سابِق|تال

Research progress and challenges of data-driven quantitative remote sensing

  • role： First author第一作者
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079

  • Email：qianqian_yang@whu.edu.cn
  • Introduction：杨倩倩，1995年生，女，博士研究生，研究方向为大气遥感。E-mail： qianqian_yang@whu.edu.cn
YANG Qianqian1,  
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079

JIN Caiyi1,  
  • Affiliation：

    School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082

LI Tongwen2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079

  • Email：qqyuan@sgg.whu.edu.cn
  • Introduction：袁强强，1985年生，男，教授，研究方向为遥感信息处理，定量遥感，机器学习。E-mail： qqyuan@sgg.whu.edu.cn
YUAN Qiangqiang1*,  
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079

SHEN Huanfeng3,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079

ZHANG Liangpei4

ملخص

Quantitative remote sensing is a technique for quantitatively inferring or inverting earth environmental variable from the original remote sensing observations, it is an important step to turn electromagnetic wave signals into easy-to-understand information about surface environment. Traditional quantitative remote sensing methods are mainly model-driven, emphasizing inversion through mathematical or physical models. With the development and popularization of artificial intelligence technology, data-driven methods have gradually received widespread attention. It emphasizes the use of machine learning methods to mine the information contained in remote sensing observation data to achieve the quantitative inversion of geophysical parameters. With the support of powerful computing capacity, the data-driven method has achieved gratifying achievements in many fields of quantitative remote sensing. This article systematically summarizes the principles, characteristics, and applications in quantitative remote sensing field of different types of data-driven models, including regression algorithms, regularization methods, instance-based algorithms, decision tree, Bayesian methods, kernel based algorithms, genetic algorithms, ensemble learning, artificial neural network, and deep learning. Though data-driven models show satisfying retrieval performance in multiple fields, its drawbacks of ignoring the laws of physics and lack of causality have also brought resistance to its development. In this context, coupling the laws of physics and machine learning to develop an inversion framework driven by both models and data has become a new research hotspot. Some pioneering researches have already achieved delightful performance through using machine learning to assist physical models or restricting machine learning with physical laws. Using machine learning techniques to optimize the systematic basis, the sub-model, and the model parameters largely improve the performance of model-driven methods. Meanwhile, integrating physics knowledge into machine learning models through adjusting the training data, modifying the loss function, and constraining the solution space also benefit the improvement of data-driven models. However, there are still great challenges to be broken through. Physical models contain complex mechanisms and rich knowledge, current fusion of data-driven and model-driven methods are quite shallow with very limited amount of physical knowledge being used. A deeper coupling strategy is worth exploring in the future. Besides, the uncertainty, generalization, and transferability of the joint model have not been scientifically evaluated currently, to which attention should be paid. Finally, there are many cases when the training samples were very difficult to obtain, therefore, the applicability of the joint model in the case of small samples is also a problem that needs to be solved urgently. The deep, robust, and generalizable coupling of data-driven and model-driven models is expected in the future.

مفهوم

remote sensing;quantitative remote sensing;model-driven;data-driven;deep learning;machine learning

References

  1. 1.
    Antropov O, Rauste Y, Häme T and Praks J. 2017. Polarimetric ALOS PALSAR time series in mapping biomass of boreal forests. Remote Sensing, 9(10): 999
  2. 2.
    Atzberger C. 2004. Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sensing of Environment, 93(1/2): 53-67
  3. 3.
    Berger K, Verrelst J, Féret J, Hank T, Wocher M, Mauser W and Camps-Valls G. 2020. Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. International Journal of Applied Earth Observation and Geoinformation, 92: 102174
  4. 4.
    Beucler T, Pritchard M, Gentine P and Rasp S. 2020. Towards physically-consistent, data-driven models of convection//IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa, HI, USA: IEEE: 3987-3990
  5. 5.
    Beucler T, Rasp S, Pritchard M and Gentine P. 2019. Achieving conservation of energy in neural network emulators for climate modeling. arXiv: 1906.06622
  6. 6.
    Bickel P J, Li B, Tsybakov A B, Van De Geer S A, Yu B, Valdés T, Rivero C, Fan J Q and Van Der Vaart A. 2006. Regularization in statistics. Test, 15(2): 271-344
  7. 7.
    Bolton T and Zanna L. 2019. Applications of deep learning to ocean data inference and subgrid parameterization. Journal of Advances in Modeling Earth Systems, 11(1): 376-399
  8. 8.
    Boukabara S A, Krasnopolsky V, Penny S G, Stewart J Q, McGovern A, Hall D, Ten Hoeve J E, Hickey J, Allen Huang H L, Williams J K, Ide K, Tissot P, Haupt S E, Casey K S, Oza N, Geer A J, Maddy E S and Hoffman R N. 2021. Outlook for exploiting artificial intelligence in the earth and environmental sciences. Bulletin of the American Meteorological Society, 102(5): E1016-E1032
  9. 9.
    Boukabara S A, Krasnopolsky V, Stewart J Q, Maddy E, Shahroudi N, and Hoffman R N. 2020. Realizing the Benefits of AI across the Numerical Weather Prediction Value Chain, Bulletin of the American Meteorological Society, 101(1), 29-33 (CaicedoJ P R, VerrelstJ, Muñoz-MaríJ, Moreno J and Camps-VallsG. 2014. Toward a semiautomatic machine learning retrieval of biophysical parameters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1249-1259)
  10. 10.
    Camps-Valls G and Bruzzone L. 2009. Kernel Methods for Remote Sensing Data Analysis. Chichester: John Wiley and Sons
  11. 11.
    Cannizzaro J P and Carder K L. 2006. Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters. Remote Sensing of Environment, 101(1): 13-24
  12. 12.
    Chang D H and Islam S. 2000. Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment, 74(3): 534-544
  13. 13.
    Chang K L, Cooper O R, West J J, Serre M L, Schultz M G, Lin M Y, Marécal V, Josse B, Deushi M, Sudo K, Liu J H and Keller C A. 2019. A new method (M³ Fusion v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution. Geoscientific Model Development, 12(3): 955-978
  14. 14.
    Charantonis A A, Badran F and Thiria S. 2015. Retrieving the evolution of vertical profiles of Chlorophyll-a from satellite observations using Hidden Markov Models and Self-Organizing Topological Maps. Remote Sensing of Environment, 163: 229-239
  15. 15.
    Chen L F, Zhang Y, Zou M M, Xu Q, Li L J, Li X Y and Tao J H. 2015. Overview of atmospheric CO2 remote sensing from space. Journal of Remote Sensing, 19(1): 1-11
  16. 16.
    Chen W, Zhao J, Cao C X and Tian H J. 2018. Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology. Global Ecology and Conservation, 16: e00479
  17. 17.
    Chen X H, Feng K R, Liu N Y, Lu Y F, Tong Z Y, Ni B B, Liu Z A and Lin N. 2020. RainNet: a large-scale dataset for spatial precipitation downscaling. arXiv: 2012.09700
  18. 18.
    Chen Y Z, Fu B J and Feng X M. 2017. Overview and outlook of remote sensing inversion of vegetation nitrogen content. Acta Ecologica Sinica, 37(18): 6240-6252
  19. 19.
    Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M and Hasiyuya. 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748-767
  20. 20.
    Cintra R S and de Campos Velho H F. 2014. Data assimilation by artificial neural networks for an atmospheric general circulation model: conventional observation. arXiv:1407.4360
  21. 21.
    Combal B, Baret F, Weiss M, Trubuil A, Macé D, Pragnère A, Myneni R, Knyazikhin Y and Wang L. 2003. Retrieval of canopy biophysical variables from bidirectional reflectance: using prior information to solve the ill-posed inverse problem. Remote Sensing of Environment, 84(1): 1-15
  22. 22.
    Davis D T, Chen Z, Tsang L, Hwang J N and Chang A T C. 1993. Retrieval of snow parameters by iterative inversion of a neural network. IEEE Transactions on Geoscience and Remote Sensing, 31(4): 842-852
  23. 23.
    de Bezenac E, Pajot A and Gallinari P. 2018. Deep learning for physical processes: incorporating prior scientific knowledge. arXiv: 1711.07970
  24. 24.
    De Jong K A. 1975. Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ann Arbor, MI, United States: University of Michigan
  25. 25.
    Dong L C, Li S W, Yang J, Shi W X and Zhang L. 2020. Investigating the performance of satellite-based models in estimating the surface PM2.5 over China. Chemosphere, 256: 127051
  26. 26.
    Dueben P D and Bauer P. 2018. Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development, 11(10): 3999-4009
  27. 27.
    Ebert-Uphoff I and Hilburn K. 2020. Evaluation, tuning, and interpretation of neural networks for working with images in meteorological applications. Bulletin of the American Meteorological Society, 101(12): E2149-E2170
  28. 28.
    Edwards J R and Parry M E. 1993. On the use of polynomial regression equations as an alternative to difference scores in organizational research. Academy of Management Journal, 36(6): 1577-1613
  29. 29.
    El Hourany R, Abboud-Abi Saab M, Faour G, Aumont O, Crépon M and Thiria S. 2019. Estimation of secondary phytoplankton pigments from satellite observations using self-organizing maps (SOMs). Journal of Geophysical Research: Oceans, 124(2): 1357-1378
  30. 30.
    Elnaggar A A and Noller J S. 2009. Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas. Remote Sensing, 2(1): 151-165
  31. 31.
    Eyre J R, English S J and Forsythe M. 2020. Assimilation of satellite data in numerical weather prediction. Part I: the early years. Quarterly Journal of the Royal Meteorological Society, 146(726): 49-68
  32. 32.
    Filippi A M, Güneralp İ and Randall J. 2014. Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting. Remote Sensing Letters, 5(5): 432-441
  33. 33.
    Foody G M. 2008. RVM-based multi-class classification of remotely sensed data. International Journal of Remote Sensing, 29(6): 1817-1823
  34. 34.
    Friedman J H. 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19(1): 1-67
  35. 35.
    Gao F, Wang Y P and Hu X Y. 2019. Evaluation of the suitability of Landsat, MERIS, and MODIS for identifying spatial distribution patterns of total suspended matter from a self-organizing map (SOM) perspective. Catena, 172: 699-710
  36. 36.
    Gastellu-Etchegorry J P, Demarez V, Pinel V, and Zagolski F. 1996. Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58(2): 131-156
  37. 37.
    Geer A J. 2021. Learning earth system models from observations: machine learning or data assimilation?. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194): 20200089
  38. 38.
    Gentine P, Pritchard M, Rasp S, Reinaudi G and Yacalis G. 2018. Could machine learning break the convection parameterization deadlock?. Geophysical Research Letters, 45(11): 5742-5751
  39. 39.
    Ghulam A, Porton I and Freeman K. 2014. Detecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 174-192
  40. 40.
    Goldberg D E and Holland J H. 1988. Genetic algorithms and machine learning. Machine Learning, 3: 95-99 [DOI: 183]
  41. 41.
    Gong P. 2009. Some essential questions in remote sensing science and technology. Journal of Remote Sensing, 13(1): 13-23
  42. 42.
    Grody N C and Basist A N. 1996. Global identification of snowcover using SSM/I measurements. IEEE Transactions on Geoscience and Remote Sensing, 34(1): 237-249
  43. 43.
    Grossman Y L, Ustin S L, Jacquemoud S, Sanderson E W, Schmuck G and Verdebout J. 1996. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sensing of Environment, 56(3): 182-193
  44. 44.
    Guo Y X, Tang Q H, Gong D Y and Zhang Z Y. 2017. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sensing of Environment, 198: 140-149
  45. 45.
    Hang R L, Liu Q S, Song H H, Sun Y B, Zhu F P and Pei H C. 2017. Graph regularized nonlinear ridge regression for remote sensing data analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1): 277-285
  46. 46.
    Healey S P, Cohen W B, Yang Z Q, Brewer C K, Brooks E B, Gorelick N, Hernandez A J, Huang C Q, Hughes M J, Kennedy R E, Loveland T R, Moisen G G, Schroeder T A, Stehman S V, Vogelmann J E, Woodcock C E, Yang L M and Zhu Z. 2018. Mapping forest change using stacked generalization: an ensemble approach. Remote Sensing of Environment, 204: 717-728
  47. 47.
    Hoerl A E and Kennard R W. 1970. Ridge regression: applications to nonorthogonal problems. Technometrics, 12(1): 69-82
  48. 48.
    Holland J H. 1975. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambrige: MIT Press
  49. 49.
    Huang H, Ji X L, Xia F, Huang S H, Shang X, Chen H, Zhang M H, Dahlgren R A and Mei K. 2020. Multivariate adaptive regression splines for estimating riverine constituent concentrations. Hydrological Processes, 34(5): 1213-1227
  50. 50.
    Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S and Zhu Y H. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1/2): 109-120
  51. 51.
    Irrgang C, Saynisch-Wagner J and Thomas M. 2020. Machine learning-based prediction of spatiotemporal uncertainties in global wind velocity reanalyses. Journal of Advances in Modeling Earth Systems, 12(5): e2019MS001876
  52. 52.
    Islam T, Srivastava P K, Dai Q, Gupta M and Zhuo L. 2015. Rain rate retrieval algorithm for conical-scanning microwave imagers aided by random forest, RReliefF, and multivariate adaptive regression splines (RAMARS). IEEE Sensors Journal, 15(4): 2186-2193
  53. 53.
    Jacquemoud S and Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75-91
  54. 54.
    Jia X W, Karpatne A, Willard J, Steinbach M, Read J, Hanson P C, Dugan H A and Kumar V. 2018. Physics guided recurrent neural networks for modeling dynamical systems: application to monitoring water temperature and quality in lakes. arXiv: 1810.02880
  55. 55.
    Jiang C M, Esmaeilzadeh S, Azizzadenesheli K, Kashinath K, Mustafa M, Tchelepi H A, Marcus P, Prabhat M and Anandkumar A. 2020b. MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework//SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. Atlanta, GA, USA: IEEE: 1-15
  56. 56.
    Jiang Q O, Xu L D, Sun S Y, Wang M L and Xiao H J. 2021. Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms - A case study in the Miyun Reservoir, China. Ecological Indicators, 124: 107356
  57. 57.
    Jiang S J, Zheng Y and Solomatine D. 2020a. Improving AI system awareness of geoscience knowledge: symbiotic integration of physical approaches and deep learning. Geophysical Research Letters, 47(13): e2020GL088229
  58. 58.
    Kang J, Jin R, Li X, Zhang Y and Zhu Z L. 2018. Spatial upscaling of sparse soil moisture observations based on ridge regression. Remote Sensing, 10(2): 192
  59. 59.
    Keller C A and Evans M J. 2019. Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10. Geoscientific Model Development, 12(3): 1209-1225
  60. 60.
    Koelemeijer R B A, Homan C D and Matthijsen J. 2006. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmospheric Environment, 40(27): 5304-5315
  61. 61.
    Kraft B, Jung M, Körner M and Reichstein M. 2020. Hybrid modeling: fusion of a deep learning approach and a physics-based model for global hydrological modeling. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020: 1537-1544
  62. 62.
    Kukreja S L, Löfberg J and Brenner M J. 2006. A least absolute shrinkage and selection operator (LASSO) for nonlinear system identification. IFAC Proceedings Volumes, 39(1): 814-819
  63. 63.
    Kuter S, Akyurek Z and Weber G W. 2018. Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines. Remote Sensing of Environment, 205: 236-252
  64. 64.
    Lee H J, Liu Y, Coull B A, Schwartz J and Koutrakis P. 2011. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmospheric Chemistry and Physics, 11(15): 7991-8002
  65. 65.
    Lee Y, Han D, Ahn M H, Im J and Lee S J. 2019. Retrieval of total precipitable water from Himawari-8 AHI data: a comparison of random forest, extreme gradient boosting, and deep neural network. Remote Sensing, 11(15): 1741
  66. 66.
    Li D R, Yao Y and Shao Z F. 2014a. Big data in smart city. Geomatics and Information Science of Wuhan University, 39(6): 631-640
  67. 67.
    Li D R, Zhang L P and Xia G S. 2014b. Automatic analysis and mining of remote sensing big data. Acta Geodaetica et Cartographica Sinica, 43(12): 1211-1216
  68. 68.
    Li L, Cheng Y B, Ustin S, Hu X T and Riaño D. 2008. Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression. Advances in Space Research, 41(11): 1755-1763
  69. 69.
    Li L, Ustin S L and Riano D. 2007. Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling. IEEE Geoscience and Remote Sensing Letters, 4(2): 216-220
  70. 70.
    Li T W, Shen H F, Yuan Q Q and Zhang L P. 2020a. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 178-188
  71. 71.
    Li T W, Shen H F, Zeng C, Yuan Q Q and Zhang L P. 2017. Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: methods and assessment. Atmospheric Environment, 152: 477-489
  72. 72.
    Li X, Liu F and Fang M. 2020b. Harmonizing models and observations: data assimilation in earth system science. Science China Earth Sciences, 63(8): 1059-1068
  73. 73.
    Li X W. 2005. Retrospect, prospect and innovation in quantitative remote sensing. Journal of Henan University (Natural Science), 35(4): 49-56
  74. 74.
    Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H, Yao Y J, Yuan W P, Zhang X T and Zhao X. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875-898
  75. 75.
    Liang S L, Li X W and Wang J D. 2013. Quantitative Remote Sensing: Concepts and Algorithms. Beijing: Science Press
  76. 76.
    Liu L, Weng C S, Li S L, Husi L, Hu S and Dong P Y. 2021. Passive remote sensing of ice cloud properties at terahertz wavelengths based on genetic algorithm. Remote Sensing, 13(4): 735
  77. 77.
    Lyu B, Hu Y T, Zhang W X, Du Y S, Luo B, Sun X L, Sun Z, Deng Z, Wang X J, Liu J, Wang X S and Russell A G. 2019. Fusion method combining ground-level observations with chemical transport model predictions using an ensemble deep learning framework: application in China to estimate spatiotemporally-resolved PM2.5 exposure fields in 2014-2017. Environmental Science and Technology, 53(13): 7306-7315
  78. 78.
    Ma J, Ding Y X, Cheng J C P, Jiang F F and Wan Z W. 2019. A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2.5. Journal of Cleaner Production, 237: 117729
  79. 79.
    Ma Y C, Zhang Z, Kang Y H and Özdoğan M. 2021. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sensing of Environment, 259: 112408
  80. 80.
    Malmgren-Hansen D, Laparra V, Aasbjerg Nielsen A and Camps-Valls G. 2019. Statistical retrieval of atmospheric profiles with deep convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 158: 231-240
  81. 81.
    Mo S X, Zhu Y H, Zabaras N, Shi X Q and Wu J C. 2019. Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media. Water Resources Research, 55(1): 703-728
  82. 82.
    Monteith J L. 1965. Evaporation and environment. Symposium of the Society of Exploratory Biology, 19: 205-234
  83. 83.
    Mountrakis G, Im J and Ogole C. 2011. Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): 247-259
  84. 84.
    Pal A, Mahajan S and Norman M R. 2019. Using deep neural networks as cost-effective surrogate models for super-parameterized E3SM radiative transfer. Geophysical Research Letters, 46(11): 6069-6079
  85. 85.
    Parikh J A, DaPonte J S, Damodaran M and Sherman P. 1990. Application of neural networks to pattern recognition problems in remote sensing and medical imagery//Proceedings Volume 1294, Applications of Artificial Neural Networks. Orlando, FL, United States: SPIE
  86. 86.
    Pathak J, Wikner A, Fussell R, Chandra S, Hunt B R, Girvan M and Ott E. 2018. Hybrid forecasting of chaotic processes: using machine learning in conjunction with a knowledge-based model. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(4): 041101
  87. 87.
    Pham T D, Yokoya N, Xia J S, Ha N T, Le N N, Nguyen T T T, Dao T H, Vu T T P, Pham T D and Takeuchi W. 2020. Comparison of machine learning methods for estimating mangrove above-ground biomass using multiple source remote sensing data in the red river delta biosphere reserve, Vietnam. Remote Sensing, 12(8): 1334
  88. 88.
    Priestley C H B and Taylor R J. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2): 81-92
  89. 89.
    Qin K, Rao L L, Xu J, Bai Y, Zou J H, Hao N, Li S S and Yu C. 2017. Estimating ground level NO2 concentrations over central-eastern China using a satellite-based geographically and temporally weighted regression model. Remote Sensing, 9(9): 950
  90. 90.
    Qu Y H, Wang J D, Wan H W, Li X W and Zhou G Q. 2008. A Bayesian network algorithm for retrieving the characterization of land surface vegetation. Remote Sensing of Environment, 112(3): 613-622
  91. 91.
    Rasp S, Pritchard M S and Gentine P. 2018. Deep learning to represent subgrid processes in climate models. Proceedings of the National Academy of Sciences of the United States of America, 115(39): 9684-9689
  92. 92.
    Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N and Prabhat N. 2019. Deep learning and process understanding for data-driven earth system science. Nature, 566(7743): 195-204
  93. 93.
    Ruckstuhl Y, Janjić T and Rasp S. 2021. Training a convolutional neural network to conserve mass in data assimilation. Nonlinear Processes in Geophysics, 28(1): 111-119
  94. 94.
    Sawada Y. 2020. Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model. Journal of Geophysical Research: Atmospheres, 125(20): e2020JD032688
  95. 95.
    Sawaya K E, Olmanson L G, Heinert N J, Brezonik P L and Bauer M E. 2003. Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88(1/2): 144-156
  96. 96.
    Shen H F, Li T W, Yuan Q Q and Zhang L P. 2018. Estimating regional ground-level PM2.5 directly from satellite top-of-atmosphere reflectance using deep belief networks. Journal of Geophysical Research: Atmospheres, 123(24): 13875-13886
  97. 97.
    Sønderby C K, Espeholt L, Heek J, Dehghani M, Oliver A, Salimans T, Agrawal S, Hickey J and Kalchbrenner N. 2020. MetNet: a neural weather model for precipitation forecasting. arXiv: 2003.12140
  98. 98.
    Su T N, Laszlo I, Li Z Q, Wei J and Kalluri S. 2020. Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: application to Himawari-8. Remote Sensing of Environment, 251: 112093
  99. 99.
    Tai A P K, Mickley L J and Jacob D J. 2010. Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: implications for the sensitivity of PM2.5 to climate change. Atmospheric Environment, 44(32): 3976-3984
  100. 100.
    Tedesco M, Pulliainen J, Takala M, Hallikainen M and Pampaloni P. 2004. Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data. Remote Sensing of Environment, 90(1): 76-85
  101. 101.
    Tseng M H, Chen S J, Hwang G H and Shen M Y. 2008. A genetic algorithm rule-based approach for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2): 202-212
  102. 102.
    Tuia D, Roscher R, Wegner J D, Jacobs N, Zhu X X and Camps-Valls G. 2021. Toward a collective agenda on AI for earth science data analysis. IEEE Geoscience and Remote Sensing Magazine, 9(2): 88-104
  103. 103.
    Uyanık G K and Güler N. 2013. A study on multiple linear regression analysis. Procedia-Social and Behavioral Sciences, 106, 234-240.
  104. 104.
    Van der Tol C, Verhoef W, Timmermans J, Verhoef A and Su Z. 2009. An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences, 6(12): 3109-3129
  105. 105.
    van Donkelaar A, Martin R V, Spurr R J D and Burnett R T. 2015. High-resolution satellite-derived PM2.5 from optimal estimation and geographically weighted regression over North America. Environmental Science and Technology, 49(17): 10482-10491
  106. 106.
    Vapnik V, Golowich S E and Smola A. 1996. Support vector method for function approximation, regression estimation and signal processing//Proceedings of the 9th International Conference on Neural Information Processing Systems. Denver, Colorado: MIT Press: 281-287
  107. 107.
    Veerman M A, Pincus R, Stoffer R, van Leeuwen C M, Podareanu D and van Heerwaarden C C. 2021. Predicting atmospheric optical properties for radiative transfer computations using neural networks. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194): 20200095
  108. 108.
    Verrelst J, Camps-Valls G, Muñoz-Marí J, Rivera J P, Veroustraete F, Clevers J G P W and Moreno J. 2015. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review. ISPRS Journal of Photogrammetry and Remote Sensing, 108: 273-290
  109. 109.
    von Rueden L, Mayer S, Sifa R, Bauckhage C and Garcke J. 2020. Combining machine learning and simulation to a hybrid modelling approach: current and future directions//18th International Symposium on Intelligent Data Analysis. Konstanz, Germany: Springer: 548-560
  110. 110.
    Wang H Q, Magagi R, Goïta K, Trudel M, McNairn H and Powers J. 2019. Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm. Remote Sensing of Environment, 231: 111234
  111. 111.
    Wang J and Christopher S A. 2003. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: implications for air quality studies. Geophysical Research Letters, 30(21): 2095
  112. 112.
    Wang L L, Hunt E R, Qu J J, Hao X J and Daughtry C S T. 2013. Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sensing of Environment, 129: 103-110
  113. 113.
    Wei C W, Huang J F, Mansaray L R, Li Z H, Liu W W and Han J H. 2017. Estimation and mapping of winter oilseed rape LAI from high spatial resolution satellite data based on a hybrid method. Remote Sensing, 9(5): 488
  114. 114.
    Wu J L, Kashinath K, Albert A, Chirila D, Prabhat N and Xiao H. 2020. Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems. Journal of Computational Physics, 406: 109209
  115. 115.
    Wu S S, Wang Z Y, Du Z H, Huang B, Zhang F and Liu R Y. 2021. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. International Journal of Geographical Information Science, 35(3): 582-608
  116. 116.
    Xu G H. 1997. Progress and prospects of information science of remote sensing. World Sci-Tech R and D, 19(3): 32-40
  117. 117.
    Xu X Q, Lu J S, Zhang N, Yang T C, He J Y, Yao X, Cheng T, Zhu Y, Cao W X and Tian Y C. 2019. Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 185-196
  118. 118.
    Yang S Y, Yang D W, Chen J S, Santisirisomboon J, Lu W W and Zhao B X. 2020. A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data. Journal of Hydrology, 590: 125206
  119. 119.
    Yin J N, Medellín-Azuara J, Escriva-Bou A and Liu Z. 2021. Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change. Science of the Total Environment, 769: 144715
  120. 120.
    Yu F, Zhao Y S and Li H T. 2012. Soil moisture retrieval based on GA-BP neural networks algorithm. Journal of Infrared and Millimeter Waves, 31(3): 283-288
  121. 121.
    Yuan Q Q, Shen H F, Li T W, Li Z W, Li S W, Jiang Y, Xu H Z, Tan W W, Yang Q Q, Wang J W, Gao J H and Zhang L P. 2020. Deep learning in environmental remote sensing: achievements and challenges. Remote Sensing of Environment, 241: 111716
  122. 122.
    Yuval J and O’Gorman P A. 2020. Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions. Nature Communications, 11(1): 3295
  123. 123.
    Zanna L, Brankart J M, Huber M, Leroux S, Penduff T and Williams P D. 2019. Uncertainty and scale interactions in ocean ensembles: from seasonal forecasts to multidecadal climate predictions. Quarterly Journal of the Royal Meteorological Society, 145(S1): 160-175
  124. 124.
    Zhang B. 2018. Remotely sensed big data era and intelligent information extraction. Geomatics and Information Science of Wuhan University, 43(12): 1861-1871
  125. 125.
    Zhang Y and Li Z Q. 2015. Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sensing of Environment, 160: 252-262
  126. 126.
    Zhang Y Z, Qu Y H, Wang J D, Liang S L and Liu Y. 2012. Estimating leaf area index from MODIS and surface meteorological data using a dynamic Bayesian network. Remote Sensing of Environment, 127: 30-43
  127. 127.
    Zhao W L, Gentine P, Reichstein M, Zhang Y, Zhou S, Wen Y Q, Lin C J, Li X and Qiu G Y. 2019a. Physics-constrained machine learning of evapotranspiration. Geophysical Research Letters, 46(24): 14496-14507
  128. 128.
    Zhu J Y, Tan S R, King J, Derksen C, Lemmetyinen J and Tsang L. 2018. Forward and inverse radar modeling of terrestrial snow using SnowSAR data. IEEE Transactions on Geoscience and Remote Sensing, 56(12): 7122-7132
  129. 129.
    Zou H and Hastie T. 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2): 301-320

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website