收稿：2021-06-15，
纸质出版：2022-02-07
移动端阅览
定量遥感是从原始遥感观测信息中定量推算或反演出地学参量的理论与方法。传统定量遥感主要基于模型驱动，强调通过数学或物理模型完成推算和反演。随着人工智能技术的发展和普及，数据驱动的方式也逐渐受到广泛关注，其强调的是通过机器学习等方式挖掘遥感观测数据中所包含的信息，完成地学参量的定量反演。在强大计算能力的支持下，数据驱动的方法在定量遥感的多个领域都取得了可喜的成就，但其也具有弱化物理规律及因果关系的缺点。在此背景下，耦合物理规律和机器学习，发展模型和数据共同驱动的反演框架，开始成为新的研究热点。以机器学习辅助物理模型，或以物理规律约束机器学习的研究逐步展开，并初见成效。然而，仍面临着较大的挑战，联合模型的不确定性、泛化性、可迁移性，以及小样本情况下的联合建模等都是急需解决的问题。未来，数据驱动和模型驱动的深度耦合将是待突破的难点和重点。
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.
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 [ DOI: 10.3390/RS9100999 http://dx.doi.org/10.3390/RS9100999 ]
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 [ DOI: 10.1016/j.rse.2004.06.016 http://dx.doi.org/10.1016/j.rse.2004.06.016 ]
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 [ DOI: 10.1016/j.jag.2020.102174 http://dx.doi.org/10.1016/j.jag.2020.102174 ]
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 [ DOI: 10.1109/IGARSS39084.2020.9324569 http://dx.doi.org/10.1109/IGARSS39084.2020.9324569 ]
Beucler T , Rasp S , Pritchard M and Gentine P . 2019 . Achieving conservation of energy in neural network emulators for climate modeling . arXiv : 1906 . 06622
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 [ DOI: 10.1007/BF02607055 http://dx.doi.org/10.1007/BF02607055 ]
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 [ DOI: 10.1029/2018MS001472 http://dx.doi.org/10.1029/2018MS001472 ]
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 [ DOI: 10.1175/BAMS-D-20-0031.1 http://dx.doi.org/10.1175/BAMS-D-20-0031.1 ]
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 ) [ DOI: 10.1109/JSTARS.2014.2298752 http://dx.doi.org/10.1109/JSTARS.2014.2298752 ]
Camps-Valls G and Bruzzone L . 2009 . Kernel Methods for Remote Sensing Data Analysis . Chichester : John Wiley and Sons
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 [ DOI: 10.1016/j.rse.2005.12.002 http://dx.doi.org/10.1016/j.rse.2005.12.002 ]
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 [ DOI: 10.1016/S0034-4257(00)00144-9 http://dx.doi.org/10.1016/S0034-4257(00)00144-9 ]
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 [ DOI: 10.5194/gmd-12-955-2019 http://dx.doi.org/10.5194/gmd-12-955-2019 ]
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 [ DOI: 10.1016/j.rse.2015.03.019 http://dx.doi.org/10.1016/j.rse.2015.03.019 ]
Chen L F , Zhang Y , Zou M M , Xu Q , Li L J , Li X Y and Tao J H . 2015 . Overview of atmospheric CO 2 remote sensing from space . Journal of Remote Sensing , 19 ( 1 ): 1 - 11
陈良富 , 张莹 , 邹铭敏 , 徐谦 , 李令军 , 李小英 , 陶金花 . 2015 . 大气CO 2 浓度卫星遥感进展 . 遥感学报 , 19 ( 1 ): 1- 11 [ DOI: 10.11834/jrs.20153331 http://dx.doi.org/10.11834/jrs.20153331 ]
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 : e 00479 [ DOI: 10.1016/J.GECCO.2018.E00479 http://dx.doi.org/10.1016/J.GECCO.2018.E00479 ]
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
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
陈永喆 , 傅伯杰 , 冯晓明 . 2017 . 遥感反演植被含氮量研究进展 . 生态学报 , 37 ( 18 ): 6240- 6252 [ DOI: 10.5846/stxb201707131271 http://dx.doi.org/10.5846/stxb201707131271 ]
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
陈仲新 , 任建强 , 唐华俊 , 史云 , 冷佩 , 刘佳 , 王利民 , 吴文斌 , 姚艳敏 , 哈斯图亚 . 2016 . 农业遥感研究应用进展与展望 . 遥感学报 , 20 ( 5 ): 748- 767 [ DOI: 10.11834/jrs.20166214 http://dx.doi.org/10.11834/jrs.20166214 ]
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
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 [ DOI: 10.1016/S0034-4257(02)00035-4 http://dx.doi.org/10.1016/S0034-4257(02)00035-4 ]
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 [ DOI: 10.1109/36.239907 http://dx.doi.org/10.1109/36.239907 ]
de Bezenac E , Pajot A and Gallinari P . 2018 . Deep learning for physical processes: incorporating prior scientific knowledge . arXiv : 1711 . 07970
De Jong K A . 1975 . Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ann Arbor, MI, United States: University of Michigan
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 PM 2.5 over China . Chemosphere , 256 : 127051 [ DOI: 10.1016/j.chemosphere.2020.127051 http://dx.doi.org/10.1016/j.chemosphere.2020.127051 ]
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 [ DOI: 10.5194/gmd-11-3999-2018 http://dx.doi.org/10.5194/gmd-11-3999-2018 ]
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 [ DOI: 10.1175/BAMS-D-20-0097.1 http://dx.doi.org/10.1175/BAMS-D-20-0097.1 ]
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 [ DOI: 10.5465/256822 http://dx.doi.org/10.5465/256822 ]
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 [ DOI: 10.1029/2018JC014450 http://dx.doi.org/10.1029/2018JC014450 ]
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 [ DOI: 10.3390/rs2010151 http://dx.doi.org/10.3390/rs2010151 ]
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 [ DOI: 10.1002/qj.3654 http://dx.doi.org/10.1002/qj.3654 ]
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 [ DOI: 10.1080/2150704X.2014.915070 http://dx.doi.org/10.1080/2150704X.2014.915070 ]
Foody G M . 2008 . RVM-based multi-class classification of remotely sensed data . International Journal of Remote Sensing , 29 ( 6 ): 1817 - 1823 [ DOI: 10.1080/01431160701822115 http://dx.doi.org/10.1080/01431160701822115 ]
Friedman J H . 1991 . Multivariate adaptive regression splines . The Annals of Statistics , 19 ( 1 ): 1 - 67 [ DOI: 10.1214/AOS/1176347963 http://dx.doi.org/10.1214/AOS/1176347963 ]
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 [ DOI: 10.1016/j.catena.2018.09.031 http://dx.doi.org/10.1016/j.catena.2018.09.031 ]
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 [ DOI: 10.1016/0034-4257(95)00253-7 http://dx.doi.org/10.1016/0034-4257(95)00253-7 ]
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 [ DOI: 10.1098/rsta.2020.0089 http://dx.doi.org/10.1098/rsta.2020.0089 ]
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 [ DOI: 10.1029/2018GL078202 http://dx.doi.org/10.1029/2018GL078202 ]
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 [ DOI: 10.1016/j.isprsjprs.2013.12.007 http://dx.doi.org/10.1016/j.isprsjprs.2013.12.007 ]
Goldberg D E and Holland J H . 1988 . Genetic algorithms and machine learning . Machine Learning , 3 : 95 - 99 [ DOI: 10.1023/A:1022602019 http://dx.doi.org/10.1023/A:1022602019 183]
Gong P . 2009 . Some essential questions in remote sensing science and technology . Journal of Remote Sensing , 13 ( 1 ): 13 - 23
宫鹏 . 2009 . 遥感科学与技术中的一些前沿问题 . 遥感学报 , 13 ( 1 ): 13 - 23
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 [ DOI: 10.1109/36.481908 http://dx.doi.org/10.1109/36.481908 ]
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 [ DOI: 10.1016/0034-4257(95)00235-9 http://dx.doi.org/10.1016/0034-4257(95)00235-9 ]
Guo Y X , Tang Q H , Gong D Y and Zhang Z Y . 2017 . Estimating ground-level PM 2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model . Remote Sensing of Environment , 198 : 140 - 149 [ DOI: 10.1016/j.rse.2017.06.001 http://dx.doi.org/10.1016/j.rse.2017.06.001 ]
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 [ DOI: 10.1109/JSTARS.2016.2574802 http://dx.doi.org/10.1109/JSTARS.2016.2574802 ]
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 [ DOI: 10.1016/j.rse.2017.09.029 http://dx.doi.org/10.1016/j.rse.2017.09.029 ]
Hoerl A E and Kennard R W . 1970 . Ridge regression: applications to nonorthogonal problems . Technometrics , 12 ( 1 ): 69 - 82 [ DOI: 10.1080/00401706.1970.10488635 http://dx.doi.org/10.1080/00401706.1970.10488635 ]
Holland J H . 1975 . Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence . Cambrige : MIT Press
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 [ DOI: 10.1002/hyp.13669 http://dx.doi.org/10.1002/hyp.13669 ]
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 [ DOI: 10.1016/S0378-1127(99)00278-9 http://dx.doi.org/10.1016/S0378-1127(99)00278-9 ]
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 ): e2019MS 001876 [ DOI: 10.1029/2019MS001876 http://dx.doi.org/10.1029/2019MS001876 ]
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 [ DOI: 10.1109/JSEN.2014.2372814 http://dx.doi.org/10.1109/JSEN.2014.2372814 ]
Jacquemoud S and Baret F . 1990 . PROSPECT: a model of leaf optical properties spectra . Remote Sensing of Environment , 34 ( 2 ): 75 - 91 [ DOI: 10.1016/0034-4257(90)90100-Z http://dx.doi.org/10.1016/0034-4257(90)90100-Z ]
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
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 [ DOI: 10.1109/SC41405.2020.00013 http://dx.doi.org/10.1109/SC41405.2020.00013 ]
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 [ DOI: 10.1016/j.ecolind.2021.107356 http://dx.doi.org/10.1016/j.ecolind.2021.107356 ]
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 ): e2020GL 088229 [ DOI: 10.1029/2020GL088229 http://dx.doi.org/10.1029/2020GL088229 ]
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 [ DOI: 10.3390/rs10020192 http://dx.doi.org/10.3390/rs10020192 ]
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 [ DOI: 10.5194/gmd-12-1209-2019 http://dx.doi.org/10.5194/gmd-12-1209-2019 ]
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 [ DOI: 10.1016/j.atmosenv.2006.04.044 http://dx.doi.org/10.1016/j.atmosenv.2006.04.044 ]
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 [ DOI: 10.5194/isprs-archives-XLIII-B2-2020-1537-2020 http://dx.doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020 ]
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 [ DOI: 10.3182/20060329-3-AU-2901.00128 http://dx.doi.org/10.3182/20060329-3-AU-2901.00128 ]
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 [ DOI: 10.1016/j.rse.2017.11.021 http://dx.doi.org/10.1016/j.rse.2017.11.021 ]
Lee H J , Liu Y , Coull B A , Schwartz J and Koutrakis P . 2011 . A novel calibration approach of MODIS AOD data to predict PM 2.5 concentrations . Atmospheric Chemistry and Physics , 11 ( 15 ): 7991 - 8002 [ DOI: 10.5194/acp-11-7991-2011 http://dx.doi.org/10.5194/acp-11-7991-2011 ]
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 [ DOI: 10.3390/rs11151741 http://dx.doi.org/10.3390/rs11151741 ]
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
李德仁 , 姚远 , 邵振峰 . 2014a . 智慧城市中的大数据 . 武汉大学学报(信息科学版) , 39 ( 6 ): 631-640 [ DOI: 10.13203/j.whugis20140135 http://dx.doi.org/10.13203/j.whugis20140135 ]
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
李德仁 , 张良培 , 夏桂松 . 2014b . 遥感大数据自动分析与数据挖掘 . 测绘学报 , 43 ( 12 ): 1211-1216 [ DOI: 10.13485/jc.nki1.1-20892.0140.187 http://dx.doi.org/10.13485/jc.nki1.1-20892.0140.187 ]
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 [ DOI: 10.1016/j.asr.2008.02.015 http://dx.doi.org/10.1016/j.asr.2008.02.015 ]
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 [ DOI: 10.1109/LGRS.2006.888847 http://dx.doi.org/10.1109/LGRS.2006.888847 ]
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 PM 2.5 . ISPRS Journal of Photogrammetry and Remote Sensing , 167 : 178 - 188 [ DOI: 10.1016/j.isprsjprs.2020.06.019 http://dx.doi.org/10.1016/j.isprsjprs.2020.06.019 ]
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 PM 2.5 distribution in China: methods and assessment . Atmospheric Environment , 152 : 477 - 489 [ DOI: 10.1016/j.atmosenv.2017.01.004 http://dx.doi.org/10.1016/j.atmosenv.2017.01.004 ]
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 [ DOI: 10.1007/s11430-019-9620-x http://dx.doi.org/10.1007/s11430-019-9620-x ]
Li X W . 2005 . Retrospect, prospect and innovation in quantitative remote sensing . Journal of Henan University (Natural Science) , 35 ( 4 ): 49 - 56
李小文 . 2005 . 定量遥感的发展与创新 . 河南大学学报(自然科学版) , 35 ( 4 ): 49- 56 [ DOI: 10.15991/j.cnki.411100.2005.04.012 http://dx.doi.org/10.15991/j.cnki.411100.2005.04.012 ]
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
梁顺林 , 程洁 , 贾坤 , 江波 , 刘强 , 刘素红 , 肖志强 , 谢先红 , 姚云军 , 袁文平 , 张晓通 , 赵祥 . 2016 . 陆表定量遥感反演方法的发展新动态 . 遥感学报 , 20 ( 5 ): 875- 898 [ DOI: 10.11834/jrs.20166258 http://dx.doi.org/10.11834/jrs.20166258 ]
Liang S L , Li X W and Wang J D . 2013 . Quantitative Remote Sensing: Concepts and Algorithms . Beijing : Science Press
梁顺林 , 李小文 , 王锦地 . 2013 . 定量遥感: 理念与算法 . 北京 : 科学出版社
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 [ DOI: 10.3390/rs13040735 http://dx.doi.org/10.3390/rs13040735 ]
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 PM 2.5 exposure fields in 2014-2017 . Environmental Science and Technology , 53 ( 13 ): 7306 - 7315 [ DOI: 10.1021/acs.est.9b01117 http://dx.doi.org/10.1021/acs.est.9b01117 ]
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 PM 2.5 . Journal of Cleaner Production , 237 : 117729 [ DOI: 10.1016/j.jclepro.2019.117729 http://dx.doi.org/10.1016/j.jclepro.2019.117729 ]
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 [ DOI: 10.1016/j.rse.2021.112408 http://dx.doi.org/10.1016/j.rse.2021.112408 ]
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 [ DOI: 10.1016/j.isprsjprs.2019.10.002 http://dx.doi.org/10.1016/j.isprsjprs.2019.10.002 ]
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 [ DOI: 10.1029/2018wr023528 http://dx.doi.org/10.1029/2018wr023528 ]
Monteith J L . 1965 . Evaporation and environment . Symposium of the Society of Exploratory Biology , 19 : 205 - 234
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 [ DOI: 10.1016/j.isprsjprs.2010.11.001 http://dx.doi.org/10.1016/j.isprsjprs.2010.11.001 ]
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 [ DOI: 10.1029/2018GL081646 http://dx.doi.org/10.1029/2018GL081646 ]
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 [ DOI: 10.1117/12.21165 http://dx.doi.org/10.1117/12.21165 ]
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 [ DOI: 10.1063/1.5028373 http://dx.doi.org/10.1063/1.5028373 ]
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 [ DOI: 10.3390/rs12081334 http://dx.doi.org/10.3390/rs12081334 ]
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 [ DOI: 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 http://dx.doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 ]
Qin K , Rao L L , Xu J , Bai Y , Zou J H , Hao N , Li S S and Yu C . 2017 . Estimating ground level NO 2 concentrations over central-eastern China using a satellite-based geographically and temporally weighted regression model . Remote Sensing , 9 ( 9 ): 950 [ DOI: 10.3390/rs9090950 http://dx.doi.org/10.3390/rs9090950 ]
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 [ DOI: 10.1016/j.rse.2007.03.031 http://dx.doi.org/10.1016/j.rse.2007.03.031 ]
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 [ DOI: 10.1073/pnas.1810286115 http://dx.doi.org/10.1073/pnas.1810286115 ]
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 [ DOI: 10.1038/s41586-019-0912-1 http://dx.doi.org/10.1038/s41586-019-0912-1 ]
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 [ DOI: 10.5194/npg-28-111-2021 http://dx.doi.org/10.5194/npg-28-111-2021 ]
Sawada Y . 2020 . Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model. Journal of Geophysical Research: Atmospheres, 125(20): e2020JD 032688 [ DOI: 10.1029/2020JD032688 http://dx.doi.org/10.1029/2020JD032688 ]
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 [ DOI: 10.1016/j.rse.2003.04.006 http://dx.doi.org/10.1016/j.rse.2003.04.006 ]
Shen H F , Li T W , Yuan Q Q and Zhang L P . 2018 . Estimating regional ground-level PM 2.5 directly from satellite top-of-atmosphere reflectance using deep belief networks . Journal of Geophysical Research : Atmospheres , 123 ( 24 ): 13875 - 13886 [ DOI: 10.1029/2018JD028759 http://dx.doi.org/10.1029/2018JD028759 ]
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
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 [ DOI: 10.1016/j.rse.2020.112093 http://dx.doi.org/10.1016/j.rse.2020.112093 ]
Tai A P K , Mickley L J and Jacob D J . 2010 . Correlations between fine particulate matter (PM 2.5 ) and meteorological variables in the United States: implications for the sensitivity of PM 2.5 to climate change . Atmospheric Environment , 44 ( 32 ): 3976 - 3984 [ DOI: 10.1016/j.atmosenv.2010.06.060 http://dx.doi.org/10.1016/j.atmosenv.2010.06.060 ]
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 [ DOI: 10.1016/j.rse.2003.12.002 http://dx.doi.org/10.1016/j.rse.2003.12.002 ]
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 [ DOI: 10.1016/j.isprsjprs.2007.09.001 http://dx.doi.org/10.1016/j.isprsjprs.2007.09.001 ]
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 [ DOI: 10.1109/MGRS.2020.3043504 http://dx.doi.org/10.1109/MGRS.2020.3043504 ]
Uyanık G K and Güler N . 2013 . A study on multiple linear regression analysis . Procedia-Social and Behavioral Sciences , 106 , 234 - 240 .
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 [ DOI: 10.5194/bg-6-3109-2009 http://dx.doi.org/10.5194/bg-6-3109-2009 ]
van Donkelaar A , Martin R V , Spurr R J D and Burnett R T . 2015 . High-resolution satellite-derived PM 2.5 from optimal estimation and geographically weighted regression over North America . Environmental Science and Technology , 49 ( 17 ): 10482 - 10491 [ DOI: 10.1021/acs.est.5b02076 http://dx.doi.org/10.1021/acs.est.5b02076 ]
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
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 [ DOI: 10.1098/rsta.2020.0095 http://dx.doi.org/10.1098/rsta.2020.0095 ]
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 [ DOI: 10.1016/j.isprsjprs.2015.05.005 http://dx.doi.org/10.1016/j.isprsjprs.2015.05.005 ]
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 [ DOI: 10.1007/978-3-030-44584-3_43 http://dx.doi.org/10.1007/978-3-030-44584-3_43 ]
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 [ DOI: 10.1016/j.rse.2019.111234 http://dx.doi.org/10.1016/j.rse.2019.111234 ]
Wang J and Christopher S A . 2003 . Intercomparison between satellite-derived aerosol optical thickness and PM 2.5 mass: implications for air quality studies . Geophysical Research Letters , 30 ( 21 ): 2095 [ DOI: 10.1029/2003GL018174 http://dx.doi.org/10.1029/2003GL018174 ]
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 [ DOI: 10.1016/j.rse.2012.10.027 http://dx.doi.org/10.1016/j.rse.2012.10.027 ]
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 [ DOI: 10.3390/rs9050488 http://dx.doi.org/10.3390/rs9050488 ]
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 [ DOI: 10.1016/j.jcp.2019.109209 http://dx.doi.org/10.1016/j.jcp.2019.109209 ]
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 [ DOI: 10.1080/13658816.2020.1775836 http://dx.doi.org/10.1080/13658816.2020.1775836 ]
Xu G H . 1997 . Progress and prospects of information science of remote sensing . World Sci-Tech R and D , 19 ( 3 ): 32 - 40
徐冠华 . 1997 . 遥感信息科学的进展和展望 . 世界科技研究与发展 , 19 ( 3 ): 32 - 40
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 [ DOI: 10.1016/j.isprsjprs.2019.02.013 http://dx.doi.org/10.1016/j.isprsjprs.2019.02.013 ]
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 [ DOI: 10.1016/j.jhydrol.2020.125206 http://dx.doi.org/10.1016/j.jhydrol.2020.125206 ]
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 [ DOI: 10.1016/j.scitotenv.2020.144715 http://dx.doi.org/10.1016/j.scitotenv.2020.144715 ]
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
余凡 , 赵英时 , 李海涛 . 2012 . 基于遗传BP神经网络的主被动遥感协同反演土壤水分 . 红外与毫米波学报 , 31 ( 3 ): 283- 288 [ DOI: 10.3724/SP.J.1010.2012.00283 http://dx.doi.org/10.3724/SP.J.1010.2012.00283 ]
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 [ DOI: 10.1016/J.RSE.2020.111716 http://dx.doi.org/10.1016/J.RSE.2020.111716 ]
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 [ DOI: 10.1038/s41467-020-17142-3 http://dx.doi.org/10.1038/s41467-020-17142-3 ]
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 [ DOI: 10.1002/qj.3397 http://dx.doi.org/10.1002/qj.3397 ]
Zhang B . 2018 . Remotely sensed big data era and intelligent information extraction . Geomatics and Information Science of Wuhan University , 43 ( 12 ): 1861 - 1871
张兵 . 2018 . 遥感大数据时代与智能信息提取 . 武汉大学学报(信息科学版) , 43 ( 12 ): 1861- 1871 [ DOI: 10.13203/j.whugis20180172 http://dx.doi.org/10.13203/j.whugis20180172 ]
Zhang Y and Li Z Q . 2015 . Remote sensing of atmospheric fine particulate matter (PM 2.5 ) mass concentration near the ground from satellite observation . Remote Sensing of Environment , 160 : 252 - 262 [ DOI: 10.1016/j.rse.2015.02.005 http://dx.doi.org/10.1016/j.rse.2015.02.005 ]
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 [ DOI: 10.1016/j.rse.2012.08.015 http://dx.doi.org/10.1016/j.rse.2012.08.015 ]
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 [ DOI: 10.1029/2019GL085291 http://dx.doi.org/10.1029/2019GL085291 ]
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 [ DOI: 10.1109/TGRS.2018.2848642 http://dx.doi.org/10.1109/TGRS.2018.2848642 ]
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 [ DOI: 10.1111/j.1467-9868.2005.00503.x http://dx.doi.org/10.1111/j.1467-9868.2005.00503.x ]
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