{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:18:30Z","timestamp":1775024310180,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42074042"],"award-info":[{"award-number":["42074042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41775034"],"award-info":[{"award-number":["41775034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model\u2019s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m\/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval.<\/jats:p>","DOI":"10.3390\/rs13234820","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4820","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiaoxu","family":"Liu","sequence":"first","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Weihua","family":"Bai","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Junming","family":"Xia","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6888-280X","authenticated-orcid":false,"given":"Feixiong","family":"Huang","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Cong","family":"Yin","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Yueqiang","family":"Sun","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Qifei","family":"Du","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Xiangguang","family":"Meng","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Congliang","family":"Liu","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Peng","family":"Hu","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]},{"given":"Guangyuan","family":"Tan","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences (NSSC\/CAS), Beijing 100190, China"},{"name":"Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China"},{"name":"Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC\/CAS and University of Graz, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","first-page":"331","article-title":"A passive reflectometry and interferometry system (PARIS) application to ocean altimetry","volume":"17","year":"1993","journal-title":"ESA J."},{"key":"ref_2","unstructured":"Auber, J., Bibaut, A., and Rigal, J. (1994, January 20\u201323). Characterization of Multipath on Land and Sea at GPS Frequencies. Proceedings of the 7th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 1994), Salt Lake City, UT, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1029\/98GL51615","article-title":"Effect of sea roughness on bistatically scattered range coded signals from the Global Positioning System","volume":"25","author":"Garrison","year":"1998","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/TGRS.2011.2162245","article-title":"Simulation of L-Band Bistatic Returns from the Ocean Surface: A Facet Approach with Application to Ocean GNSS Reflectometry","volume":"50","author":"Clarizia","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/TGRS.2005.845643","article-title":"Detection and Processing of bistatically reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing","volume":"43","author":"Gleason","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5435","DOI":"10.1002\/2015GL064204","article-title":"Spaceborne GNSS Reflectometry for Ocean Winds: First Results from the UK TechDemoSat-1 Mission","volume":"42","author":"Foti","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3418","DOI":"10.1109\/JSTARS.2017.2674305","article-title":"An Assessment of Non-Geophysical Effects in Spaceborne GNSS Reflectometry Data from the UK TechDemoSat-1 Mission","volume":"10","author":"Foti","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4678","DOI":"10.1109\/JSTARS.2016.2602703","article-title":"The GNSS Reflectometry Response to the Ocean Surface Winds and Waves","volume":"9","author":"Soisuvarn","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4419","DOI":"10.1109\/TGRS.2016.2541343","article-title":"Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission","volume":"54","author":"Clarizia","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Unwin, M., Jales, P., Blunt, P., Duncan, S., Brummitt, M., and Ruf, C. (2013, January 2\u20139). The SGR-ReSI and its application for GNSS reflectometry on the NASA EV-2 CYGNSS mission. Proceedings of the 2013 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2013.6497151"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1175\/1520-0426(2004)021<0515:ROOSWS>2.0.CO;2","article-title":"Retrieval of Ocean Surface Wind Speed and Wind Direction Using Reflected GPS Signals","volume":"21","author":"Komjathy","year":"2004","journal-title":"J. Atmos. Ocean Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2011RS004683","article-title":"GNSS-R ground-based and airborne campaigns for ocean, land, ice, and snow techniques: Application to the GOLD-RTR data sets","volume":"46","author":"Cardellach","year":"2011","journal-title":"Radio Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111944","DOI":"10.1016\/j.rse.2020.111944","article-title":"Pan-tropical soil moisture mapping based on a three-layer model from CYGNSS GNSS-R data","volume":"247","author":"Yan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Stilla, D., Zribi, M., Pierdicca, N., Baghdadi, N., and Huc, M. (2020). Desert Roughness Retrieval Using CYGNSS GNSS-R Data. Remote Sens., 12.","DOI":"10.3390\/rs12040743"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Carreno-Luengo, H., Luzi, G., and Crosetto, M. (2020). Above-Ground Biomass Retrieval over Tropical Forests: A Novel GNSS-R Approach with CyGNSS. Remote Sens., 12.","DOI":"10.3390\/rs12091368"},{"key":"ref_16","unstructured":"Jacobson, M., Emery, W., and Westwater, E. (1996, January 31). Oceanic wind vector determination using a dual-frequency microwave airborne radiometer theory and experiment. Proceedings of the 1996 International Geoscience and Remote Sensing Symposium (IGARSS \u201996.), Lincoln, NE, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/36.974994","article-title":"Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements","volume":"39","author":"Monaldo","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6829","DOI":"10.1109\/TGRS.2014.2303831","article-title":"Spaceborne GNSS-R Minimum Variance Wind Speed Estimator","volume":"52","author":"Clarizia","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1109\/TGRS.2007.892009","article-title":"Use of Neural Networks for Automatic Classification from High-Resolution Images","volume":"45","author":"Frate","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1016\/j.asr.2008.02.012","article-title":"Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem","volume":"41","author":"Chi","year":"2008","journal-title":"Adv. Space Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Eroglu, O., Kurum, M., Boyd, D., and G\u00fcrb\u00fcz, A.C. (2019). High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11192272"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/JSTARS.2020.2968156","article-title":"Wind Speed Estimation from CYGNSS Using Artificial Neural Networks","volume":"13","author":"Reynolds","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/LGRS.2019.2948566","article-title":"A GNSS-R Geophysical Model Function: Machine Learning for Wind Speed Retrievals","volume":"17","author":"Asgarimehr","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9756","DOI":"10.1109\/TGRS.2019.2929002","article-title":"Application of Neural Network to GNSS-R Wind Speed Retrieval","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5971","DOI":"10.1109\/JSTARS.2020.3010879","article-title":"Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval","volume":"13","author":"Chu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Balasubramaniam, R., and Ruf, C. (2020). Neural Network Based Quality Control of CYGNSS Wind Retrieval. Remote Sens., 12.","DOI":"10.3390\/rs12172859"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/JSTARS.2018.2832981","article-title":"The CYGNSS Level 1 Calibration Algorithm and Error Analysis Based on On-Orbit Measurements","volume":"12","author":"Gleason","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1146\/annurev.neuro.23.1.315","article-title":"Mechanisms of Visual Attention in the Human Cortex","volume":"23","author":"Kastner","year":"2000","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms, Chapman and Hall\/CRC.","DOI":"10.1201\/b12207"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Senyurek, V., Lei, F., Boyd, D., Kurum, M., Gurbuz, A.C., and Moorhead, R. (2020). Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN Sites in CONUS. Remote Sens., 12.","DOI":"10.3390\/rs12071168"},{"key":"ref_34","first-page":"157","article-title":"Random Forests","volume":"Volume 45","author":"Cutler","year":"2011","journal-title":"Machine Learning\u2014ML"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.renene.2018.08.044","article-title":"Solar Radiation Forecasting Using Artificial Neural Network and Random Forest Methods: Application to Normal Beam, Horizontal Diffuse and Global Components","volume":"132","author":"Benali","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Maddikunta, P.K.R., Kaluri, R., Singh, S., Gadekallu, T.R., Alazab, M., and Tariq, U. (2020). A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU. Electronics, 9.","DOI":"10.3390\/electronics9020219"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zamani Joharestani, M., Cao, C., Ni, X., Bashir, B., and Talebiesfandarani, S. (2019). PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere, 10.","DOI":"10.3390\/atmos10070373"},{"key":"ref_39","unstructured":"Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., and Vapnik, V. (1996, January 3). Support Vector Regression Machines. Proceedings of the 9th International Conference on Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/JSTARS.2018.2833075","article-title":"Development of the CYGNSS Geophysical Model Function for Wind Speed","volume":"12","author":"Ruf","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4534","DOI":"10.1109\/JSTARS.2018.2873241","article-title":"TDS-1 GNSS Reflectometry: Development and Validation of Forward Scattering Winds","volume":"11","author":"Asgarimehr","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1016\/j.patrec.2012.05.019","article-title":"Efficient Feature Selection Filters for High-Dimensional Data","volume":"33","author":"Ferreira","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/JSTARS.2018.2825948","article-title":"Assessment of CYGNSS Wind Speed Retrieval Uncertainty","volume":"12","author":"Ruf","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4820\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:36:47Z","timestamp":1760168207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4820"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,27]]},"references-count":43,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234820"],"URL":"https:\/\/doi.org\/10.3390\/rs13234820","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,27]]}}}