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Smart Agriculture ›› 2023, Vol. 5 ›› Issue (2): 161-171.doi: 10.12133/j.smartag.SA202304013

• 综合研究 • 上一篇    

基于人工智能的地球物理参数反演范式理论及判定条件

毛克彪1,2,3(), 张晨阳4, 施建成5, 王旭明2, 郭中华2, 李春树2, 董立新6, 吴门新7, 孙瑞静6, 武胜利6, 姬大彬3, 蒋玲梅8, 赵天杰3, 邱玉宝3, 杜永明3, 徐同仁8   

  1. 1. 中国农业科学院农业资源与农业区划研究所 北方干旱半干旱耕地高效利用全国重点实验室,北京 100081
    2. 宁夏大学 物理与电子电气工程学院,宁夏 银川 750021
    3. 中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100094
    4. 北京大学 环境科学与工程学院,北京 100871
    5. 中国科学院国家空间科学中心,北京 100190
    6. 国家卫星气象中心,北京 100081
    7. 国家气象中心,北京 100081
    8. 北京师范大学 地理科学部,北京 100875
  • 收稿日期:2023-04-24 出版日期:2023-06-30
  • 基金资助:
    风云卫星应用先行计划(FY-APP-2022.0205); 第二次青藏高原综合科学考察研究(2019QZKK0206XX-02); 遥感科学国家重点实验室开放基金(OFSLRSS202201)
  • 通信作者:
    毛克彪,博士,研究员,研究方向为人工智能在地学和农学中的应用。E-mail:

The Paradigm Theory and Judgment Conditions of Geophysical Parameter Retrieval Based on Artificial Intelligence

MAO Kebiao1,2,3(), ZHANG Chenyang4, SHI Jiancheng5, WANG Xuming2, GUO Zhonghua2, LI Chunshu2, DONG Lixin6, WU Menxin7, SUN Ruijing6, WU Shengli6, JI Dabin3, JIANG Lingmei8, ZHAO Tianjie3, QIU Yubao3, DU Yongming3, XU Tongren8   

  1. 1. State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China
    3. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100094, China
    4. College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
    5. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
    6. National Satellite Meteorological Center, Beijing 100081, China
    7. National Meteorological Center, Beijing 100101, China
    8. Department of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2023-04-24 Online:2023-06-30
  • Supported by:
    Fengyun Satellite Application Pilot Plan (FY-APP-2022.0205); Research on the Second Comprehensive Scientific Expedition to the Qinghai Tibet Plateau (2019QZKK0206XX-02); Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS202201)

摘要:

[目的/意义] 人工智能(Artificial Intelligence,AI)技术已在学术和工程应用领域掀起了研究高潮,在地球物理参数和农业气象遥感参数反演方面也表现出了强大的应用潜力。目前大部分AI技术在地学和农学的应用还是“黑箱”,没有物理意义或缺乏可解释性及通用性。为了促进AI在地学和农学的应用和培养交叉学科的人才,本研究提出基于AI耦合物理和统计方法的地球物理参数反演范式理论。 [方法] 首先基于物理能量平衡方程进行物理逻辑推理,从理论上构造反演方程组,然后基于物理推导构建泛化的统计方法。通过物理模型模拟获得物理方法的代表性解以及利用多源数据获得统计方法代表性的解作为深度学习的训练和测试数据库,最后利用深度学习进行优化求解。 [结果和讨论] 判定形成具有通用性和物理可解释的范式条件包括:(1)输入与输出变量(参数)之间必须存在因果关系;(2)输入和输出变量(参数)之间理论上可以构建闭合的方程组(未知数个数少于或等于方程组个数),也就是说输出参数可以被输入参数唯一确定。如果输入参数(变量)和输出参数(变量)之间存在很强的因果关系,则可以直接使用深度学习进行反演。如果输入参数和输出参数之间存在弱相关性,则需要添加先验知识来提高输出参数的反演精度。此外,本研究以农业气象遥感中的关键参数地表温度、发射率、近地表空气温度和大气水汽含量联合反演作为案例对理论进行了证明,分析结果表明本理论是可行的,并且可以辅助优化设计卫星传感器波段组合。 [结论] 本理论和判定条件的提出在地球物理参数反演史上具有里程碑意义。

关键词: 人工智能, 深度学习, 反演范式, 物理逻辑推导, 可解释, 农业气象遥感

Abstract:

Objective Deep learning is one of the most important technologies in the field of artificial intelligence, which has sparked a research boom in academic and engineering applications. It also shows strong application potential in remote sensing retrieval of geophysical parameters. The cross-disciplinary research is just beginning, and most deep learning applications in geosciences are still "black boxes", with most applications lacking physical significance, interpretability, and universality. In order to promote the application of artificial intelligence in geosciences and agriculture and cultivate interdisciplinary talents, a paradigm theory for geophysical parameter retrieval based on artificial intelligence coupled physics and statistical methods was proposed in this research. Methods The construction of the retrieval paradigm theory for geophysical parameters mainly included three parts: Firstly, physical logic deduction was performed based on the physical energy balance equation, and the inversion equation system was constructed theoretically which eliminated the ill conditioned problem of insufficient equations. Then, a fuzzy statistical method was constructed based on physical deduction. Representative solutions of physical methods were obtained through physical model simulation, and other representative solutions as the training and testing database for deep learning were obtained using multi-source data. Finally, deep learning achieved the goal of coupling physical and statistical methods through the use of representative solutions from physical and statistical methods as training and testing databases. Deep learning training and testing were aimed at obtaining curves of solutions from physical and statistical methods, thereby making deep learning physically meaningful and interpretable. Results and Discussions The conditions for determining the formation of a universal and physically interpretable paradigm were: (1) There must be a causal relationship between input and output variables (parameters); (2) In theory, a closed system of equations (with unknowns less than or equal to the number of equations) can be constructed between input and output variables (parameters), which means that the output parameters can be uniquely determined by the input parameters. If there is a strong causal relationship between input parameters (variables) and output parameters (variables), deep learning can be directly used for inversion. If there is a weak correlation between the input and output parameters, prior knowledge needs to be added to improve the inversion accuracy of the output parameters. The MODIS thermal infrared remote sensing data were used to retrieve land surface temperature, emissivity, near surface air temperature and atmospheric water vapor content as a case to prove the theory. When there was strong correlation between output parameters (LST and LSE) and input variables (BTi), using deep learning coupled with physical and statistical methods could obtain very high accuracy. When there was a weak correlation between the output parameter (NSAT) and the input variable (BTi), adding prior knowledge (LST and LSE) could improve the inversion accuracy and stability of the output parameter (NSAT). When there was partial strong correlation (WVC and BTi), adding prior knowledge (LST and LSE) could slightly improve accuracy and stability, but the error of prior knowledge (LST and LSE) may bring uncertainty, so prior knowledge could also be omitted. According to the inversion analysis of geophysical parameters of MODIS sensor thermal infrared band, bands 27, 28, 29 and 31 were more suitable for inversion of atmospheric water vapor content, and bands 28, 29, 31 and 32 were more suitable for inversion of surface temperature, Emissivity and near surface air temperature. If someone want to achieve the highest accuracy of four parameters, it was recommended to design the instrument with five bands (27, 28, 29, 31, 32) which were most suitable. If only four thermal infrared bands were designed, bands 27, 28, 31, and 32 should be given priority consideration. From the results of land surface temperature, emissivity, near surface air temperature and atmospheric water vapor content retrieved from MODIS data using this theory, it was not only more accurate than traditional methods, but also could reduce some bands, reduce satellite load and improve satellite life. Especially, this theoretical method overcomes the influence of the MODIS official algorithm (day/night algorithm) on sudden changes in surface types and long-term lack of continuous data, which leads to unstable accuracy of the inversion product. The analysis results showed that the proposed theory and conditions are feasible, and the accuracy and applicability were better than traditional methods. The theory and judgment conditions of geophysical parameter retrieval paradigms were also applicable for target recognition such as remote sensing classification, but it needed to be interpreted from a different perspective. For example, the feature information extracted by different convolutional kernels must be able to uniquely determine the target. Under satisfying with the conditions of paradigm theory, the inversion of geophysical parameters based on artificial intelligence is the best choice. Conclusions The geophysical parameter retrieval paradigm theory based on artificial intelligence proposed in this study can overcome the shortcomings of traditional retrieval methods, especially remote sensing parameter retrieval, which simplify the inversion process and improve the inversion accuracy. At the same time, it can optimize the design of satellite sensors. The proposal of this theory is of milestone significance in the history of geophysical parameter retrieval.

Key words: artificial intelligence, deep learning, retrieval paradigm, physical logic derivation, explainable, agrometeorological remote sensing

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