AMSR-E地表温度数据重建深度学习方法

苏扬; 吴鹏海; 程洁; 殷志祥; 马晓双; 杨辉

doi:10.11834/jrs.20220426

智能遥感处理与分析 | 浏览量 : 0 下载量: 1292 CSCD: 5

R-PDF
PDF
导出
分享
收藏
专辑

AMSR-E地表温度数据重建深度学习方法
Research on deep learning methods for AMSR-E land surface temperature data reconstruction
2022年26卷第4期页码：739-751
收稿：2020-09-27，

纸质出版：2022-04-07
DOI： 10.11834/jrs.20220426
稿件说明：

移动端阅览

苏扬，吴鹏海，程洁，殷志祥，马晓双，杨辉.2022.AMSR-E地表温度数据重建深度学习方法.遥感学报，26（4）： 739-751 DOI： 10.11834/jrs.20220426.

Su Y，Wu P H，Cheng J，Yin Z X，Ma X S and Yang H. 2022. Research on deep learning methods for AMSR-E land surface temperature data reconstruction. National Remote Sensing Bulletin， 26（4）：739-751 DOI： 10.11834/jrs.20220426.

摘要

地表温度对于全球气候变化等研究具有重要意义。被动微波遥感传感器AMSR-E（Advanced Microwave Scanning Radiometer for EOS）可以获得全天候地表温度，可作为多云条件下热红外地表温度数据的补充；但轨道扫描间隙限制了该数据在全球或区域尺度上的实际应用。鉴于地表温度的高时空异质性和AMSR-E LST轨道间隙数据的特点，本文提出了一种多时相特征连接卷积神经网络地表温度双向重建模型（MTFC-CNN），利用深度学习在处理复杂非线性问题上的优势，重建轨道间隙区域的地表温度值。将2010年中国大陆四季的AMSR-E LST数据（数据未含港澳台区域），分为白天和夜晚，形成共8个数据子集进行实验。在模拟实验中，重建结果与原始反演地表温度值平均均方根误差在1.0 K左右，决定系数

在0.88以上，优于传统的样条空间插值和时间线性回归方法；真实实验结果具有较好的目视效果，且与对应MODIS LST产品对比发现，重建区LST值和未重建区LST值与MODIS LST产品间具有相近的平均均方根误差和决定系数。因此，本文提出的MTFC-CNN方法能有效重建AMSR-E LST轨道间隙数据，且优于传统方法。

Abstract

Land Surface Temperature (LST) is a key parameter in the physical process of surface radiant energy balance and the water cycle. Obtaining LST data accurately and promptly

and mastering its temporal and spatial changes

are of considerable importance to climate change research. Thermal infrared (TIR) measurements are limited in practical applications due to cloud cover and other effects. Passive microwave (PMW) remote sensing measurements can penetrate clouds and are less affected by atmospheric interference

which has the advantage of obtaining all-weather surface radiation information. The microwave remote sensing data Advanced Microwave Scanning Radiometer for EOS (AMSR-E) can obtain all-weather LST

which can be used as a supplement to the missing LST information in thermal infrared (TIR) products under cloudy conditions. However

the AMSR-E data has the problem of lack of information due to the satellite orbit scanning gap of its own sensor

causing the obtained AMSR-E LST data to be greatly restricted in practical applications. Therefore

proposing an effective method for solving the problem is necessary.

Based on the superiority of deep learning in solving non-linear problems and the high dynamic variability of LST

this paper proposes a Multi-Temporal Feature-Connected Convolutional Neural Network (MTFC-CNN) that uses specific input combinations of multi-temporal information and spatial fusion units. The network structure is based on the characteristics of the temporal and spatial distribution of missing track gaps in AMSR-E LST data

and the reconstruction of missing LST values is carried out from the timing information.

In the simulation experiment

the 2010 annual data was divided into fight data subsets in four seasons and into day and night. The average root mean square error of the reconstructed LST value is approximately 1.0 K and the coefficient of determination

is above 0.88. Compared with the other two methods

namely

spline interpolation (Spline) and time multiple linear regression (Regress)

the reconstruction effect of the MTFC-CNN method performs better regardless of seasons or day and night

proving that MTFC-CNN is better than the other two methods at mining the characteristics of temporal and spatial changes in LST. In real experiments

through comparison with MODIS LST products

the LST value reconstructed at the missing area is consistent with it at other areas in temporal and spatial distribution. The reconstruction results show that the LST in the mainland China region shows a gradually increasing trend from January to July and a gradually decreasing trend from August to December. This finding is consistent with the temperature changes in the four seasons. The change in LST during the day is more significant than that at night. In summer

the temperature in Northwest China is significantly higher than that in other regions. In winter

the temperature in Northeast China is generally lower than that in other regions. At night

the difference between summer and winter is more obvious. The difference in LST changes at night in autumn is relatively close.

The experimental results show that the MTFC-CNN method proposed in this paper mines the spatio-temporal variation information of LSTs more effectively than two traditional methods and achieves better results in reconstructing the orbital gap missing in AMSR-E LST data. The method provides the possibility for the reconstruction of missing information from TIR LST data under the cloud.

关键词

Keywords

references

Chollet F . 2017 . Xception: deep learning with depthwise separable convolutions // IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Honolulu : IEEE: 1800 - 1807 [ DOI: 10.1109/CVPR.2017.195 http://dx.doi.org/10.1109/CVPR.2017.195 ]

Duan S B , Li Z L and Leng P . 2017 . A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data . Remote Sensing of Environment , 195 ( 15 ): 107 - 117 [ DOI: 10.1016/j.rse.2017.04.008 http://dx.doi.org/10.1016/j.rse.2017.04.008 ]

Holmes T R H , Crow W T , Yilmaz M T , Jackson T J and Basara J B . 2013 . Enhancing model-based land surface temperature estimates using multiplatform microwave observations . Journal of Geophysical Research : Atmospheres , 118 ( 2 ): 577 - 591 [ DOI: 10.1002/jgrd.50113 http://dx.doi.org/10.1002/jgrd.50113 ]

Li X M , Zhou Y Y , Asrar G R and Zhu Z Y . 2018 . Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States . Remote Sensing of Environment , 206 : 84 - 97 [ DOI: 10.1016/j.rse.2017.12.010 http://dx.doi.org/10.1016/j.rse.2017.12.010 ]

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 ]

Li Z L , Wu H , Wang N , Qiu S , Sobrino J A , Wan Z M , Tang B H and Yan G J . 2013 . Land surface emissivity retrieval from satellite data . International Journal of Remote Sensing , 34 ( 9-10 ): 3084 - 3127 [ DOI: 10.1080/01431161.2012.716540 http://dx.doi.org/10.1080/01431161.2012.716540 ]

Liu H Z , Lu N , Jiang H , Qin J and Yao L . 2020 . Filling gaps of monthly terra/MODIS daytime land surface temperature using discrete cosine transform method . Remote Sensing , 12 ( 3 ): 361 [ DOI: 10.3390/rs12030361 http://dx.doi.org/10.3390/rs12030361 ]

Liu X M and Wang M H . 2016 . Analysis of ocean diurnal variations from the Korean Geostationary Ocean Color Imager measurements using the DINEOF method . Estuarine, Coastal and Shelf Science , 180 : 230 - 241 [ DOI: 10.1016/j.ecss.2016.07.006 http://dx.doi.org/10.1016/j.ecss.2016.07.006 ]

Liu Z H , Wu P H , Wu Y L , Shen H F and Zeng C . 2017 . Robust reconstruction of missing data in Feng Yun geostationary satellite land surface temperature products . Journal of Remote Sensing , 21 ( 1 ): 40 - 51

刘紫涵 , 吴鹏海 , 吴艳兰 , 沈焕锋 , 曾超 . 2017 . 风云静止卫星地表温度产品空值数据稳健修复 . 遥感学报 , 21 ( 1 ): 40 - 51 [ DOI: 10.11834/jrs.20176003 http://dx.doi.org/10.11834/jrs.20176003 ]

Long D , Yan L , Bai L L , Zhang C J , Li X Y , Lei H M , Yang H B , Tian F Q , Zeng C , Meng X Y and Shi C X . 2020 . Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach . Remote Sensing of Environment , 246 : 111863 [ DOI: 10.1016/j.rse.2020.111863 http://dx.doi.org/10.1016/j.rse.2020.111863 ]

Pede T , Mountrakis G . 2018 . An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States . ISPRS Journal of Photogrammetry and Remote Sensing , 142 : 137 - 150 [ DOI: 10.1016/j.isprsjprs.2018.06.003 http://dx.doi.org/10.1016/j.isprsjprs.2018.06.003 ]

Ronneberger O , Fischer P and Brox T . 2015 . U-Net: convolutional networks for biomedical image segmentation // 18th International Conference on Medical Image Computing and Computer-Assisted Intervention . Munich : Springer: 234 - 241 [ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]

Shen H F , Li X H , Cheng Q , Zeng C , Yang G , Li H F and Zhang L P . 2015 . Missing information reconstruction of remote sensing data: a technical review . IEEE Geoscience and Remote Sensing Magazine , 3 ( 3 ): 61 - 85 [ DOI: 10.1109/MGRS.2015.2441912 http://dx.doi.org/10.1109/MGRS.2015.2441912 ]

Shuai T , Zhang X , Wang S D , Zhang L F , Shang K , Chen X P and Wang J N . 2014 . A spectral angle distance-weighting reconstruction method for filled pixels of the MODIS land surface temperature product . IEEE Geoscience and Remote Sensing Letters , 11 ( 9 ): 1514 - 1518 [ DOI: 10.1109/LGRS.2013.2297735 http://dx.doi.org/10.1109/LGRS.2013.2297735 ]

Shwetha H R and Kumar D N . 2016 . Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN . ISPRS Journal of Photogrammetry and Remote Sensing , 117 : 40 - 55 [ DOI: 10.1016/j.isprsjprs.2016.03.011 http://dx.doi.org/10.1016/j.isprsjprs.2016.03.011 ]

Tang W B , Xue D J , Long Z Y , Zhang X D and Zhou J . 2021 . Near-real-time estimation of 1-km all-weather land surface temperature by integrating satellite passive microwave and thermal infrared observations . IEEE Geoscience and Remote Sensing Letters , 1 - 5 [ DOI: 10.1109/LGRS.2021.3067908 http://dx.doi.org/10.1109/LGRS.2021.3067908 ]

Weiss D J , Atkinson P M , Bhatt S , Mappin B , Hay S I and Gething P W . 2014 . An effective approach for gap-filling continental scale remotely sensed time-series . ISPRS Journal of Photogrammetry and Remote Sensing , 98 : 106 - 118 [ DOI: 10.1016/j.isprsjprs.2014.10.001 http://dx.doi.org/10.1016/j.isprsjprs.2014.10.001 ]

Wu P H , Yin Z X , Yang H , Wu Y L and Ma X S . 2019 . Reconstructing geostationary satellite land surface temperature imagery based on a multiscale feature connected convolutional neural network . Remote Sensing , 11 ( 3 ): 300 [ DOI: 10.3390/rs11030300 http://dx.doi.org/10.3390/rs11030300 ]

Wu P H , Yin Z X , Zeng C , Duan S B , Göttsche F M , Ma X S , Li X H , Yang H and Shen H F . 2021 . Spatially continuous and high-resolution land surface temperature product generation: a review of reconstruction and spatiotemporal fusion techniques . IEEE Geoscience and Remote Sensing Magazine , 9 ( 3 ): 112 - 137 [ DOI: 10.1109/MGRS.2021.3050782 http://dx.doi.org/10.1109/MGRS.2021.3050782 ]

Xu S and Cheng J . 2021 . A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering . Remote Sensing of Environment , 254 : 112256 [ DOI: 10.1016/j.rse.2020.112256 http://dx.doi.org/10.1016/j.rse.2020.112256 ]

Xu S , Cheng J and Zhang Q . 2019 . Reconstructing all-weather land surface temperature using the bayesian maximum entropy method over the tibetan plateau and heihe river basin . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 12 ( 9 ): 3307 - 3316 [ DOI: 10.1109/JSTARS.2019.2921924 http://dx.doi.org/10.1109/JSTARS.2019.2921924 ]

Xu Y M and Shen Y . 2013 . Reconstruction of the land surface temperature time series using harmonic analysis . Computers and Geosciences , 61 : 126 - 132 [ DOI: 10.1016/j.cageo.2013.08.009 http://dx.doi.org/10.1016/j.cageo.2013.08.009 ]

Zeng C , Li 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.2018.04.005 http://dx.doi.org/10.1016/j.isprsjprs.2018.04.005 ]

Zeng C , Shen H F , Zhong M L , Zhang L P and Wu P H . 2015 . Reconstructing MODIS LST based on multitemporal classification and robust regression . IEEE Geoscience and Remote Sensing Letters , 12 ( 3 ): 512 - 516 [ DOI: 10.1109/LGRS.2014.2348651 http://dx.doi.org/10.1109/LGRS.2014.2348651 ]

Zhang L P , Zhang L F and Du B . 2016 . Deep learning for remote sensing data: a technical tutorial on the state of the art . IEEE Geoscience and Remote Sensing Magazines , 4 ( 2 ): 22 - 40 [ DOI: 10.1109/MGRS.2016.2540798 http://dx.doi.org/10.1109/MGRS.2016.2540798 ]

Zhang Q and Cheng J . 2020 . An empirical algorithm for retrieving land surface temperature from AMSR-E data considering the comprehensive effects of environmental variables . Earth and Space Science , 7 ( 4 ): e2019 EA 001006 [ DOI: 10.1029/2019ea001006 http://dx.doi.org/10.1029/2019ea001006 ]

Zhang Q , Yuan Q Q , Zeng C , Li X H and Wei Y C . 2018 . Missing data reconstruction in remote sensing image with a unified spatial-temporal-spectral deep convolutional neural network . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 8 ): 4274 - 4288 [ DOI: 10.1109/TGRS.2018.2810208 http://dx.doi.org/10.1109/TGRS.2018.2810208 ]

Zhang X D , Zhou J , Liang S L , Chai L N , Wang D D and Liu J . 2020 . Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data . ISPRS Journal of Photogrammetry and Remote Sensing , 167 : 321 - 344 [ DOI: 10.1016/j.isprsjprs.2020.07.014 http://dx.doi.org/10.1016/j.isprsjprs.2020.07.014 ]

Zhang X D , Zhou J , Liang S L and Wang D D . 2021 . A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature . Remote Sensing of Environment , 260 : 112437 [ DOI: 10.1016/j.rse.2021.112437 http://dx.doi.org/10.1016/j.rse.2021.112437 ]

Zhao W and Duan S B . 2020 . Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data . Remote Sensing of Environment , 247 : 111931 [ DOI: 10.1016/j.rse.2020.111931 http://dx.doi.org/10.1016/j.rse.2020.111931 ]

Zhou J , Zhang X D , Zhan W F , Göttsche F M , Liu S M , Olesen F S , Hu W X and Dai F N . 2017 . A thermal sampling depth correction method for land surface temperature estimation from satellite passive microwave observation over barren land . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 8 ): 4743 - 4756 [ DOI: 10.1109/TGRS.2017.2698828 http://dx.doi.org/10.1109/TGRS.2017.2698828 ]

文章被引用时，请邮件提醒。

提交

AMSR-E地表温度数据重建深度学习方法研究

Landsat 8 地表温度产品降尺度深度学习方法研究

基于深度特征重构增强的光学和SAR图像鲁棒匹配

1985年—2023年遥感地表温度研究进展与展望

遥感农作物早期分类研究现状与展望