{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:09:14Z","timestamp":1773817754814,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T00:00:00Z","timestamp":1614038400000},"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>In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat\/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3\/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson\u2019s correlation coefficient (R) = 0.96 and root mean square error (RMSE) \u2243 39 m3\/ha when applied to the test dataset and average R = 0.86 and RMSE \u2243 75 m3\/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.<\/jats:p>","DOI":"10.3390\/rs13040809","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T20:19:36Z","timestamp":1614111576000},"page":"809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1882-6321","authenticated-orcid":false,"given":"Emanuele","family":"Santi","sequence":"first","affiliation":[{"name":"Institute of Applied Physics\u2013National Research Council of Italy (IFAC\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3459-6693","authenticated-orcid":false,"given":"Marta","family":"Chiesi","sequence":"additional","affiliation":[{"name":"Institute of BioEconomy\u2013National Research Council of Italy (IBE\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3790-8288","authenticated-orcid":false,"given":"Giacomo","family":"Fontanelli","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics\u2013National Research Council of Italy (IFAC\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7711-7229","authenticated-orcid":false,"given":"Alessandro","family":"Lapini","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics\u2013National Research Council of Italy (IFAC\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3414-4531","authenticated-orcid":false,"given":"Simonetta","family":"Paloscia","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics\u2013National Research Council of Italy (IFAC\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3155-8918","authenticated-orcid":false,"given":"Simone","family":"Pettinato","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics\u2013National Research Council of Italy (IFAC\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]},{"given":"Giuliano","family":"Ramat","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics\u2013National Research Council of Italy (IFAC\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7316-643X","authenticated-orcid":false,"given":"Leonardo","family":"Santurri","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics\u2013National Research Council of Italy (IFAC\u2013CNR), Via Madonna del Piano 10, 50019 Florence, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.foreco.2015.06.014","article-title":"Dynamics of Global Forest Area: Results from the FAO Global Forest Resources Assessment 2015","volume":"352","author":"Keenan","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.foreco.2015.04.022","article-title":"New Estimates of CO2 Forest Emissions and Removals: 1990\u20132015","volume":"352","author":"Federici","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_3","unstructured":"Waring, R.H., and Running, S.W. (2010). Forest Ecosystems: Analysis at Multiple Scales, Elsevier."},{"key":"ref_4","first-page":"69","article-title":"Forest Ecosystem Inventory and Monitoring as a Framework for Terrestrial Natural Renewable Resource Survey Programmes","volume":"136","author":"Corona","year":"2002","journal-title":"Plant. Biosyst. Int. J. Deal. All Asp. Plant. Biol."},{"key":"ref_5","unstructured":"(2020, December 16). Definitional Issues Related to Reducing Emissions from Deforestation in Developing Countries. Available online: http:\/\/www.fao.org\/3\/j9345e\/j9345e12.htm."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kindermann, G., McCallum, I., Fritz, S., and Obersteiner, M. (2008). A Global Forest Growing Stock, Biomass and Carbon Map Based on FAO Statistics. Silva. Fenn., 42.","DOI":"10.14214\/sf.244"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1080\/01431169408954284","article-title":"Retrieval of Forest Biomass from SAR Data","volume":"15","author":"Beaudoin","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","first-page":"25","article-title":"How to Estimate Forest Carbon for Large Areas from Inventory Data","volume":"102","author":"Smith","year":"2004","journal-title":"J. For."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A Survey of Remote Sensing-Based Aboveground Biomass Estimation Methods in Forest Ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production","volume":"54","author":"Running","year":"2004","journal-title":"BioScience"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20069","DOI":"10.1029\/2000JD000115","article-title":"Variations in Northern Vegetation Activity Inferred from Satellite Data of Vegetation Index during 1981 to 1999","volume":"106","author":"Zhou","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_12","unstructured":"Carl\u00e0, R., Santurri, L., Bonora, L., and Conese, C. (September, January 31). Multitemporal Burnt Area Detection Methods Based on a Couple of Images Acquired after the Fire Event. Proceedings of the SPIE REMOTE SENSING, Berlin, Germany."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111383","DOI":"10.1016\/j.rse.2019.111383","article-title":"Remote Sensing of the Terrestrial Carbon Cycle: A Review of Advances over 50 Years","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1109\/36.885208","article-title":"Modeling Lidar Returns from Forest Canopies","volume":"38","author":"Ranson","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"073699","DOI":"10.1117\/1.JRS.7.073699","article-title":"Application of a Single-Tree Identification Algorithm to LiDAR Data for the Simulation of Stem Volume Current Annual Increment","volume":"7","author":"Bottai","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lefsky, M.A., Harding, D.J., Keller, M., Cohen, W.B., Carabajal, C.C., Del Bom Espirito-Santo, F., Hunter, M.O., and de Oliveira, R. (2005). Estimates of Forest Canopy Height and Aboveground Biomass Using ICESat. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL023971"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(95)00224-3","article-title":"Estimation of Tree Heights and Stand Volume Using an Airborne Lidar System","volume":"56","author":"Nilsson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1093\/forestry\/cpw016","article-title":"A Principal Component Approach for Predicting the Stem Volume in Eucalyptus Plantations in Brazil Using Airborne LiDAR Data","volume":"89","author":"Silva","year":"2016","journal-title":"Forestry"},{"key":"ref_19","first-page":"G04021","article-title":"Mapping Forest Canopy Height Globally with Spaceborne Lidar","volume":"116","author":"Simard","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1111\/geb.13158","article-title":"Evaluating the Potential of Full-waveform Lidar for Mapping Pan-tropical Tree Species Richness","volume":"29","author":"Marselis","year":"2020","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping Global Forest Canopy Height through Integration of GEDI and Landsat Data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/8.45128","article-title":"Measuring the Propagation Properties of a Forest Canopy Using a Polarimetric Scatterometer","volume":"38","author":"Ulaby","year":"1990","journal-title":"IEEE Trans. Antennas Propagat."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/36.551929","article-title":"The Potential of Multifrequency Polarimetric SAR in Assessing Agricultural and Arboreous Biomass","volume":"35","author":"Ferrazzoli","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TGRS.1987.289861","article-title":"L-Band Radar Backscatter Modeling of Forest Stands","volume":"GE-25","author":"Richards","year":"1987","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/36.134089","article-title":"Relating Forest Biomass to SAR Data","volume":"30","author":"Beaudoin","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5167","DOI":"10.1109\/JSTARS.2019.2957549","article-title":"Aboveground Biomass Mapping Using ALOS-2\/PALSAR-2 Time-Series Images for Borneo\u2019s Forest","volume":"12","author":"Hayashi","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1016\/j.rse.2011.03.020","article-title":"The BIOMASS Mission: Mapping Global Forest Biomass to Better Understand the Terrestrial Carbon Cycle","volume":"115","author":"Quegan","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1109\/TGRS.2007.901027","article-title":"ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment","volume":"45","author":"Rosenqvist","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2014.04.011","article-title":"Operational Performance of the ALOS Global Systematic Acquisition Strategy and Observation Plans for ALOS-2 PALSAR-2","volume":"155","author":"Rosenqvist","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2010.08.022","article-title":"Soil Moisture Limitations on Monitoring Boreal Forest Regrowth Using Spaceborne L-Band SAR Data","volume":"115","author":"Kasischke","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1109\/36.843016","article-title":"Effects of Environmental Conditions on Boreal Forest Classification and Biomass Estimates with SAR","volume":"38","author":"Ranson","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1109\/36.312903","article-title":"Radar Estimates of Aboveground Biomass in Boreal Forests of Interior Alaska","volume":"32","author":"Rignot","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/36.841999","article-title":"Estimation of Crown and Stem Water Content and Biomass of Boreal Forest Using Polarimetric SAR Imagery","volume":"38","author":"Saatchi","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Stelmaszczuk-G\u00f3rska, M., Urbazaev, M., Schmullius, C., and Thiel, C. (2018). Estimation of Above-Ground Biomass over Boreal Forests on Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10101550"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.isprsjprs.2019.01.002","article-title":"TanDEM-X Digital Surface Models in Boreal Forest above-Ground Biomass Change Detection","volume":"148","author":"Karila","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1016\/j.rse.2011.03.021","article-title":"Forest Biomass Mapping from Lidar and Radar Synergies","volume":"115","author":"Sun","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TGRS.2008.2009437","article-title":"Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign","volume":"47","author":"Hajnsek","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/TGRS.2011.2176133","article-title":"Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass","volume":"50","author":"Neumann","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Antropov, O., Rauste, Y., H\u00e4me, T., and Praks, J. (2017). Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests. Remote Sens., 9.","DOI":"10.3390\/rs9100999"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TGRS.2011.2159614","article-title":"Multibaseline Polarimetric SAR Tomography of a Boreal Forest at P- and L-Bands","volume":"50","author":"Tebaldini","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","unstructured":"Santoro, M., and Cartus, O. (2019). ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest above-Ground Biomass for the Year 2017 V1, Centre for Environmental Analysis."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/36.134090","article-title":"Dependence of Radar Backscatter on Coniferous Forest Biomass","volume":"30","author":"Dobson","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/36.295053","article-title":"Mapping Biomass of a Northern Forest Using Multifrequency SAR Data","volume":"32","author":"Ranson","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1109\/36.312892","article-title":"Backscattering Properties of Boreal Forests at the C- and X-Bands","volume":"32","author":"Pulliainen","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1016\/j.rse.2011.01.008","article-title":"Aboveground Biomass Retrieval in Tropical Forests\u2014The Potential of Combined X- and L-Band SAR Data Use","volume":"115","author":"Englhart","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_46","first-page":"585","article-title":"Tropical Forest Measurement by Interferometric Height Modeling and P-Band Radar Backscatter","volume":"51","author":"Neeff","year":"2005","journal-title":"For. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1016\/j.rse.2011.04.009","article-title":"Sensitivity of SAR Data to Post-Fire Forest Regrowth in Mediterranean and Boreal Forests","volume":"115","author":"Tanase","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.rse.2008.11.004","article-title":"Use of Multitemporal SAR Data for Monitoring Vegetation Recovery of Mediterranean Burned Areas","volume":"113","author":"Minchella","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/JSTARS.2011.2176720","article-title":"Modeling Aboveground Biomass in Tropical Forests Using Multi-Frequency SAR Data\u2014A Comparison of Methods","volume":"5","author":"Englhart","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0378-1127(00)00383-2","article-title":"Forests of the Mediterranean Region: Gaps in Knowledge and Research Needs","volume":"132","author":"Oswald","year":"2000","journal-title":"For. Ecol. Manag."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bispo, P., da, C., Rodr\u00edguez-Veiga, P., Zimbres, B., do Couto de Miranda, S., Henrique Giusti Cezare, C., Fleming, S., Baldacchino, F., Louis, V., and Rains, D. (2020). Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sens., 12.","DOI":"10.3390\/rs12172685"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Alebele, Y., Zhang, X., Wang, W., Yang, G., Yao, X., Zheng, H., Zhu, Y., Cao, W., and Cheng, T. (2020). Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking. Remote Sens., 12.","DOI":"10.3390\/rs12162564"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.07.038","article-title":"The Potential of Multifrequency SAR Images for Estimating Forest Biomass in Mediterranean Areas","volume":"200","author":"Santi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_54","unstructured":"Rapetti, F., and Vittorini, S. (1995). Carta Climatica Della Toscana, Pacini Editore."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1039\/b818164k","article-title":"Quality Control Procedures in the Italian National Forest Inventory","volume":"11","author":"Gasparini","year":"2009","journal-title":"J. Environ. Monit."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Tomppo, E., Gschwantner, T., Lawrence, M., and McRoberts, R.E. (2010). Comparison of National Forest Inventories. National Forest Inventories, Springer.","DOI":"10.1007\/978-90-481-3233-1"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1198\/108571106X130548","article-title":"A Three-Phase Sampling Strategy for Large-Scale Multiresource Forest Inventories","volume":"11","author":"Fattorini","year":"2006","journal-title":"JABES"},{"key":"ref_58","unstructured":"(2020, March 06). Regione Toscana GEOScopio. Available online: www502.regione.toscana.it\/geoscopio\/cartoteca.html."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer Feedforward Networks Are Universal Approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/0167-8191(90)90081-J","article-title":"Inversion of Neural Networks by Gradient Descent","volume":"14","author":"Kindermann","year":"1990","journal-title":"Parallel Comput."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Garcia Rosa, J.L. (2016). Neural Networks Applications for the Remote Sensing of Hydrological Parameters. Artificial Neural Networks-Models and Applications, IntechOpen.","DOI":"10.5772\/61493"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"701","DOI":"10.5589\/m02-066","article-title":"Retrieving Surface Roughness and Soil Moisture from Synthetic Aperture Radar (SAR) Data Using Neural Networks","volume":"28","author":"Baghdadi","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/S0034-4257(02)00105-0","article-title":"Retrieving Soil Moisture and Agricultural Variables by Microwave Radiometry Using Neural Networks","volume":"84","author":"Ferrazzoli","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2008.08.012","article-title":"On the Relevance of Using Artificial Neural Networks for Estimating Soil Moisture Content","volume":"362","author":"Elshorbagy","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.fcr.2011.01.016","article-title":"Simulation for Response of Crop Yield to Soil Moisture and Salinity with Artificial Neural Network","volume":"121","author":"Dai","year":"2011","journal-title":"Field Crop. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/JSTARS.2020.2982993","article-title":"Remote Sensing of Forest Biomass Using GNSS Reflectometry","volume":"13","author":"Santi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_67","first-page":"61","article-title":"Application of Artificial Neural Networks for the Soil Moisture Retrieval from Active and Passive Microwave Spaceborne Sensors","volume":"48","author":"Santi","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:26:52Z","timestamp":1760160412000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,23]]},"references-count":67,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040809"],"URL":"https:\/\/doi.org\/10.3390\/rs13040809","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,23]]}}}