毛克彪团队“基于热红外遥感反演和气象数据融合的地表温度产品重建技术”获得中国国际大数据产业博览会领先科技成果奖
5月26日，2022中国国际大数据产业博览会领先科技成果奖正式揭晓。由中国农业科学院农业资源与农业区划研究所牵头，宁夏大学、国家卫星气象中心、中国科学院国家空间科学中心、国家气象中和山东建筑大学共同完成的“基于热红外遥感反演和气象数据融合的地表温度产品重建技术”项目获2022数博会领先科技成果奖“优秀项目”奖项（主要完成人：毛克彪，施建成，杨军，王旭明，毛留喜，郭中华，孟飞，曹萌萌，王涵，袁紫晋等）。
中国国际大数据产业博览会由国家发展和改革委员会、工业和信息化部、国家互联网信息办公室和贵州省政府共同主办。该奖项在国家科学技术奖励工作办公室备案，是目前国内唯一以大数据为主题的社会科技奖励，旨在展现工业、农业、旅游业等领域大数据相关领先科技成果。
地表温度是气象预报和农业灾害监测的关键参数，借助遥感技术快速准确获取大面积和长时间序列的地表温度信息是顺应当前科学技术发展的趋势，从而满足全球多种自然灾害和农情监测等的迫切需求，进一步保障全球和我国粮食安全。本研究通过利用深度学习耦合物理方法和统计方法，建立“物理模型+统计方法+专家知识”驱动的深度学习地表温度反演理论框架，从理论上解决了地表温度和发射率分离难题。通过利用辐射传输模型模拟及高精度统计数据解决了深度学习所需要的训练和测试数据，从技术上高精度地解决了地表温度和发射率反演难题，在观测角小于10度和65度时，理论精度分别在0.1 K和0.5 K以下。本技术为热红外传感器地表温度反演提供了通用算法模式，同时也为热红外传感器波段设计提供了方案。在此基础上，我们进一步将反演结果与地面气象站点数据融合，从而解决有云情况下地表温度数据缺失的问题，生产时空连续的地表温度数据集。该技术框架将成为全球地表温度和其他参数遥感反演及重建的主要通用范式，特别是成为我国风云和高分等卫星地表温度反演和数据产品重建的主要标准算法。深度动态学习神经网络与辐射传输模型及高精度统计数据相结合，即深度学习与物理方法和统计方法耦合反演地表温度和发射率在遥感地表温度和发射率反演史上具有里程牌意义。
（图1. “物理模型+统计方法”和深度学习耦合的地表温度反演技术路线）
（图2. 每天四个时段全球陆地表面温度时空变化特征）
（图3. 中国地表温度融合数据时空变化规律分析）
团队部分论文介绍
4套数据集（气温数据集，陆面温度数据集，海温数据集和土壤水分数据集）
1. Fang, S., Mao, K., Xia, X., Wang, P., Shi, J., Bateni, S. M., Xu, T., Cao, M., Heggy, E., Qin, Z., Dataset of daily near-surface air temperature in China from 1979 to 2018, Earth Syst. Sci. Data. 14, 1413–1432, https://doi.org/10.5194/essd-14-1413-2022, 2022. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5502275. 【中国气温数据集】
2. Zhao, B., Mao, K., Cai, Y., Shi, J., Li, Z., Qin, Z., Meng, X., A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 – 2017, Earth Syst. Sci. Data, 2020, 12, 2555–2577. https://doi.org/10.5194/essd-12-2555-2020 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3528024. 【中国地表温度数据集】
3. Cao, M., Mao, K., Yan, Y., Shi, J., Wang, H., Xu, T., Fang, S., Yuan, Z., A new global gridded sea surface temperature data product based on multisource data, Earth Syst. Sci. Data, 2021 13, 2111–2134, https://doi.org/10.5194/essd-13-2111-2021, 2021. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4419804. 【全球海面温度数据集】
4. Meng, X., Mao, K., Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Jiang, L., Guo, Z., A fine-resolution soil moisture dataset for China in 2002–2018, Earth Syst. Sci. Data, 2021, 13, 3239–3261. https://doi.org/10.5194/essd-13-3239-2021. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4738556. 【中国土壤水分数据集】
部分地表温度和土壤水分反演相关论文：
1. Wang, H, Mao, K., Yuan, Z., Shi, J, Cao, M., Qin, Z., Duan, S., Tang, B., A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning, Remote Sensing of Environment, 2021, 265, 1-19. https://doi.org/10.1016/j.rse.2021.112665
2. Mao, K., Shi, J., Li, Z., Tang, H., An RM-NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data, Journal of Geophysical Research-atmosphere, 2007, 112, D21102, 1-17. https://doi.org/10.1029/2007JD008428
3. Wang, H., Mao, K., Mu, F., Shi, J., Yang, J., Li, Z., Qin, Z., A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 data, Remote Sensing, 2019, 11, 20183, 1-25.https://doi.org/10.3390/rs11182083
4. Mao, K., Qin Z., Shi J., Gong P., A Practical Split-Window Algorithm for Retrieving Land Surface Temperature from MODIS Data, International Journal of Remote Sensing, 2005,26:3181-3204.https://doi.org/10.1080/01431160500044713
5. Mao, K., Shi, J., Tang, H., Li, Z., Wang, X., Chen, K., A Neural Network Technique for Separating Land Surface Emissivity and Temperature from ASTER Imagery, IEEE Trans. Geosci. Remote Sensing, 2008, 46(1), 200-208.10.1109/TGRS.2007.907333
6. Mao, K., Li, S., Wang, D., Zhang, L., Tang, H., Wang, X., Li, Z., Retrieval of Land Surface Temperature and Emissivity from ASTER1B data Using Dynamic Learning Neural Network, international journal of remote sensing, 2011, 32(19), 5413-5423.https://doi.org/10.1080/01431161.2010.501043
7.Mao, K., Ma, Y., Shen, X., et al., Estimation of Broadband Emissivity (8-12um) from ASTER Data by Using RM-NN, Optics Express, 2012, 20(18), 20096-20101.https://doi.org/10.1364/OE.20.020096
8. Xia, L., Mao, K., Ma, Y., Zhao, F., Jiang, L.P., Shen, X.Y., Qin, Z.H., An algorithm for retrieving land surface temperature using VIIRS data in combination with multi-sensors, Sensors, 2014, 14, 21385-21408.https://doi.org/10.3390/s141121385
9.Mao, K., Shi, J., Li, Z., Qin, Z., Li, M., Xu, B., A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data, Science in China (Series D), 2007, 7, 1115-1120.doi: 10.1007/s11430-007-2053-x
10.Tan, J., Nusseiba, N., Mao, K., Shi, J., Li, Z., Xu, T., Yuan, Z., Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China, Sensors, 2019, 19, 2987:1-20. https://doi.org/10.3390/s19132987 .
11.Mao, K., Zuo, Z., Shen, X., Xu, T., Gao, C., Liu, G., Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network, Chinese Geographical Science. 2018, 28,1, 1–11.doi: 10.1007/s11769-018-0930-1
12.Mao, K., Tang, H., Wang, X., Zhou, Q., Wang, D., Near-Surface Air Temperature Estimation from ASTER Data Using Neural Network, International Journal of Remote Sensing,2008, 29(20), 6021-6028.https://doi.org/10.1080/01431160802192160
13. Yan, Y., Mao, K., Shen, X., Cao, M., Xu, T., Guo, Z., Bao, Q., Evaluation of the influence of ENSO on tropical vegetation in long time series using a new indicator, Ecological Indicators, 2021,129,1-22. https://doi.org/10.1016/j.ecolind.2021.107872 .
14. Yan, Y., Mao, K., Shi, J., Piao, S. L., Shen, X.Y., Dozier, J., Liu, Y., Ren, H.L, Bao, Q., Driving forces of land surface temperature anomalous changes in North America in 2002–2018, Scientific Reports, 2020, 6931(10), 1-13. https://doi.org/10.1038/s41598-020-63701-5 .
15. Han, J., Mao, K.,Xu, T.,Guo, J., Zuo, Z., Gao, C., A soil moisture estimation framework based on the CART algorithm and its application in China, Journal of Hydrology, 2018, 561, 65-75.https://doi.org/10.1016/j.jhydrol.2018.05.051
16.Xia, L., Zhao, F., Chen, L., Zhang, R., Mao, K., Kylling, A., Ma, Y., Performance comparison of the MODIS and the VIIRS 1.38 μm cirrus cloud channels using libRadtran and CALIOP data, Remote Sensing of Environment,2018,206,363–374. https://doi.org/10.1016/j.rse.2017.12.040
17.Lang Xia, Fen Zhao, Kebiao Mao*, Zijin Yuan, Zhiyuan Zuo,Tongren Xu, SPI-Based Analyses of Drought Changes over the Past 60 Years in China’s Major Crop-Growing Areas, Remote Sens. 2018, 171(10),1-15.https://doi.org/10.3390/rs10020171
18.Mao, K., Tang H. J., Zhang L. X., Li M. C., Guo Y., Zhao D. Z., A Method for Retrieving Soil Moisture in Tibet Region By Utilizing Microwave Index from TRMM/TMI Data, International Journal of Remote Sensing, 2008, 29(10), 2905-2925.https://doi.org/10.1080/01431160701442104
19.Kebiao Mao, H. T. Li, D. Y. Hu, J. Wang, J. X. Huang, Z. L. Li, Q. B. Zhou, and H. J. Tang, Estimation of water vapor content in near-infrared bands around 1 μm from MODIS data by using RM–NN, Optics Express, 2010, 18(9), 9542–9554.
部分全球变化论文：
1. Guo, J., Mao, K., Yuan, Z., Qin, Z., Xu, T., Bateni, S.M., Zhao, Y., Ye, C., Global Food Security Assessment during 1961–2019.Sustainability 2021, 132, 1-18. https://doi.org/10.3390/su132414005
2. Mao, K., Ma, Y., Tan, X., Shen, X., Liu, G., Li, Z., Chen, J., Xia, L., Global surface temperature change analysis based on MODIS data in recent twelve years, Advance Space Research, 2017,59,503-512.https://doi.org/10.1016/j.asr.2016.11.007 .
3. Mao, K., Ma, Y., Xia, L., Chen, W.Y., Shen, X. Y., He, T.J., Global aerosol change in the last decade: An analysis based on MODIS data, Atmospheric Environment, 2014, 94, 680-686.https://doi.org/10.1016/j.atmosenv.2014.04.053
4. Mao, K., Li, Z., Chen, J., Ma, Y., Liu, G., Tan, X., Yang, K., Global vegetation change analysis based on MODIS data in recent twelve years, High Technology Letters, 2016, 22(4), 343-349. Doi:10.3773/j.issn.1006-6748.2016.04.001
5. Mao, K., Chen, J., Li, Z., Ma, Y., Song, Y., Tan, X., Yang, K., Global water vapor content decreases from 2003 to 2012: an analysis based on MODIS Data, Chinese Geographical Science, 2017, 27(1), 1-7.https://doi.org/10.1007/s11769-017-0841-6
6. Mao, K., Yuan, Z., Zuo, Z., Xu, T., Shen, X., Gao, C., Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012, Chinese Geographical Science, 2019,29,2, 306–315.https://doi.org/10.1007/s11769-019-1030-6
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