Ermida S, Martins J P A, Trigo I F, Nogueira M and Dutra E. 2019. Cold bias of ERA5 summertime daily maximum land surface temperature over iberian peninsula. Remote Sensing, 11(21): 2570 [DOI: 10.3390/rs11212570http://dx.doi.org/10.3390/rs11212570]
Kustas W P, Norman J M, Anderson M C and French A N. 2003. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sensing of Environment, 85(4): 429-440 [DOI: 10.1016/S0034-4257(03)00036-1http://dx.doi.org/10.1016/S0034-4257(03)00036-1]
Li W, Niu L, Chen H and Wu H. 2020. Robust downscaling method of land surface temperature by using random forest algorithm. Journal of Geo-information Science, 22(8): 1666-1678
李婉, 牛陆, 陈虹, 吴骅. 2020. 基于随机森林算法的地表温度鲁棒降尺度方法. 地球信息科学学报, 22(8): 1666-1678 [DOI: 10.12082/dqxxkx.2020.190142http://dx.doi.org/10.12082/dqxxkx.2020.190142]
Li Z L, Duan S B, Tang B H, Wu H, Ren H Z, Yan G J, Tang R L and Leng P. 2016. Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing, 20(5): 899-920
李召良, 段四波, 唐伯惠, 吴骅, 任华忠, 阎广建, 唐荣林, 冷佩. 2016. 热红外地表温度遥感反演方法研究进展. 遥感学报, 20(5): 899-920 [DOI:10.11834/jrs.20166192http://dx.doi.org/10.11834/jrs.20166192]
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]
Mukherjee S, Joshi P K and Garg R D. 2015. Regression-Kriging technique to downscale satellite-derived land surface temperature in heterogeneous agricultural landscape. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3): 1245-1250 [DOI: 10.1109/JSTARS.2015.2396032http://dx.doi.org/10.1109/JSTARS.2015.2396032]
Pardo-Igúzquiza E, Chica-Olmo M and Atkinson P M. 2006. Downscaling cokriging for image sharpening. Remote Sensing of Environment, 102(1/2): 86-98 [DOI: 10.1016/j.rse.2006.02.014http://dx.doi.org/10.1016/j.rse.2006.02.014]
Pereira O J R, Melfi A J, Montes C R and Lucas Y. 2018. Downscaling of ASTER thermal images based on geographically weighted regression kriging. Remote Sensing, 10(4): 633 [DOI: 10.3390/rs10040633http://dx.doi.org/10.3390/rs10040633]
Qin Z H, Karnieli A and Berliner P. 2001. 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(18): 3719-3746 [DOI: 10.1080/01431160010006971http://dx.doi.org/10.1080/01431160010006971]
Qin Z H, Li W J, Xu B, Chen Z X and Liu J. 2004. The estimation of land surface emissivity for Landsat TM6. Remote Sensing for Land and Resources, 16(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, 16(3): 28-32, 36, 41 [DOI: 10.3969/j.issn.1001-070X.2004.03.007]
Quan J L, Zhan W F, Chen Y H and Liu W Y. 2013. Downscaling remotely sensed land surface temperatures: a comparison of typical methods. Journal of Remote Sensing, 17(2): 361-387
全金玲, 占文凤, 陈云浩, 刘闻雨. 2013. 遥感地表温度降尺度方法比较——性能对比及适应性评价. 遥感学报, 17(2): 361-387 [DOI: 10.11834/jrs.20132007http://dx.doi.org/10.11834/jrs.20132007]
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]
Sobrino J A, Raissouni N and Li Z L. 2001. A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing of Environment, 75(2): 256-266 [DOI: 10.1016/S0034-4257(00)00171-1http://dx.doi.org/10.1016/S0034-4257(00)00171-1]
Stathopoulou M, Cartalis C and Petrakis M. 2007. Integrating Corine Land Cover data and Landsat TM for surface emissivity definition: application to the urban area of Athens, Greece. International Journal of Remote Sensing, 28(15): 3291-3304 [DOI: 10.1080/01431160600993421http://dx.doi.org/10.1080/01431160600993421]
Steven M D, Malthus T J, Baret F, Xu H and Chopping M J. 2003. Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, 88(4): 412-422 [DOI: 10.1016/j.rse.2003.08.010http://dx.doi.org/10.1016/j.rse.2003.08.010]
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]
Wang F, Qin Z, Song C, Tu L, Karnieli A and Zhao S. 2015. 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 Y T, Xie D H and Li Y H. 2014. Downscaling remotely sensed land surface temperature over urban areas using trend surface of spectral index. Journal of Remote Sensing, 18(6): 1169-1181
王祎婷, 谢东辉, 李亚惠. 2014. 光谱指数趋势面的城市地表温度降尺度转换. 遥感学报, 18(6): 1169-1181 [DOI: 10.11834/jrs.20144115http://dx.doi.org/10.11834/jrs.20144115]
Weng Q H and Fu P. 2014. Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 97: 78-88 [DOI: 10.1016/j.isprsjprs.2014.08.009http://dx.doi.org/10.1016/j.isprsjprs.2014.08.009]
Wu H, Li X J, Li Z L, Duan S B and Qian Y G. 2021. Hyperspectral thermal infrared remote sensing:current status and perspectives. National Remote Sensing Bulletin, 25(8): 1567-1590
吴骅, 李秀娟, 李召良, 段四波, 钱永刚. 2021. 高光谱热红外遥感: 现状与展望. 遥感学报, 25(8): 1567-1590 [DOI: 10.11834/jrs.20211306http://dx.doi.org/10.11834/jrs.20211306]
Xu H Q. 2005. A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5): 589-595
徐涵秋. 2005. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究. 遥感学报, 9(5): 589-595 [DOI: 10.11834/jrs.20050586http://dx.doi.org/10.11834/jrs.20050586]
Xu H Q. 2013. A remote sensing urban ecological index and its application. Acta Ecologica Sinica, 33(24): 7853-7862
徐涵秋. 2013. 城市遥感生态指数的创建及其应用. 生态学报, 33(24): 7853-7862 [DOI: 10.5846/stxb201208301223http://dx.doi.org/10.5846/stxb201208301223]
Yang G J, Pu R L, Zhao C J, Huang W J and Wang J H. 2011. Estimation of subpixel land surface temperature using an endmember index based technique: a case examination on ASTER and MODIS temperature products over a heterogeneous area. Remote Sensing of Environment, 115(5): 1202-1219 [DOI: 10.1016/j.rse.2011.01.004http://dx.doi.org/10.1016/j.rse.2011.01.004]
Yang Y B, Cao C, Pan X, Li X H and Zhu X. 2017. Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression. Remote Sensing, 9(8): 789 [DOI: 10.3390/rs9080789http://dx.doi.org/10.3390/rs9080789]
Zhan W F, Chen Y H, Zhou J, Wang J F, Liu W Y, Voogt J, Zhu X L, Quan J L and Li J. 2013. Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats. Remote Sensing of Environment, 131: 119-139 [DOI: 10.1016/j.rse.2012.12.014http://dx.doi.org/10.1016/j.rse.2012.12.014]
Zhen Z J, Chen S B, Qin W H, Li J, Mike M and Yang B P. 2019. A modified transformed soil adjusted vegetation index for cropland in Jilin Province, China. Acta Geologica Sinica-English Edition, 93(S3): 173-176 [DOI: 10.1111/1755-6724.14281http://dx.doi.org/10.1111/1755-6724.14281]
Zhu J and Yuan H Z. 2019. Applicability of ERA reanalysis data of land surface temperature in Zhejiang Province. Meteorological Science and Technology, 47(2): 289-298
朱景, 袁慧珍. 2019. ERA再分析陆面温度资料在浙江省的适用性. 气象科技, 47(2): 289-298 [ DOI: 10.19517/j.1671-6345.20180171http://dx.doi.org/10.19517/j.1671-6345.20180171]
Zhu J S, Ren H Z, Ye X, Zeng H, Nie J, Jiang C C and Guo J X. 2021. Ground validation of land surface temperature and surface emissivity from thermal infrared remote sensing data: A review. National Remote Sensing Bulletin, 25(8): 1538-1566
朱金顺, 任华忠, 叶昕, 曾晖, 聂婧, 蒋晨琛, 郭金鑫. 2021. 热红外遥感地表温度与发射率地面验证进展. 遥感学报, 25(8): 1538-1566 [DOI: 10.11834/jrs.20211299http://dx.doi.org/10.11834/jrs.20211299]
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