{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:20:15Z","timestamp":1769577615781,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T00:00:00Z","timestamp":1611446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004952","name":"Minnesota Department of Agriculture","doi-asserted-by":"publisher","award":["AFREC R2018-25, R2019-20 and R2020-32"],"award-info":[{"award-number":["AFREC R2018-25, R2019-20 and R2020-32"]}],"id":[{"id":"10.13039\/100004952","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016705","name":"Minnesota Soybean Research and Promotion Council","doi-asserted-by":"publisher","award":["project 00071830"],"award-info":[{"award-number":["project 00071830"]}],"id":[{"id":"10.13039\/100016705","id-type":"DOI","asserted-by":"publisher"}]},{"name":"USDA National Institute of Food and Agriculture","award":["State project 1016571"],"award-info":[{"award-number":["State project 1016571"]}]},{"name":"Startup Fund","award":["Yuxin Miao"],"award-info":[{"award-number":["Yuxin Miao"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha\u22121 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +\/\u22121 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (\u0394Temp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.<\/jats:p>","DOI":"10.3390\/rs13030401","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T09:59:40Z","timestamp":1611568780000},"page":"401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System"],"prefix":"10.3390","volume":"13","author":[{"given":"Cadan","family":"Cummings","sequence":"first","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, Saint Paul, MN 55108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8419-6511","authenticated-orcid":false,"given":"Yuxin","family":"Miao","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, Saint Paul, MN 55108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel Dias","family":"Paiao","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, Saint Paul, MN 55108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujiang","family":"Kang","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, Saint Paul, MN 55108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9539-0050","authenticated-orcid":false,"given":"Fabi\u00e1n G.","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, Saint Paul, MN 55108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1126\/science.1183899","article-title":"Precision Agriculture and Food Security","volume":"327","author":"Gebbers","year":"2010","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.fcr.2008.06.013","article-title":"Assessment of Some Major Yield-Limiting Factors on Maize Production in a Humid Temperate Environment","volume":"110","author":"Subedi","year":"2009","journal-title":"Field Crops Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.2134\/jeq2000.00472425002900040009x","article-title":"Crop Management and Corn Nitrogen Rate Effects on Nitrate Leaching","volume":"29","author":"Andraski","year":"2000","journal-title":"J. Environ. Qual."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"134","DOI":"10.2134\/agronj2009.0021","article-title":"On-Farm Assessment of the Amount and Timing of Nitrogen Fertilizer on Ammonia Volatilization","volume":"102","author":"Ma","year":"2010","journal-title":"Agron. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., van der Wal, T., Soto, I., G\u00f3mez-Barbero, M., Barnes, A., and Eory, V. (2017). Precision Agriculture Technologies Positively Contributing to Ghg Emissions Mitigation, Farm Productivity and Economics. Sustainability, 9.","DOI":"10.3390\/su9081339"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s13593-012-0111-z","article-title":"Precision Nitrogen Management of Wheat. A Review","volume":"33","author":"Diacono","year":"2013","journal-title":"Agron. Sustain. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.fcr.2017.09.033","article-title":"Improving Nitrogen Use Efficiency with Minimal Environmental Risks Using an Active Canopy Sensor in a Wheat-Maize Cropping System","volume":"214","author":"Cao","year":"2017","journal-title":"Field Crops Res."},{"key":"ref_8","first-page":"132","article-title":"Agroecosystems, Nitrogen-Use Efficiency, and Nitrogen Management","volume":"79","author":"Cassman","year":"2006","journal-title":"Biogeochemistry"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.eja.2008.05.007","article-title":"Estimating the Nitrogen Nutrition Index Using Spectral Canopy Reflectance Measurements","volume":"29","author":"Mistele","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_10","unstructured":"Silva, J.A., and Uchida, R. (2000). Essential Nutrients for Plant Growth. Plant Nutrient Management in Hawaii\u2019s Soils: Approaches for Tropical and Subtropical Agriculture, College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1023\/A:1004783431055","article-title":"Relationships between Dynamics of Nitrogen Uptake and Dry Matter Accumulation in Maize Crops. Determination of Critical N Concentration","volume":"216","author":"Lemaire","year":"1999","journal-title":"Plant Soil"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6549","DOI":"10.3390\/rs6076549","article-title":"Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery","volume":"6","author":"Cilia","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xia, T., Miao, Y., Wu, D., Shao, H., Khosla, R., and Mi, G. (2016). Active Optical Sensing of Spring Maize for In-Season Diagnosis of Nitrogen Status Based on Nitrogen Nutrition Index. Remote Sens., 8.","DOI":"10.3390\/rs8070605"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.2134\/agronj2008.0016","article-title":"Chlorophyll Measurements and Nitrogen Nutrition Index for the Evaluation of Corn Nitrogen Status","volume":"100","author":"Ziadi","year":"2008","journal-title":"Agron. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.eja.2017.12.006","article-title":"Exploring New Spectral Bands and Vegetation Indices for Estimating Nitrogen Nutrition Index of Summer Maize","volume":"93","author":"Zhao","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"147","DOI":"10.4081\/ija.2009.4.147","article-title":"Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture","volume":"4","author":"Basso","year":"2009","journal-title":"Ital. J. Agron."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Miao, Y., Mulla, D.J., Randall, G.W., Vetsch, J.A., and Vintila, R. (2009). Combining Chlorophyll Meter Readings and High Spatial Resolution Remote Sensing Images for In-Season Site-Specific Nitrogen Management of Corn. Precis. Agric.","DOI":"10.1007\/s11119-008-9091-z"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1002\/agj2.20248","article-title":"Ground-Based Optical Canopy Sensing Technologies for Corn\u2013Nitrogen Management in the Upper Midwest","volume":"112","author":"Paiao","year":"2020","journal-title":"Agron. J."},{"key":"ref_19","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"Volume 351","author":"Rouse","year":"1974","journal-title":"Proceedings of the Third Earth Resources Technology Satellite\u20141 Symposium"},{"key":"ref_20","unstructured":"Robert, P.C., Rust, R.H., and Larson, W.E. (2000, January 16\u201319). Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture, Madison, WI, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"723","DOI":"10.2135\/cropsci2000.403723x","article-title":"Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies","volume":"40","author":"Serrano","year":"2000","journal-title":"Crop Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.2134\/agronj2019.04.0309","article-title":"An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study","volume":"111","author":"Alzaben","year":"2019","journal-title":"Agron. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10705-016-9810-1","article-title":"Corn and Soybean\u2019s Season-Long in-Situ Nitrogen Mineralization in Drained and Undrained Soils","volume":"107","author":"Fabrizzi","year":"2017","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_24","unstructured":"Holland, K.H., and Schepers, J.S. (2011, January 16\u201319). Active Proximal Sensing: Review of Waveband Selection, Vegetation Indices, Scientific Trump Cards, Etc. Proceedings of the ASA CSSA SSSA 2011 International Annual Meetings, San Antonio, TX, USA."},{"key":"ref_25","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1094\/CM-2009-1211-01-RS","article-title":"Assessing Nitrogen Status of Dryland Wheat Using the Canopy Chlorophyll Content Index","volume":"8","author":"Long","year":"2009","journal-title":"Crop Manag."},{"key":"ref_28","unstructured":"Holland Scientific (2016). Crop Circle Phenom User\u2019s Guide, Holland Scientific."},{"key":"ref_29","unstructured":"Horneck, D.A., and Miller, R.O. (1998). Determination of total nitrogen in plant tissue. Handbook of Reference Methods for Plant Analysis, CRC Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_31","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System Tianqi. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.ecolmodel.2019.06.002","article-title":"Hyperparameter Tuning and Performance Assessment of Statistical and Machine-Learning Algorithms Using Spatial Data","volume":"406","author":"Schratz","year":"2019","journal-title":"Ecol. Model."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"10646","DOI":"10.3390\/rs70810646","article-title":"Satellite Remote Sensing-Based in-Season Diagnosis of Rice Nitrogen Status in Northeast China","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lu, J., Miao, Y., Shi, W., Li, J., and Yuan, F. (2017). Evaluating Different Approaches to Non-Destructive Nitrogen Status Diagnosis of Rice Using Portable RapidSCAN Active Canopy Sensor. Sci. Rep.","DOI":"10.1038\/s41598-017-14597-1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"530","DOI":"10.2134\/agronj2006.0135","article-title":"By-Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height","volume":"99","author":"Freeman","year":"2007","journal-title":"Agron. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, X., Miao, Y., Guan, Y., Xia, T., Lu, J., and Mulla, D.J. (2016, January 18\u201320). An evaluation of two active sensor systems for non-destructive estimation of spring maize biomass. Proceedings of the Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2016), Tianjin, China.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577610"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1029\/WR017i004p01133","article-title":"Canopy Temperature as a Crop Water Stress Indicator","volume":"17","author":"Jackson","year":"1981","journal-title":"Water Resour. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.agwat.2015.03.023","article-title":"Comparison of Canopy Temperature-Based Water Stress Indices for Maize","volume":"156","author":"DeJonge","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1017\/S0021859610000018","article-title":"The Impact of Relative Humidity, Genotypes and Fertilizer Application Rates on Panicle, Leaf Temperature, Fertility and Seed Setting of Rice","volume":"148","author":"Yan","year":"2010","journal-title":"J. Agric. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/BF00189456","article-title":"Canopy-Air Temperature of Crops Grown under Different Irrigation Regimes in a Temperate Humid Climate","volume":"11","author":"Jensen","year":"1990","journal-title":"Irrig. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.2134\/agronj2011.0124","article-title":"Use of the Canopy Chlorophyl Content Index (CCCI) for Remote Estimation of Wheat Nitrogen Content in Rainfed Environments","volume":"103","author":"Cammarano","year":"2011","journal-title":"Agron. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.fcr.2012.06.003","article-title":"Rapid Estimation of Canopy Nitrogen of Cereal Crops at Paddock Scale Using a Canopy Chlorophyll Content Index","volume":"134","author":"Perry","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.fcr.2013.12.018","article-title":"Improving Estimation of Summer Maize Nitrogen Status with Red Edge-Based Spectral Vegetation Indices","volume":"157","author":"Li","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_47","first-page":"1279","article-title":"Planar Domain Indices: A Method for Measuring a Quality of a Single Component in Two-Component Pixels","volume":"3","author":"Clarke","year":"2001","journal-title":"Int. Geosci. Remote Sens. Symp."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fcr.2010.01.010","article-title":"Measuring and Predicting Canopy Nitrogen Nutrition in Wheat Using a Spectral Index-The Canopy Chlorophyll Content Index (CCCI)","volume":"116","author":"Fitzgerald","year":"2010","journal-title":"Field Crops Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1007\/s11119-016-9431-3","article-title":"Evaluation of Mid-Season Sensor Based Nitrogen Fertilizer Recommendations for Winter Wheat Using Different Estimates of Yield Potential","volume":"17","author":"Bushong","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.2134\/agronj2017.12.0733","article-title":"Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information","volume":"110","author":"Bean","year":"2018","journal-title":"Agron. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"104872","DOI":"10.1016\/j.compag.2019.104872","article-title":"Statistical and Machine Learning Methods Evaluated for Incorporating Soil and Weather into Corn Nitrogen Recommendations","volume":"164","author":"Ransom","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"14939","DOI":"10.3390\/rs71114939","article-title":"Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration","volume":"7","author":"Yao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation in Precision Agriculture: A Review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zheng, H., Li, W., Jiang, J., Liu, Y., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Zhang, Y., and Yao, X. (2018). A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle. Remote Sens., 10.","DOI":"10.3390\/rs10122026"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zha, H., Miao, Y., Wang, T., Li, Y., Zhang, J., and Sun, W. (2020). Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12020215"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/401\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:14:47Z","timestamp":1760159687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,24]]},"references-count":55,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030401"],"URL":"https:\/\/doi.org\/10.3390\/rs13030401","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,24]]}}}