{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:18:27Z","timestamp":1774505907554,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"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>The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m\u22122 and mean absolute error (MAE) of 0.51 m2m\u22122. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m\u22122 and MAE of 0.61 m2m\u22122) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m\u22122 and MAE of 0.30 m2m\u22122). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.<\/jats:p>","DOI":"10.3390\/rs13071348","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T10:44:01Z","timestamp":1617273841000},"page":"1348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3242-613X","authenticated-orcid":false,"given":"Mehdi","family":"Hosseini","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada"},{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1006-0018","authenticated-orcid":false,"given":"Heather","family":"McNairn","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada"},{"name":"Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4657-0706","authenticated-orcid":false,"given":"Scott","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9372-1952","authenticated-orcid":false,"given":"Laura Dingle","family":"Robertson","sequence":"additional","affiliation":[{"name":"Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3784-682X","authenticated-orcid":false,"given":"Andrew","family":"Davidson","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada"},{"name":"Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}]},{"given":"Nima","family":"Ahmadian","sequence":"additional","affiliation":[{"name":"Julius-Maximilians-Universit\u00e4t, 97070 W\u00fcrzburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6720-6108","authenticated-orcid":false,"given":"Avik","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8288-8426","authenticated-orcid":false,"given":"Erik","family":"Borg","sequence":"additional","affiliation":[{"name":"Department of National Ground Segment, German Aerospace Center (DLR), 17235 Neustrelitz, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0807-7059","authenticated-orcid":false,"given":"Christopher","family":"Conrad","sequence":"additional","affiliation":[{"name":"Department of Geoecology, Institute of Geosciences and Geography, University of Halle-Wittenberg, Von Seckendorff-Platz 4, 06120 Halle (Saale), Germany"}]},{"given":"Katarzyna","family":"Dabrowska-Zielinska","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}]},{"given":"Diego","family":"de Abelleyra","sequence":"additional","affiliation":[{"name":"Instituto de Clima y Agua, Instituto Nacional de Tecnolog\u00eda Agropecuaria (INTA), Buenos Aires 1439, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8991-7306","authenticated-orcid":false,"given":"Radoslaw","family":"Gurdak","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Cartography, 02-679 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-3815","authenticated-orcid":false,"given":"Vineet","family":"Kumar","sequence":"additional","affiliation":[{"name":"Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India"},{"name":"Department of Water Management, Delft University of Technology, 2628 CN Delft, The Netherlands"}]},{"given":"Nataliia","family":"Kussul","sequence":"additional","affiliation":[{"name":"Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, 03680 Kyiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8407-7125","authenticated-orcid":false,"given":"Dipankar","family":"Mandal","sequence":"additional","affiliation":[{"name":"Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India"}]},{"given":"Y. S.","family":"Rao","sequence":"additional","affiliation":[{"name":"Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India"}]},{"given":"Nicanor","family":"Saliendra","sequence":"additional","affiliation":[{"name":"USDA-ARS Northern Great Plains Research Laboratory, North Dakota, ND 58554, USA"}]},{"given":"Andrii","family":"Shelestov","sequence":"additional","affiliation":[{"name":"Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, 03680 Kyiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2939-8764","authenticated-orcid":false,"given":"Daniel","family":"Spengler","sequence":"additional","affiliation":[{"name":"Division of Remote Sensing, Deutsches GeoForschungsZentrum (GFZ), 14473 Helmholtz-Zentrum Potsdam, Germany"}]},{"given":"Santiago R.","family":"Ver\u00f3n","sequence":"additional","affiliation":[{"name":"Instituto de Clima y Agua, Instituto Nacional de Tecnolog\u00eda Agropecuaria (INTA), Buenos Aires 1439, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0214-5356","authenticated-orcid":false,"given":"Saeid","family":"Homayouni","sequence":"additional","affiliation":[{"name":"Institut National de la Recherche Scientifique (INRS), Center Eau Terre Environnement, Quebec, QC G1K9A9, Canada"}]},{"given":"Inbal","family":"Becker-Reshef","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"583","DOI":"10.2134\/agronj2001.933583x","article-title":"Use of remote-sensing imagery to estimate corn grain yield","volume":"93","author":"Shanahan","year":"2001","journal-title":"Agron. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.fcr.2012.08.008","article-title":"The use of satellite data for crop yield gap analysis","volume":"143","author":"Lobell","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_3","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for esti-mation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2018.12.032","article-title":"Assessment of red-edge vegetation indices for crop leaf area index estimation","volume":"222","author":"Dong","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Towers, P.C., Strever, A., and Poblete-Echeverr\u00eda, C. (2019). Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting. Remote. Sens., 11.","DOI":"10.3390\/rs11091073"},{"key":"ref_6","unstructured":"Delince, J. (2017). Detailed Crop Mapping Using Remote Sensing Data (Crop Data Layers), In Handbook on Remote Sensing for Agricultural Statistics (Chapter 4), Handbook of the Glob-al Strategy to improve Agricultural and Rural Statistics (GSARS)."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Br\u00e9da, N. (2008). Leaf Area Index. Encycl. Ecol., 2148\u20132154.","DOI":"10.1016\/B978-008045405-4.00849-1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2003.12.001","article-title":"Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery","volume":"90","author":"Wang","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.biosystemseng.2005.06.008","article-title":"Correlation between ground measured soil moisture and RADARSAT-1 derived backscattering coefficient over an agricul-tural catchment of Navarre (North of Spain)","volume":"92","author":"Verhoest","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"136","DOI":"10.5589\/m11-021","article-title":"Estimating surface-soil moisture for retrieving maize leaf-area index from SAR data","volume":"37","author":"Lambot","year":"2011","journal-title":"Can. J. Remote. Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.rse.2015.09.002","article-title":"Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data","volume":"170","author":"Hosseini","year":"2015","journal-title":"Remote. Sens. Environ."},{"key":"ref_12","first-page":"301","article-title":"Estimating the Leaf Area Index of Agricultural Crops using multi-temporal dual-polarimetric TerraSAR-X Data: A case study in North-Eastern Germany. Photogrammetrie, Fernerkundung","volume":"5","author":"Ahmadian","year":"2016","journal-title":"Geoinformation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1029\/RS013i002p00357","article-title":"Vegetation modeled as a water cloud","volume":"13","author":"Attema","year":"1978","journal-title":"Radio Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hosseini, M., McNairn, H., Mitchell, S., Davidson, A., and Dingle Robertson, L. (2019). Comparison of SAR and Optical Sensors for Biomass Estimations Over Corn Fields. Int. J. Earth Obs. Geoinf., 83.","DOI":"10.1109\/IGARSS.2018.8518998"},{"key":"ref_15","first-page":"50","article-title":"Using multi-polarization C- and L-band synthetic aperture radar to estimate wheat fields biomass and soil moisture","volume":"58","author":"Hosseini","year":"2017","journal-title":"Int. J. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"16204","DOI":"10.3390\/rs71215818","article-title":"Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model","volume":"7","author":"Waldner","year":"2015","journal-title":"Remote. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mandal, D., Hosseini, M., McNairn, H., Kumar, V., Bhattacharya, A., Rao, Y.S., Mitchell, S., Dingle Robertson, R., Davidson, A., and Dabrowska-Zielinska, K. (2019). An investigation of inversion methodologies to retrieve the Leaf Area Index of corn from C-Band backscatter. Int. J. Earth Obs. Geoinf., 82.","DOI":"10.1016\/j.jag.2019.06.003"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6822","DOI":"10.1080\/01431161.2019.1594436","article-title":"Crop Biomass Estimation using Multi-Regression Analysis and Neural Networks from Multitemporal L-band PolSAR data","volume":"40","author":"Homayouni","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Asilo, S., Nelson, A., De Bie, K., Skidmore, A., Laborte, A., Maunahan, A., and Quilang, E.J.P. (2019). Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases. Remote. Sens., 11.","DOI":"10.3390\/rs11121462"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1109\/TGRS.2014.2364913","article-title":"The soil moisture active passive validation experiment 2012 (SMAPVEX12): Pre-launch calibration and validation of the SMAP satellite","volume":"53","author":"McNairn","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69","DOI":"10.5589\/m11-023","article-title":"The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index","volume":"37","author":"Jiao","year":"2011","journal-title":"Can. J. Remote. Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/j.agrformet.2008.02.014","article-title":"Intercomparison and sensitivity analysis of leaf area index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands","volume":"148","author":"Garrigues","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/S0034-4257(96)00157-5","article-title":"Monitoring local environmental conditions with SIR-C\/X-SAR","volume":"59","author":"Pultz","year":"1997","journal-title":"Remote. Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"180132","DOI":"10.2136\/vzj2018.07.0132","article-title":"Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site","volume":"17","author":"Bhuiyan","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.jhydrol.2013.05.021","article-title":"Calibration Proce-dures for Surface Soil Moisture Measurements during Soil Moisture Active Passive Experiment 2012 (SMAPVEX-12)","volume":"498","author":"Rowlandson","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_27","unstructured":"Potter, E., Wood, J., and Nicholl, C. (1996). SunScan Canopy Analysis System: Users Manual, Delta-T Devices."},{"key":"ref_28","first-page":"102052","article-title":"Crop characterization using an improved scattering power decomposition technique for compact polarimetric SAR data","volume":"88","author":"Kumar","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","first-page":"102059","article-title":"In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data","volume":"88","author":"Dey","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111561","DOI":"10.1016\/j.rse.2019.111561","article-title":"As-sessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data","volume":"237","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","unstructured":"Mandal, D., Kumar, V., Rao, Y., Bhattacharya, A., and Ramana, K. (2019). Experimental Field Campaigns at Vijayawada Test Site, Technical Report MRS2019TR02; Microwave Remote Sensing Lab."},{"key":"ref_32","unstructured":"Dabrowska-Zielinska, K. (2021, February 01). LPVP\u2014Land Products Validation and Characterisation in Support to Proba-V, S-2 and S-3 Mission\u2014ESRIN Contract No. 4000116440\/16\/I-SBo. Available online: https:\/\/lpvp.eu\/."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., and Kostetsky, O. (2015, January 26\u201331). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326123"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1080\/01431161.2016.1194545","article-title":"Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity","volume":"37","author":"Waldner","year":"2016","journal-title":"Int. J. Remote. Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2006.872529","article-title":"Validation of global moderate-resolution LAI products: A framework pro-posed within the CEOS land product validation subgroup","volume":"44","author":"Morisette","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TGE.1979.294626","article-title":"Microwave backscatter dependence on surface roughness soil moisture and soil texture. Part I\u2014Bare soil","volume":"17","author":"Ulaby","year":"1978","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: Fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/LGRS.2006.871748","article-title":"Robust support vector regression for biophysical var-iable estimation from remotely sensed images","volume":"3","author":"Bruzzone","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","unstructured":"Miller, S.J. (2021, February 01). The Method of Least Squares, in Brown University. 2006. Available online: https:\/\/web.williams.edu\/Mathematics\/sjmiller\/public_html\/BrownClasses\/54\/handouts\/MethodLeastSquares.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1137\/0111030","article-title":"An Algorithm for Least-Squares Estimation of Nonlinear Parameters","volume":"11","author":"Marquardt","year":"1963","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1109\/JSTARS.2014.2322311","article-title":"RADARSAT-2 Polarimetric SAR Response to Crop Biomass for Agricultural Production Monitoring","volume":"7","author":"Wiseman","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hosseini, M., Kerner, H., Sahajpal, R., Puricelli, E., Lu, Y.-H., Lawal, A., Humber, M., Mitkish, M., Meyer, S., and Becker-Reshef, I. (2020). Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar. Remote Sens., 12.","DOI":"10.3390\/rs12233878"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2018.10.012","article-title":"Estimating canola phenology using synthetic aperture ra-dar","volume":"219","author":"McNairn","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","year":"2003","journal-title":"J. Exp. Bot."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1348\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:25:49Z","timestamp":1760361949000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1348"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,1]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071348"],"URL":"https:\/\/doi.org\/10.3390\/rs13071348","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,1]]}}}