收稿：2021-04-26，
纸质出版：2021-08-07
移动端阅览
以光谱指数为趋势面因子的降尺度方法被广泛用于遥感地表温度尺度转换中，但面临构建的光谱指数难以凸显地表温度分布规律、浅层的统计模型难以精准刻画趋势面因子与地表温度之间的复杂关系的不足。为此，本文以Landsat 8 ARD 地表温度产品为降尺度对象，以Landsat 8 OLI原始数据为潜在趋势面因子，构建地表温度降尺度残差网络（LSTDRN）的深度学习模型；探索适用于Landsat 8地表温度产品空间降尺度的趋势面波段或组合，并在不同季节、不同地表类型下与经典传统方法TsHARP进行定量比较。结果表明：LSTDRN方法利用Landsat 8 OLI原始单波段作为趋势面因子就能有较好的降尺度效果，增加潜在趋势面因子的组合数量并不能提高降尺度效果。不同地表覆盖类型实验中，LSTDRN方法降尺度效果整体优于经典传统方法，且以近红外波段、红光波段和归一化植被指数为趋势面因子时，近红外波段降尺度效果定量评价表现最佳；不同地表覆盖类型的LSTDRN降尺度效果排序为：植被>建筑>水体，而经典传统方法则没有表现出明显的差异。不同季节实验中，LSTDRN方法在春夏冬3季的降尺度效果的定量评价表现明显好于经典传统方法，两类方法的秋季降尺度结果相当。因此，提出的LSTDRN对Landsat 8遥感地表温度产品具有较好的降尺度效果，整体优于经典传统方法且稳定性更强。
The downscaling method using spectral index as trend surface factor is widely used in remote sensing land surface temperature scale conversion. However
it is difficult to highlight the distribution of land surface temperature and describe the complex relationship between trend surface factor and land surface temperature in statistical model. Therefore
this paper constructs a Land Surface Temperature Downscaling Residual Network (LSTDRN) taking Landsat 8 ARD LST as downscaling objects and Landsat 8 OLI raw data as potential trend surface factors. The LSTDRN aims to explore the trend surface bands or combinations suitable for spatial downscaling of Landsat 8 land surface temperature products
and verify the spatiotemporal applicability of the model.
In view of the strong nonlinear relationship fitting and feature extraction ability of deep learning
this paper proposes a LST downscaling model based on deep learning. In the training stage
the relationship model between the Landsat 8 ARD LST and all bands of Landsat 8 OLI (except band 9) is fitted at the low resolution level. The Huber loss function is used to minimize the residual between the prediction results and the label to realize the transformation residual constraint. Then the optimal model is obtained through iterative learning and parameter adjustment. In the test stage
the optimal model is applied at the high resolution level to obtain the final downscaling results according to the “scale invariant” hypothesis of land surface temperature downscaling. In addition to visual evaluation
the downscaling results and original LST data were scaled up to 100 m resolution for quantitative evaluation. After evaluating the downscaling effect of each band
the multi-trend surface factor downscaling experiment was carried out. Meanwhile
the deep learning method is compared with the classic traditional method TsHARP to compare the stability of different land surface types and different seasons.
In LSTDRN
using the original single band of Landsat 8 OLI as the trend surface factor has a good downscaling effect
and increasing the number of potential trend surface factors can not improve the downscaling effect. In the experiments of different land cover types
the effect of deep learning method is better than that of traditional methods
and the best effect is when NIR band is the trend surface factor. The order of deep learning downscaling effect in quantitative evaluation of different land cover types is vegetation > building > water
however
there is no obvious difference between the traditional methods. In different season experiments
the downscaling effect in quantitative evaluation of deep learning method in spring
summer and winter is better than that of traditional classical methods
and the downscaling results of the two methods in autumn are similar. Therefore
the proposed LSTDRN has better downscaling effect on Landsat 8 remote sensing land surface temperature products
which is superior to the traditional classical methods and has stronger stability.
The LSTDRN proposed in this study can make good use of Landsat 8 OLI single band data as trend surface factors for downscaling. With the increase of trend surface factors
the downscaling effect is not significantly improved. Compared with the classic traditional method TsHARP
the downscaling effect of deep learning method is less affected by land cover types and seasonal factors
and the difference of downscaling effect is rather small in different spatial-temporal conditions. The method has stronger stability
which is conducive to enhancing the applicability of downscaling research and promoting the application of high-quality land surface temperature data.
Agam N , Kustas W P , Anderson M C , Li F and Neale CMU . 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.2006.10.006 http://dx.doi.org/10.1016/j.rse.2006.10.006 ]
Duan S B and Li Z L . 2016 . Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: Case study in Northern China, IEEE Trans . Geosci. Remote Sens , 54 ( 11 ): 6458 - 6469 [ DOI: 10.1109/TGRS.2016.2585198 http://dx.doi.org/10.1109/TGRS.2016.2585198 ]
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.20211296 http://dx.doi.org/10.11834/jrs.20211296 ]
Ebrahimy H and Azadbakht M . 2019 . Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels . Computers & Geosciences , 124 : 93 - 102 [ DOI: 10.1016/j.cageo.2019.01.004 http://dx.doi.org/10.1016/j.cageo.2019.01.004 ]
Guo H M , Gong A D , He R Y and Jiang J B . 2015 . Spatial Downscaling Research of the Remotely Land Surface Temperature . Remote Sensing Information , 000 ( 004 ): 29 - 36
郭会敏 , 宫阿都 , 何汝艳 , 蒋金豹 . 2015 . 遥感地表温度空间分辨率降尺度研究 . 遥感信息 , 000 ( 004 ): 29- 36 [ DOI: 10.3969/j.issn.1000-3177.2015.04.006 http://dx.doi.org/10.3969/j.issn.1000-3177.2015.04.006 ]
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.2017.04.18 http://dx.doi.org/10.6046/gtzyyg.2017.04.18 ]
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.2016.03.006 http://dx.doi.org/10.1016/j.rse.2016.03.006 ]
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 , 2020, 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 ( 08 ): 1666- 1678 [ DOI: 10.12082/dqxxkx.2020.190142 http://dx.doi.org/10.12082/dqxxkx.2020.190142 ]
Li X J , Xin X Z , Jiang T and Zhang H L . 2017 . Spatial Downscaling Research of Satellite Land Surface Temperature Based on Spectral Normalization Index . Acta Geodaetica et Cartographica Sinica , 46 ( 3 ): 353 - 361
李小军 , 辛晓洲 , 江涛 , 张海龙 . 2017 . 卫星遥感地表温度降尺度的光谱归一化指数法 . 测绘学报 , 2017 ( 03 ): 353- 361 [ DOI: 10.11947/j.AGCS.2017.20160196 http://dx.doi.org/10.11947/j.AGCS.2017.20160196 ]
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.20166192 http://dx.doi.org/10.11834/jrs.20166192 ]
Liu Q H , Xu X R and Chen J Y . 1998 . The Retrieval of Land Surface Temperature and Emissivity by Remote Sensing Data : Theory and Digital Simulation , 2 ( 1 ): 1 - 9
柳钦火 , 徐希孺 , 陈家宜 . 1998 . 遥测地表温度与比辐射率的迭代反演方法:理论推导与数值模拟 . 遥感学报 , 2 ( 1 ): 1- 9 [ DOI: 10.11834/jrs.19980101 http://dx.doi.org/10.11834/jrs.19980101 ]
Liu Y , Zhu R , Qian J X , Dang C Y and Yue H . 2020 . Land Surface Temperature Downscaling Based on Multiple Factors . Remote Sensing Information . 35 ( 06 ): 6 - 18
刘英 , 朱蓉 , 钱嘉鑫 , 党超亚 , 岳辉 . 2020 . 多因子地表温度降尺度研究 . 遥感信息 , 35 ( 06 ): 6- 18 [ DOI: 10.3969/j.issn.1000-3177.2020.06.002 http://dx.doi.org/10.3969/j.issn.1000-3177.2020.06.002 ]
Luo X B , Wang S M , Gao Y H and Chen Y . 2020 . Research on downscaling algorithm of land surface temperature based on the non-linear geographically weighted model . Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition) , 32 ( 06 ): 1003 - 1011
罗小波 , 王书敏 , 高阳华 , 陈圆 . 2020 . 基于局部非线性地理加权回归模型的地表温度降尺度算法研究 . 重庆邮电大学学报(自然科学版) , 32 ( 06 ): 1003- 1011 [ DOI: 10.3979/j.issn.1673-825X.2020.06.012 http://dx.doi.org/10.3979/j.issn.1673-825X.2020.06.012 ]
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 ): 1 - 1 [ DOI:10.1109/JSTARS.2015.2396032]
Peng Y D , Li W S , Luo X B and Li H . 2019 . A Geographically and Temporally Weighted Regression Model for Spatial Downscaling of MODIS Land Surface Temperatures Over Urban Heterogeneous Regions, IEEE transactions on geoscience and remote sensing: a publication of the IEEE Geoscience and Remote Sensing Society , 57 ( 7 ): 5012 - 5027 [ DOI: 10.1109/TGRS.2019.2895351 http://dx.doi.org/10.1109/TGRS.2019.2895351 ]
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 . 遥感学报 , 17 ( 2 ): 361- 387 [ DOI: CNKI:SUN:YGXB.0.2013-02-012 http://dx.doi.org/CNKI:SUN:YGXB.0.2013-02-012 ]
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.20144115 http://dx.doi.org/10.11834/jrs.20144115 ]
Wang Z H , Qin Q M , Sun Y H , Zhang T Y and Ren H Z . 2018 . Downscaling of Remotely Sensed Land Surface Temperature with the BP Neural Network . Remote Sensing Technology and Application , 2018, 33 ( 5 ): 793 - 802
汪子豪 , 秦其明 , 孙元亨 , 张添源 , 任华忠 . 2018 . 基于BP神经网络的地表温度空间降尺度方法 . 遥感技术与应用 , 2018, 33 ( 5 ): 793-802 [ DOI: 10.11873/j.issn.1004-0323.2018.5.0793 http://dx.doi.org/10.11873/j.issn.1004-0323.2018.5.0793 ]
Wu J H , Zhong B , Tian S F , Yang A X and Wu J J . 2019 . Downscaling of Urban Land Surface Temperature Based on Multi-Factor Geographically Weighted Regression, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 2019 , 12 ( 8 ): 2897 - 2911 [ DOI: 10.1109/JSTARS.2019.2919936 http://dx.doi.org/10.1109/JSTARS.2019.2919936 ]
Wu H and Li W . 2019 . Downscaling land surface temperatures using a random forest regression model with multitype predictor variables . IEEE Access , 2019 : 1 - 1 [ DOI: 10.1109/ACCESS.2019.2896241 http://dx.doi.org/10.1109/ACCESS.2019.2896241 ]
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.20211306 http://dx.doi.org/10.11834/jrs.20211306 ]
Yang C , Zhan Q , Lv Y and Liu H . 2019 . Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP ( 99 ): 1 - 10 [ DOI: 10.1109/JSTARS.2019.2955551 http://dx.doi.org/10.1109/JSTARS.2019.2955551 ]
Yang Y B , Li X L and Cao C . 2017 . Downscaling urban land surface temperature based on multi-scale factor . Science of Surveying and Mapping , 42 ( 010 ): 73 - 79
杨英宝 , 李小龙 , 曹晨 . 2017 . 多尺度城市地表温度降尺度方法 . 测绘科学 , 42 ( 010 ): 73- 79 [ DOI: 10.16251/j.cnki.1009-2307.2017.10.012 http://dx.doi.org/10.16251/j.cnki.1009-2307.2017.10.012 ]
Zakšek K and Ostir K . 2012 . Downscaling land surface temperature for urban heat island diurnal cycle analysis . Remote Sensing of Environment , 117 : 114 - 124 [ DOI: 10.1016/j.rse.2011.05.027 http://dx.doi.org/10.1016/j.rse.2011.05.027 ]
Zhan W F , Chen Y H , Wang J F , Zhou J , Quan J L , Liu W Y and Li J . 2012 . Downscaling land surface temperatures with multi-spectral and multi-resolution images . International Journal of Applied Earth Observation and Geoinformation , 18 : 23 - 36 [ DOI: 10.1016 /j.jag. 2012.01.003 http://dx.doi.org/10.1016/j.jag.2012.01.003 ]
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.2012.12.014 http://dx.doi.org/10.1016/j.rse.2012.12.014 ]
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