{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T21:09:46Z","timestamp":1769548186496,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T00:00:00Z","timestamp":1552867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Greenhouse gas concentrations are increasing over the past few decades, creating the need to measure their concentration with high accuracy, including for determining their trends, sources, and sinks. In this regard, various methods of regional and global control are being developed. One of the measuring methods is passive satellite method, but they allow for you to get data mainly during the day and outside the poles of the Earth. Another method is active lidar; they require the consideration of various aspects that are related to the technical characteristics of the lidar and methods for solving inverse problems. This article discusses the possibility of using lidars for sensing carbon dioxide from space (orbit 450 km) and from a height of 10 km and 23 km, which presumably corresponds to the aircrafts and balloons. As a method of solving the inverse problem, the method of fully connected neural networks with three layers and pre-training of first layer is considered, allowing for the application of additional data, including the IPDA (Integrated Path Differential Absorption) signal, the scattered DIAL (Differential Absorption Lidar) signal, temperature, and pressure profiles. These estimates show the possibility of measuring the average concentration from an orbit height of 450 km with an error of 0.16%, a resolution of 60 km, with a 50 mJ laser pulse energy, and 1 m diameter telescope. It is also shown that it is possible to obtain the concentration profile, including the near-surface concentration with an error of 2 ppm.<\/jats:p>","DOI":"10.3390\/rs11060659","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T12:18:53Z","timestamp":1552911533000},"page":"659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Application of Neural Networks for Retrieval of the CO2 Concentration at Aerospace Sensing by IPDA-DIAL lidar"],"prefix":"10.3390","volume":"11","author":[{"given":"Gennadii G","family":"Matvienko","sequence":"first","affiliation":[{"name":"Laboratory of Lidar Methods, V.E. Zuev Institute of Atmospheric Optics RAS SB, 1, Academician Zuev square, Tomsk 634055, Russia"}]},{"given":"Alexander Ya","family":"Sukhanov","sequence":"additional","affiliation":[{"name":"Laboratory of Lidar Methods, V.E. Zuev Institute of Atmospheric Optics RAS SB, 1, Academician Zuev square, Tomsk 634055, Russia"},{"name":"Department of Automated Control Systems, Tomsk State University of Control Systems and Radioelectronics, 40, prospect Lenina, Tomsk 634050, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s00340-007-2892-3","article-title":"Space-borne remote sensing of CO2, CH4, and N2O by integrated path absorption lidar: A sensitivity analysis","volume":"90","author":"Ehret","year":"2008","journal-title":"J. Appl. Phys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.5194\/amt-11-127-2018","article-title":"Measurement of atmospheric CO2 column concentrations to cloud tops with a pulsed multi-wavelength airborne lidar","volume":"11","author":"Mao","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"768","DOI":"10.3390\/rs9080768","article-title":"Performance Evaluation for China\u2019s Planned CO2-IPDA","volume":"9","author":"Han","year":"2017","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.3390\/rs9101052","article-title":"MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane","volume":"9","author":"Ehret","year":"2017","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"387","DOI":"10.5194\/amt-6-387-2013","article-title":"An airborne amplitude-modulated 1.57 \u03bcm differential laser absorption spectrometer: Simultaneous measurement of partial column-averaged dry air mixing ratio of CO2 and target range","volume":"6","author":"Sakaizawa","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1175\/JTECH-D-13-00128.1","article-title":"Airborne Laser Absorption Spectrometer Measurements of Atmospheric CO2 Column Mole Fractions: Source and Sink Detection and Environmental Impacts on Retrievals","volume":"31","author":"Menzies","year":"2014","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.3390\/rs9111137","article-title":"Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources","volume":"9","author":"Kiemle","year":"2017","journal-title":"Remote Sens."},{"key":"ref_8","unstructured":"Matvienko, G.G., Krekov, G.M., and Sukhanov, A.Y. (2010, January 5\u20139). Space-borne remote sensing of greenhouse gases by IPDA lidar: A potentialities estimate. Proceedings of the 25th International Laser Radar Conference, St. Petersburg, Russia."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1134\/S1024856015030045","article-title":"Assessing the possibilities of sensing CH4 and CO2 greenhouse gases above the underlying surface with satellite-based IPDA lidar","volume":"28","author":"Babchenko","year":"2015","journal-title":"Atmos. Ocean. Opt."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.5194\/amt-9-5227-2016","article-title":"Quantification of uncertainties in OCO-2 measurements of XCO2: Simulations and linear error analysis","volume":"9","author":"Connor","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.5194\/amt-10-2209-2017","article-title":"Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON","volume":"10","author":"Wunch","year":"2017","journal-title":"Atmos. Meas. Tech."},{"key":"ref_12","unstructured":"(2018, September 30). Orbiting Carbon Observatory-2 (OCO-2). Home Page, Available online: https:\/\/ocov2.jpl.nasa.gov\/mission\/quickfacts\/."},{"key":"ref_13","first-page":"589","article-title":"Airborne DIAL-IPDA lidar sensing of carbon dioxide inverse problem solution on basis bionic methods","volume":"30","author":"Sukhanov","year":"2017","journal-title":"Optika Atmosfery i Okeana"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1029\/97RS02219","article-title":"A combined natural orthogonal functions\/neural network technique for radiometric estimations of atmospheric profiles","volume":"33","author":"Frate","year":"1998","journal-title":"Radio Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1029\/98RS02133","article-title":"Neural networks for retrieval of water vapor and liquid water from radiometric data","volume":"33","author":"Frate","year":"1998","journal-title":"Radio Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/36.789630","article-title":"Nonlinear principal component analysis for the radiometric inversion of atmospheric profiles by using neural networks","volume":"37","author":"Frate","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23841","DOI":"10.1029\/1999JD900431","article-title":"Inversion algorithm using neural networks to retrieve atmospheric CO total columns from high-resolution nadir radiances","volume":"104","author":"Clerbaux","year":"1999","journal-title":"J. Geophys. Res."},{"key":"ref_18","unstructured":"Frate, F.D., Casadio, S., and Zehner, C. (2000, January 24\u201328). Retrieval of ozone profiles by using GOME measurements and a neural network algorithm. Proceedings of the IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment, IGARSS\u20192000, Honolulu, HI, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1029\/2000RS002561","article-title":"A neural network technique for inversion of atmospheric observation from microwave limb sounders","volume":"36","author":"Jimenez","year":"2001","journal-title":"Radio Sci."},{"key":"ref_20","first-page":"503","article-title":"Non-linear inversion of Odin sub-mm observation in the lower stratosphere by neural networks","volume":"200","author":"Jimenez","year":"2000","journal-title":"Radio Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14887","DOI":"10.1029\/2001JD900085","article-title":"A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations","volume":"106","author":"Aires","year":"2001","journal-title":"J. Geophys. Res."},{"key":"ref_22","first-page":"D10303","article-title":"Neural network uncertainly assessment using Bayesian statistics with application to remote sensing: 1. Network weights","volume":"109","author":"Aires","year":"2004","journal-title":"J. Geophys. Res."},{"key":"ref_23","first-page":"D10303","article-title":"Neural network uncertainly assessment using Bayesian statistics with application to remote sensing: 2. Output errors","volume":"109","author":"Aires","year":"2004","journal-title":"J. Geophys. Res."},{"key":"ref_24","unstructured":"Sukhanov, A.Y. (2006). Algorithms, Methods, and Software for Solution of Problems of Lidar Sensing of the Atmosphere. [Ph.D. Thesis, Tomsk State University of Control Systems and Radioelectronics, Engineering Sciences]. (In Russian)."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mamun, M.M., and M\u00fcller, D. (2016). Retrieval of Intensive Aerosol Microphysical Parameters from Multiwavelength Raman\/HSRL Lidar: Feasibility Study with Artificial Neural Networks. Atmos. Meas. Tech., 46.","DOI":"10.5194\/amt-2016-7"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.jqsrt.2016.06.034","article-title":"Neural networks for aerosol particles characterization","volume":"184","author":"Berdnik","year":"2016","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_27","unstructured":"Krekov, G.M., Krekova, M.M., Lisenko, A.A., and Sukhanov, A.Y. (,  2008). Identification of minor pathogenic admixtures in the atmosphere based on the method of artificial neural networks, Siberian Aerosols. Proceedings of the 15th Workshop, Tomsk, Russia."},{"key":"ref_28","unstructured":"Sukhanov, A.Y. (July, January 30). On the algorithm of pre-learning of a neural network as applied to some inverse problems of lidar sensing, Atmospheric and Ocean Optics. Atmospheric Physics. Proceedings of the XXII International Symposium, Tomsk, Russia."},{"key":"ref_29","unstructured":"Sukhanov, A.Y., and Krekov, G.M. (2011, January 11\u201317). Recognition of fluorescence spectra of bacteria and polyaromatic hydrocarbons. Proceedings of the All-Russian Conference on Mathematical Methods of Image Recognition, Petrozavodsk, Russia."},{"key":"ref_30","first-page":"1020","article-title":"Capabilities of the neural network method for retrieval of the ozone profile from lidar data","volume":"16","author":"Kataev","year":"2003","journal-title":"Atmos. Ocean. Opt."},{"key":"ref_31","unstructured":"DeepLearning4j (2018, September 20). Deep Learning for Java. Home Page. Available online: https:\/\/deeplearning4j.org\/."},{"key":"ref_32","unstructured":"(2018, September 20). Keras: The Python Deep Learning library. Home Page. Available online: https:\/\/keras.io\/."},{"key":"ref_33","unstructured":"Goodfellow, I.J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., and Bengio, Y. (arXiv, 2013). Pylearn2: A machine learning research library, arXiv."},{"key":"ref_34","unstructured":"Tikhonov, A.N., and Arsenin, V.Y. (1977). Solution of Ill-Posed Problems, Winston & Sons."},{"key":"ref_35","first-page":"362","article-title":"Sounding of stratospheric ozone with an UV bifrequency DIAL: Methods for solving the inverse problem and results of the field experiment","volume":"5","author":"Zuev","year":"1992","journal-title":"Atmos. Ocean. Opt."},{"key":"ref_36","unstructured":"Galushkin, A.I. (2007). Neural Network Theory, Springer."},{"key":"ref_37","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press."},{"key":"ref_38","first-page":"1051","article-title":"The dynamics in vertical distribution of greenhouse gases in the atmosphere","volume":"25","author":"Arshinov","year":"2012","journal-title":"Optika Atmosfery i Okeana."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1134\/S1024856009030087","article-title":"Vertical distribution of greenhouse gases above Western Siberia by the long-term measurement data","volume":"22","author":"Arshinov","year":"2009","journal-title":"Atmos. Ocean. Opt."},{"key":"ref_40","unstructured":"Edwards, B. (1985). Handbook MAP 16, University of Illinois."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1029\/90JA02125","article-title":"Extension of the MSIS Thermospheric Model into the Middle and Lower Atmosphere","volume":"96","author":"Hedin","year":"1991","journal-title":"J. Geophys. Res."},{"key":"ref_42","unstructured":"Komarov, V.S. (1986). Statistical Models of Temperature and Gaseous Constituents of the Atmosphere, Gidrometeoizdat."},{"key":"ref_43","unstructured":"Matvienko, G.G. (2015). Lidar Monitoring of Cloud and Aerosol Fields, Minor. Gaseous Constituents, and Meteorological Parameters of the Atmosphere, IAO SB RAS."},{"key":"ref_44","first-page":"96804I","article-title":"Space-borne remote sensing of CO2 by IPDA lidar with heterodyne detection: Random error estimation","volume":"9680","author":"Matvienko","year":"2015","journal-title":"Proc. SPIE"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/6\/659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:38:48Z","timestamp":1760186328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/6\/659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,18]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["rs11060659"],"URL":"https:\/\/doi.org\/10.3390\/rs11060659","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,18]]}}}