{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T14:58:25Z","timestamp":1776265105770,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kansas Agricultural Experiment Station","award":["25-050-J"],"award-info":[{"award-number":["25-050-J"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite data to estimate aboveground biomass in alfalfa (Medicago sativa L.) at farm scale. For this purpose, samples were collected throughout the 2022 growing season on different mowing dates at three fields in Kansas, USA. The satellite data employed comprised four sources: Sentinel-2, PlanetScope, Planet Fusion, and Biomass Proxy. A grid of hyperparameters was created to establish different combinations and select the best coefficients. The permutation feature importance technique revealed that the Planet\u2019s PlanetScope near-infrared (NIR) band and the Biomass Proxy product were the predictive features with the highest contribution to the biomass prediction model\u2019s. A Bayesian Additive Regression Tree (BART) was applied to explore its ability to build a predictive model. Its performance was assessed via statistical metrics (r2: 0.61; RMSE: 0.29 kg.m\u22122). Additionally, uncertainty quantifications were proposed with this framework to assess the range of error in the predictions. In conclusion, this integration in a nonparametric approach achieved a useful predicting tool with the potential to optimize farmers\u2019 management decisions.<\/jats:p>","DOI":"10.3390\/rs16183379","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T10:33:01Z","timestamp":1726050781000},"page":"3379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing Alfalfa Biomass Prediction: An Innovative Framework Using Remote Sensing Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7787-3049","authenticated-orcid":false,"given":"Matias F.","family":"Lucero","sequence":"first","affiliation":[{"name":"Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5171-2516","authenticated-orcid":false,"given":"Carlos M.","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Ana J. P.","family":"Carcedo","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA"}]},{"given":"Ariel","family":"Zajdband","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8075-9003","authenticated-orcid":false,"given":"Pierre C.","family":"Guillevic","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3604-0747","authenticated-orcid":false,"given":"Rasmus","family":"Houborg","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}]},{"given":"Kevin","family":"Hamilton","sequence":"additional","affiliation":[{"name":"AGCO Corporation, Duluth, GA 30096, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9619-5129","authenticated-orcid":false,"given":"Ignacio A.","family":"Ciampitti","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.agwat.2018.02.003","article-title":"Soil properties and agro-physiological responses of alfalfa (Medicago sativa L.) irrigated by treated domestic wastewater","volume":"202","author":"Elfanssi","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106525","DOI":"10.1016\/j.agwat.2020.106525","article-title":"Effect of partial root-zone drying irrigation (PRDI) on the biomass, water productivity and carbon, nitrogen and phosphorus allocations in different organs of alfalfa","volume":"243","author":"Zhang","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.plantsci.2017.01.018","article-title":"MicroRNA156 improves drought stress tolerance in alfalfa (Medicago sativa) by silencing SPL13","volume":"258","author":"Arshad","year":"2017","journal-title":"Plant Sci."},{"key":"ref_4","first-page":"487","article-title":"Determination of yield and quality characteristics of alfalfa (Medicago sativa L.) varieties grown in different locations","volume":"12","author":"Avci","year":"2013","journal-title":"J. Anim. Vet. Adv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.fcr.2018.01.017","article-title":"Estimating alfalfa yield and nutritive value using remote sensing and air temperature","volume":"222","author":"Noland","year":"2018","journal-title":"Field Crops Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"892","DOI":"10.2135\/cropsci2010.04.0216","article-title":"Management-intensive rotational grazing enhances forage production and quality of subhumid cool-season pastures","volume":"51","author":"Oates","year":"2011","journal-title":"Crop Sci."},{"key":"ref_7","unstructured":"Caddel, J., Stritzke, J., Berberet, R., Bolin, P., Huhnke, R., Johnson, G., and Cuperus, G. (2024, August 28). Alfalfa Production Guide for the Southern Great Plains. 2001, 71, E-826. Available online: https:\/\/extension.okstate.edu\/fact-sheets\/print-publications\/e\/e-826-2018.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/pbi.12841","article-title":"From model to crop: Functional characterization of SPL 8 in M. truncatula led to genetic improvement of biomass yield and abiotic stress tolerance in alfalfa","volume":"16","author":"Gou","year":"2018","journal-title":"Plant Biotechnol. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1111\/pbi.13258","article-title":"Improvement of alfalfa forage quality and management through the down-regulation of Ms FT a1","volume":"18","author":"Lorenzo","year":"2020","journal-title":"Plant Biotechnol. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/bs.agron.2021.06.002","article-title":"Drought stress responses in non-transgenic and transgenic alfalfa\u2014Current status and future research directions","volume":"Volume 170","author":"Diatta","year":"2021","journal-title":"Advances in Agronomy"},{"key":"ref_11","first-page":"265","article-title":"Dry matter yield and plant density of alfalfa as affected by cutting schedule and seeding rate","volume":"Volume 23","author":"Katanski","year":"2018","journal-title":"Proceedings of the 27th General Meeting of the European Grassland Federation \u201cSustainable Meat and Milk Production from Grasslands\u201d"},{"key":"ref_12","first-page":"43","article-title":"Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data","volume":"43","author":"Ramoelo","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Reinermann, S., Asam, S., and Kuenzer, C. (2020). Remote Sensing of Grassland Production and Management\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12121949"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100018","DOI":"10.1016\/j.srs.2021.100018","article-title":"An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping","volume":"3","author":"Song","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108294","DOI":"10.1016\/j.agwat.2023.108294","article-title":"Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model","volume":"282","author":"Wang","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"71","DOI":"10.3390\/ai2010006","article-title":"Using Machine Learning and Feature Selection for Alfalfa Yield Prediction","volume":"2","author":"Whitmire","year":"2021","journal-title":"AI"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.15625\/0866-7187\/41\/2\/13690","article-title":"Estimation of Above Ground Biomass Using Support Vector Machines and ALOS\/PALSAR data","volume":"41","author":"Sivasankar","year":"2019","journal-title":"Vietnam J. Earth Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108096","DOI":"10.1016\/j.compag.2023.108096","article-title":"On-farm soybean seed protein and oil prediction using satellite data","volume":"212","author":"Hernandez","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, J., Quackenbush, L.J., Volk, T.A., and Im, J. (2020). Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions. Remote Sens., 12.","DOI":"10.3390\/rs12182934"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8379391","DOI":"10.34133\/2021\/8379391","article-title":"Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities","volume":"2021","author":"Gao","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.renene.2021.05.099","article-title":"Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas","volume":"177","author":"Wu","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhang, Z., and Wang, Y. (2022). Forage yield, canopy characteristics, and radiation interception of ten alfalfa varieties in an arid environment. Plants, 11.","DOI":"10.3390\/plants11091112"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1146\/annurev.soc.34.040507.134631","article-title":"Nonparametric methods for modeling nonlinearity in regression analysis","volume":"35","author":"Andersen","year":"2009","journal-title":"Annu. Rev. Sociol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"82","DOI":"10.3390\/geographies3010005","article-title":"Comparison between parametric and non-parametric supervised land cover classifications of sentinel-2 msi and landsat-8 oli data","volume":"3","author":"Mancino","year":"2023","journal-title":"Geographies"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1214\/09-AOAS285","article-title":"BART: Bayesian additive regression trees","volume":"4","author":"Chipman","year":"2010","journal-title":"Ann. Appl. Stat."},{"key":"ref_27","first-page":"57","article-title":"Bayesian methods for updating crop-model predictions, applications for predicting biomass and grain protein content","volume":"3","author":"Makowski","year":"2004","journal-title":"Frontis"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108668","DOI":"10.1016\/j.agrformet.2021.108668","article-title":"Unraveling uncertainty drivers of the maize yield response to nitrogen: A Bayesian and machine learning approach","volume":"311","author":"Correndo","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","unstructured":"Hamilton, V.L., Kansas Agricultural Experiment Station, and United States (2024, February 10). Soil Survey, Wichita County, Kansas. U.S. Dept. of Agriculture, Soil Conservation Service. Available online: https:\/\/catalog.hathitrust.org\/Record\/101740228."},{"key":"ref_30","unstructured":"(2022, December 20). Kansas Mesonet. Available online: https:\/\/mesonet.k-state.edu\/."},{"key":"ref_31","unstructured":"C\u00f3rdoba, M., Vega, A., and Balzarini, M. (2014). Protocolo de An\u00e1lisis para la Delimitaci\u00f3n de Zonas de Manejo Intralote, Conference: XIX Reuni\u00f3n Cient\u00edfica del GABAt."},{"key":"ref_32","unstructured":"(2022, December 15). Planet Fusion Monitoring Technical Specifications. Available online: https:\/\/assets.planet.com\/docs\/Planet_fusion_specification_March_2021.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112586","DOI":"10.1016\/j.rse.2021.112586","article-title":"A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery","volume":"264","author":"Roy","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Burger, R., Aouizerats, B., Den Besten, N., Guillevic, P., Catarino, F., Van Der Horst, T., Jackson, D., Koopmans, R., Ridderikhoff, M., and Robson, G. (2024). The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2. Remote Sens., 16.","DOI":"10.3390\/rs16050835"},{"key":"ref_35","unstructured":"Gatti, A., and Bertolini, A. (2018). Sentinel-2 Products Specification Document, Thales Alenia Space."},{"key":"ref_36","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.21105\/joss.02272","article-title":"rgee: An R package for interacting with Google Earth Engine","volume":"5","author":"Aybar","year":"2020","journal-title":"J. Open Source Softw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v070.i04","article-title":"bartMachine: Machine Learning with Bayesian Additive Regression Trees","volume":"70","author":"Kapelner","year":"2016","journal-title":"J. Stat. Softw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Debeer, D., and Strobl, C. (2020). Conditional permutation importance revisited. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-03622-2"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103","DOI":"10.15446\/rce.v43n1.80000","article-title":"Nested and Repeated Cross Validation for Classification Model with High-Dimensional Data","volume":"43","author":"Zhong","year":"2020","journal-title":"Rev. Colomb. Estad."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.5194\/gmd-15-3519-2022","article-title":"Nested leave-two-out cross-validation for the optimal crop yield model selection","volume":"15","author":"Dinh","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2016.09.011","article-title":"Correlation confidence limits for unevenly sampled data","volume":"104","author":"Roberts","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.5194\/cp-12-1215-2016","article-title":"Technical note: Estimating unbiased transfer-function performances in spatially structured environments","volume":"12","author":"Trachsel","year":"2016","journal-title":"Clim. Past"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mazzara, M., Bruel, J.-M., Meyer, B., and Petrenko, A. (2019, January 15\u201317). Software Technology: Methods and Tools. Proceedings of the 51st International Conference, TOOLS 2019, Innopolis, Russia.","DOI":"10.1007\/978-3-030-29852-4"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1016\/j.ecolmodel.2011.07.003","article-title":"Adaptive resource management and the value of information","volume":"222","author":"Williams","year":"2011","journal-title":"Ecol. Model."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"025002","DOI":"10.1088\/2515-7620\/ab67f0","article-title":"An empirical, Bayesian approach to modelling crop yield: Maize in USA","volume":"2","author":"Shirley","year":"2020","journal-title":"Environ. Res. Commun."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.isprsjprs.2023.03.010","article-title":"Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects","volume":"198","author":"Mutanga","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1016\/j.rse.2010.06.001","article-title":"A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing","volume":"114","author":"Duveiller","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"9034","DOI":"10.3390\/rs6099034","article-title":"Defining the spatial resolution requirements for crop identification using optical remote sensing","volume":"6","author":"Duveiller","year":"2014","journal-title":"Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tedesco, D., Nieto, L., Hern\u00e1ndez, C., Rybecky, J.F., Min, D., Sharda, A., and Ciampitti, I.A. (2022). Remote sensing on alfalfa as an approach to optimize production outcomes: A review of evidence and directions for future assessments. Remote Sens., 14.","DOI":"10.3390\/rs14194940"},{"key":"ref_53","first-page":"171","article-title":"Identification and mapping of soybean and maize crops based on Sentinel-2 data","volume":"13","author":"She","year":"2020","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar Remote Sensing of Agricultural Canopies: A Review","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1080\/03610918.2020.1850790","article-title":"Nested cross-validation with ensemble feature selection and classification model for high-dimensional biological data","volume":"52","author":"Zhong","year":"2023","journal-title":"Commun. Stat.\u2014Simul. Comput."},{"key":"ref_56","first-page":"100657","article-title":"Alfalfa yield estimation based on time series of Landsat 8 and PROBA-V images: An investigation of machine learning techniques and spectral-temporal features","volume":"25","author":"Azadbakht","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, R., Zhang, M., Wang, X., Yan, Y., Sun, X., and Xu, D. (2022). A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data. Agronomy, 13.","DOI":"10.3390\/agronomy13102597"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e20392","DOI":"10.1002\/agg2.20392","article-title":"Estimating fall-harvested alfalfa (Medicago sativa L.) yield using unmanned aerial vehicle\u2013based multispectral and thermal images in southern California","volume":"6","author":"Sapkota","year":"2023","journal-title":"Agrosystems Geosci. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.foreco.2014.06.026","article-title":"A critical review of forest biomass estimation models, common mistakes and corrective measures","volume":"329","author":"Sileshi","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.rama.2017.02.004","article-title":"Integrating Remotely Sensed Imagery and Existing Multiscale Field Data to Derive Rangeland Indicators: Application of Bayesian Additive Regression Trees","volume":"70","author":"McCord","year":"2017","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Habyarimana, E., Piccard, I., Zinke-Wehlmann, C., De Franceschi, P., Catellani, M., and Dall\u2019Agata, M. (2019). Early within-season yield prediction and disease detection using sentinel satellite imageries and machine learning technologies in biomass sorghum. Software Technology: Methods and Tools: 51st International Conference, TOOLS, Innopolis, Russia, Proceedings 51, Springer International Publishing.","DOI":"10.1007\/978-3-030-29852-4_19"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"112408","DOI":"10.1016\/j.rse.2021.112408","article-title":"Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach","volume":"259","author":"Ma","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1146\/annurev-statistics-031219-041110","article-title":"Bayesian Additive Regression Trees: A Review and Look Forward","volume":"7","author":"Hill","year":"2020","journal-title":"Annu. Rev. Stat. Its Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/ai4010001","article-title":"Data Synthesis for Alfalfa Biomass Yield Estimation","volume":"4","author":"Vance","year":"2022","journal-title":"AI"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3379\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:54:04Z","timestamp":1760111644000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":64,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183379"],"URL":"https:\/\/doi.org\/10.3390\/rs16183379","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,11]]}}}