{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:13:55Z","timestamp":1761596035380,"version":"build-2065373602"},"reference-count":135,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"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 2018\u20132019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation\u2013evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.<\/jats:p>","DOI":"10.3390\/rs13101885","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T22:53:40Z","timestamp":1620773620000},"page":"1885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7895-1904","authenticated-orcid":false,"given":"Floris","family":"Hermanns","sequence":"first","affiliation":[{"name":"Helmholtz Center for Environmental Research\u2014UFZ, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1035-9781","authenticated-orcid":false,"given":"Felix","family":"Pohl","sequence":"additional","affiliation":[{"name":"Helmholtz Center for Environmental Research\u2014UFZ, Department of Computational Hydrosystems, Permoserstr. 15, 04318 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8665-0375","authenticated-orcid":false,"given":"Corinna","family":"Rebmann","sequence":"additional","affiliation":[{"name":"Helmholtz Center for Environmental Research\u2014UFZ, Department of Computational Hydrosystems, Permoserstr. 15, 04318 Leipzig, Germany"}]},{"given":"Gundula","family":"Schulz","sequence":"additional","affiliation":[{"name":"Helmholtz Center for Environmental Research\u2014UFZ, Department of Remote Sensing, Permoserstr. 15, 04318 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4700-5258","authenticated-orcid":false,"given":"Ulrike","family":"Werban","sequence":"additional","affiliation":[{"name":"Helmholtz Center for Environmental Research\u2014UFZ, Department of Monitoring and Exploration Technologies, Permoserstr. 15, 04318 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4490-7232","authenticated-orcid":false,"given":"Angela","family":"Lausch","sequence":"additional","affiliation":[{"name":"Helmholtz Center for Environmental Research\u2014UFZ, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany"},{"name":"Lab for Landscape Ecology, Department of Geography, Humboldt University of Berlin, Rudower Chaussee 16, 12489 Berlin, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","unstructured":"Field, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D.J., Ebi, K.L., Mastrandrea, M.D., Mach, K.J., Plattner, G.K., and Allen, S.K. (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, Cambridge University Press. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1002\/joc.5291","article-title":"Will Drought Events Become More Frequent and Severe in Europe?","volume":"38","author":"Spinoni","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1175\/BAMS-ExplainingExtremeEvents2018.1","article-title":"Explaining Extreme Events of 2018 from a Climate Perspective","volume":"101","author":"Herring","year":"2020","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.5194\/hess-21-1397-2017","article-title":"The European 2015 Drought from a Climatological Perspective","volume":"21","author":"Ionita","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-68872-9","article-title":"Increased Future Occurrences of the Exceptional 2018\u20132019 Central European Drought under Global Warming","volume":"10","author":"Hari","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"035001","DOI":"10.1088\/1748-9326\/9\/3\/035001","article-title":"A Few Extreme Events Dominate Global Interannual Variability in Gross Primary Production","volume":"9","author":"Zscheischler","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","article-title":"Hyperspectral Image Analysis Techniques for the Detection and Classification of the Early Onset of Plant Disease and Stress","volume":"13","author":"Lowe","year":"2017","journal-title":"Plant Methods"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens., 12.","DOI":"10.3390\/rs12061046"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1007\/s10531-010-9875-0","article-title":"Indicators for Biodiversity and Ecosystem Services: Towards an Improved Framework for Ecosystems Assessment","volume":"19","author":"Feld","year":"2010","journal-title":"Biodivers. Conserv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1111\/2041-210X.13025","article-title":"Understanding and Assessing Vegetation Health by in Situ Species and Remote-Sensing Approaches","volume":"9","author":"Lausch","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16155","DOI":"10.1073\/pnas.1911799116","article-title":"Opinion: To Advance Sustainable Stewardship, We Must Document Not Only Biodiversity but Geodiversity","volume":"116","author":"Schrodt","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2019.02.016","article-title":"Benefits of the Free and Open Landsat Data Policy","volume":"224","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"vzj2011.0138ra","DOI":"10.2136\/vzj2011.0138ra","article-title":"Characterization of Crop Canopies and Water Stress Related Phenomena Using Microwave Remote Sensing Methods: A Review","volume":"11","author":"Vereecken","year":"2012","journal-title":"Vadose Zone J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"E249","DOI":"10.1073\/pnas.1523397113","article-title":"Progressive Forest Canopy Water Loss during the 2012\u20132015 California Drought","volume":"113","author":"Asner","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/S0034-4257(02)00150-5","article-title":"Multi-Angular Optical Remote Sensing for Assessing Vegetation Structure and Carbon Absorption","volume":"84","author":"Chen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.rse.2016.09.017","article-title":"Fluspect-B: A Model for Leaf Fluorescence, Reflectance and Transmittance Spectra","volume":"186","author":"Vilfan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1111\/gcb.12822","article-title":"Observing Terrestrial Ecosystems and the Carbon Cycle from Space","volume":"21","author":"Schimel","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Martin, R.E., Chadwick, K.D., Brodrick, P.G., Carranza-Jimenez, L., Vaughn, N.R., and Asner, G.P. (2018). An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests. Remote Sens., 10.","DOI":"10.3390\/rs10020199"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cai, F., Tian, J., and Williams, T.K.A. (2018). Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions. Remote Sens., 10.","DOI":"10.3390\/rs10040527"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"741","DOI":"10.4314\/wsa.v35i5.49201","article-title":"Review of Commonly Used Remote Sensing and Ground-Based Technologies to Measure Plant Water Stress","volume":"35","author":"Govender","year":"2009","journal-title":"Water SA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11104-011-1051-0","article-title":"Advances in Remote Sensing of Plant Stress","volume":"354","author":"Barton","year":"2012","journal-title":"Plant Soil"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s40725-019-00096-1","article-title":"Early Diagnosis of Vegetation Health from High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned from Empirical Relationships and Radiative Transfer Modelling","volume":"5","author":"Hornero","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_23","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2742","DOI":"10.1016\/j.rse.2011.06.016","article-title":"Optimizing Spectral Indices and Chemometric Analysis of Leaf Chemical Properties Using Radiative Transfer Modeling","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2010.08.023","article-title":"The Photochemical Reflectance Index (PRI) and the Remote Sensing of Leaf, Canopy and Ecosystem Radiation Use Efficiencies: A Review and Meta-Analysis","volume":"115","author":"Garbulsky","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1071\/FP12060","article-title":"Early Drought Stress Detection in Cereals: Simplex Volume Maximisation for Hyperspectral Image Analysis","volume":"39","author":"Wahabzada","year":"2012","journal-title":"Funct. Plant Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1109\/JSTARS.2015.2396577","article-title":"Random Forests Unsupervised Classification: The Detection and Mapping ofSolanum mauritianumInfestations in Plantation Forestry Using Hyperspectral Data","volume":"8","author":"Peerbhay","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.03.016","article-title":"Detection of Early Plant Stress Responses in Hyperspectral Images","volume":"93","author":"Behmann","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ceamanos, X., and Valero, S. (2016). Processing Hyperspectral Images. Optical Remote Sensing of Land Surface, Elsevier. [1st ed.].","DOI":"10.1016\/B978-1-78548-102-4.50004-1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the Parts of Objects by Non-Negative Matrix Factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pauca, V.P., Shahnaz, F., Berry, M.W., and Plemmons, R.J. (2004, January 22\u201324). Text Mining Using Non-Negative Matrix Factorizations. Proceedings of the 2004 SIAM International Conference on Data Mining, Lake Buena Vista, FL, USA.","DOI":"10.1137\/1.9781611972740.45"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/1751-0473-8-10","article-title":"The Non-Negative Matrix Factorization Toolbox for Biological Data Mining","volume":"8","author":"Li","year":"2013","journal-title":"Source Code Biol. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1002\/ima.20276","article-title":"Investigation of Spectrally Coherent Resting-State Networks Using Non-Negative Matrix Factorization for Functional MRI Data","volume":"21","author":"Lee","year":"2011","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1142\/S0218271810017160","article-title":"Data Mining and Machine Learning in Astronomy","volume":"19","author":"Ball","year":"2010","journal-title":"Int. J. Mod. Phys. D"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/TGRS.2008.2002882","article-title":"Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing","volume":"47","author":"Jia","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"027001","DOI":"10.1117\/1.3533025","article-title":"Dimensionality Reduction, Classification, and Spectral Mixture Analysis Using Non-Negative Underapproximation","volume":"50","author":"Gillis","year":"2011","journal-title":"Opt. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Karoui, M.S., Deville, Y., Hosseini, S., Ouamri, A., and Ducrot, D. (2008, January 15\u201318). Contribution of Non-Negative Matrix Factorization to the Classification of Remote Sensing Images. Proceedings of the Image and Signal Processing for Remote Sensing XIV. International Society for Optics and Photonics, Cardiff, Wales, UK.","DOI":"10.1117\/12.799749"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TGRS.2013.2253612","article-title":"Spatial and Spectral Image Fusion Using Sparse Matrix Factorization","volume":"52","author":"Huang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1080\/01431169208904226","article-title":"Singular Value Decomposition in Multispectral Radiometry","volume":"13","author":"Danaher","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Thurau, C., Kersting, K., and Bauckhage, C. (2010, January 25\u201329). Yes We Can: Simplex Volume Maximization for Descriptive Web-Scale Matrix Factorization. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM\u201910, Toronto, ON, Canada.","DOI":"10.1145\/1871437.1871729"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1175\/2009JCLI2909.1","article-title":"A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index","volume":"23","year":"2010","journal-title":"J. Clim."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s12665-016-6327-5","article-title":"The Bode Hydrological Observatory: A Platform for Integrated, Interdisciplinary Hydro-Ecological Research within the TERENO Harz\/Central German Lowland Observatory","volume":"76","author":"Attinger","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"955","DOI":"10.2136\/vzj2010.0139","article-title":"A Network of Terrestrial Environmental Observatories in Germany","volume":"10","author":"Zacharias","year":"2011","journal-title":"Vadose Zone J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1515\/intag-2017-0044","article-title":"ICOS Eddy Covariance Flux-Station Site Setup: A Review","volume":"32","author":"Rebmann","year":"2018","journal-title":"Int. Agrophys."},{"key":"ref_45","unstructured":"Bernhofer, C., Goldberg, V., Franke, J., Surke, M., and Adam, J. (2008). Regionale Klimadiagnose f\u00fcr Sachsen-Anhalt, Abschlussbericht zum Forschungsvorhaben des Landesamtes f\u00fcr Umweltschutz Sachsen-Anhalt, Technische Universit\u00e4t Dresden. Berichte des Landesamtes f\u00fcr Umweltschutz Sachsen-Anhalt."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1002\/joc.3887","article-title":"Standardized Precipitation Evapotranspiration Index (SPEI) Revisited: Parameter Fitting, Evapotranspiration Models, Tools, Datasets and Drought Monitoring","volume":"34","author":"Reig","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"701","DOI":"10.2166\/nh.2016.076","article-title":"Evaluating the Uncertainty and Reliability of Standardized Indices","volume":"48","author":"Vergni","year":"2017","journal-title":"Hydrol. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.5194\/hess-21-1769-2017","article-title":"A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty","volume":"21","author":"Zink","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.5194\/hess-20-1117-2016","article-title":"Multiscale Evaluation of the Standardized Precipitation Index as a Groundwater Drought Indicator","volume":"20","author":"Kumar","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1080\/01431160110115834","article-title":"Geo-Atmospheric Processing of Airborne Imaging Spectrometry Data. Part 1: Parametric Orthorectification","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1080\/01431160110115834","article-title":"Geo-Atmospheric Processing of Airborne Imaging Spectrometry Data. Part 2: Atmospheric\/Topographic Correction","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","unstructured":"DWD Climate Data Center (2019). Historical Hourly Weather Station Measurements of Visibility in Germany, Deutscher Wetterdienst. Version v002."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1109\/TGRS.2007.894922","article-title":"Hyperspectral Imagery Visualization Using Double Layers","volume":"45","author":"Cai","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0022-1694(89)90060-7","article-title":"The Calibration of Frequency-Domain Electromagnetic Induction Meters and Their Possible Use in Recharge Studies","volume":"107","author":"Cook","year":"1989","journal-title":"J. Hydrol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2012.0217","article-title":"Analysis of Vegetation and Soil Patterns Using Hyperspectral Remote Sensing, EMI, and Gamma-Ray Measurements","volume":"12","author":"Lausch","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.geoderma.2018.08.001","article-title":"Large-Scale Soil Mapping Using Multi-Configuration EMI and Supervised Image Classification","volume":"335","author":"Brogi","year":"2019","journal-title":"Geoderma"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"495","DOI":"10.5194\/hess-21-495-2017","article-title":"Repeated Electromagnetic Induction Measurements for Mapping Soil Moisture at the Field Scale: Validation with Data from a Wireless Soil Moisture Monitoring Network","volume":"21","author":"Martini","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_58","unstructured":"M\u00fcller, S., Sch\u00fcler, L., Zech, A., Attinger, S., and He\u00dfe, F. (2020). GeoStat-Framework\/GSTools: V1.2.1. Zenodo."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TPAMI.2008.277","article-title":"Convex and Semi-Nonnegative Matrix Factorizations","volume":"32","author":"Ding","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/00401706.1994.10485840","article-title":"Archetypal Analysis","volume":"36","author":"Cutler","year":"1994","journal-title":"Technometrics"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1609\/aaai.v26i1.8168","article-title":"Pre-Symptomatic Prediction of Plant Drought Stress Using Dirichlet-Aggregation Regression on Hyperspectral Images","volume":"Volume 26","author":"Kersting","year":"2012","journal-title":"Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-12)"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kersting, K., Wahabzada, M., R\u00f6mer, C., Thurau, C., Ballvora, A., Rascher, U., L\u00e9on, J., Bauckhage, C., and Pl\u00fcmer, L. (2012, January 26\u201328). Simplex Distributions for Embedding Data Matrices over Time. Proceedings of the 2012 SIAM International Conference on Data Mining, Anaheim, CA, USA.","DOI":"10.1137\/1.9781611972825.26"},{"key":"ref_63","unstructured":"Kim, Y., Glenn, D.M., Park, J., Ngugi, H.K., and Lehman, B.L. (2010, January 20\u201323). Hyperspectral Image Analysis for Plant Stress Detection. Proceedings of the 2010 ASABE Annual International Meeting, Pittsburgh, PA, USA."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"El-Hendawy, S., Al-Suhaibani, N., Hassan, W., Tahir, M., and Schmidhalter, U. (2017). Hyperspectral Reflectance Sensing to Assess the Growth and Photosynthetic Properties of Wheat Cultivars Exposed to Different Irrigation Rates in an Irrigated Arid Region. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183262"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1002\/j.1537-2197.1993.tb13796.x","article-title":"Responses of Leaf Spectral Reflectance to Plant Stress","volume":"80","author":"Carter","year":"1993","journal-title":"Am. J. Bot."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Bayat, B., Van der Tol, C., and Verhoef, W. (2016). Remote Sensing of Grass Response to Drought Stress Using Spectroscopic Techniques and Canopy Reflectance Model Inversion. Remote Sens., 8.","DOI":"10.3390\/rs8070557"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3683","DOI":"10.1080\/014311697216883","article-title":"Spectral Reflectance of Dehydrating Leaves: Measurements and Modelling","volume":"18","author":"Aldakheel","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1080\/01431169308953986","article-title":"Red Edge Spectral Measurements from Sugar Maple Leaves","volume":"14","author":"Vogelmann","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","unstructured":"Minka, T. (2000). Estimating a Dirichlet Distribution, MIT. Technical Report."},{"key":"ref_70","unstructured":"Frigyik, B.A., Kapila, A., and Gupta, M.R. (2010). Introduction to the Dirichlet Distribution and Related Processes, University of Washington. Technical Report."},{"key":"ref_71","unstructured":"Thurau, C. (2021, May 11). Python Matrix Factorization Module. Available online: https:\/\/github.com\/cthurau\/pymf."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lausch, A., Erasmi, S., King, D.J., Magdon, P., and Heurich, M. (2016). Understanding Forest Health with Remote Sensing, Part I: A Review of Spectral Traits, Processes and Remote-Sensing Characteristics. Remote Sens., 8.","DOI":"10.3390\/rs8121029"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus Hippocastanum L. and Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1007\/s004420050337","article-title":"The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency across Species, Functional Types, and Nutrient Levels","volume":"112","author":"Gamon","year":"1997","journal-title":"Oecologia"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1016\/j.rse.2011.04.036","article-title":"Assessing Structural Effects on PRI for Stress Detection in Conifer Forests","volume":"115","author":"Morales","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.rse.2013.07.024","article-title":"A PRI-Based Water Stress Index Combining Structural and Chlorophyll Effects: Assessment Using Diurnal Narrow-Band Airborne Imagery and the CWSI Thermal Index","volume":"138","author":"Williams","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1641\/0006-3568(2004)054[0523:UISTSE]2.0.CO;2","article-title":"Using Imaging Spectroscopy to Study Ecosystem Processes and Properties","volume":"54","author":"Ustin","year":"2004","journal-title":"BioScience"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"4443","DOI":"10.1080\/01431160802575661","article-title":"PRI Assessment of Long-Term Changes in Carotenoids\/Chlorophyll Ratio and Short-Term Changes in de-Epoxidation State of the Xanthophyll Cycle","volume":"30","author":"Filella","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.rse.2010.10.007","article-title":"Disentangling the Relationships between Plant Pigments and the Photochemical Reflectance Index Reveals a New Approach for Remote Estimation of Carotenoid Content","volume":"115","author":"Garrity","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1007\/s00442-002-0905-9","article-title":"Seasonal Patterns of Reflectance Indices, Carotenoid Pigments and Photosynthesis of Evergreen Chaparral Species","volume":"131","author":"Stylinski","year":"2002","journal-title":"Oecologia"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1080\/01431169408954109","article-title":"Ratios of Leaf Reflectances in Narrow Wavebands as Indicators of Plant Stress","volume":"15","author":"Carter","year":"1994","journal-title":"Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"709","DOI":"10.5589\/m11-002","article-title":"A Suitable Vegetation Index for Quantifying Temporal Variation of Leaf Area Index (LAI) in Semiarid Mixed Grassland","volume":"36","author":"Li","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1111\/j.1744-7909.2007.00401.x","article-title":"Comparison of Vegetation Indices and Red-Edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data","volume":"49","author":"Liu","year":"2007","journal-title":"J. Integr. Plant Biol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.23953\/cloud.ijarsg.44","article-title":"MODIS Derived Vegetation Index for Drought Detection on the San Carlos Apache Reservation","volume":"5","author":"Wu","year":"2016","journal-title":"Int. J. Adv. Remote Sens. GIS"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/01431169608949012","article-title":"Cell Wall Elasticity and Water Index (R970 Nm\/R900 Nm) in Wheat under Different Nitrogen Availabilities","volume":"17","author":"Penuelas","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1023\/A:1007033503276","article-title":"Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves","volume":"36","author":"Inoue","year":"1999","journal-title":"Photosynthetica"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/S0034-4257(98)00013-3","article-title":"Processing of High Spectral Resolution Reflectance Data for the Retrieval of Canopy Water Content Information","volume":"65","author":"Rollin","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_90","first-page":"388","article-title":"Using Spectral Information from the NIR Water Absorption Features for the Retrieval of Canopy Water Content","volume":"10","author":"Clevers","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_92","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_93","unstructured":"Wei, T., and Simko, V. (2021, May 11). R Package \u201cCorrplot\u201d: Visualization of a Correlation Matrix. Available online: https:\/\/CRAN.R-project.org\/package=corrplot."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1080\/0266476042000214501","article-title":"Beta Regression for Modelling Rates and Proportions","volume":"31","author":"Ferrari","year":"2004","journal-title":"J. Appl. Stat."},{"key":"ref_95","first-page":"1","article-title":"Beta Regression in R","volume":"34","author":"Zeileis","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"495","DOI":"10.32614\/RJ-2016-062","article-title":"Mctest: An R Package for Detection of Collinearity among Regressors","volume":"8","author":"Imdadullah","year":"2016","journal-title":"R. J."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/j.1600-0587.2012.07348.x","article-title":"Collinearity: A Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance","volume":"36","author":"Dormann","year":"2013","journal-title":"Ecography"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1111\/j.1467-9876.2011.01033.x","article-title":"Generalized Additive Models for Location, Scale and Shape for High Dimensional Data\u2014a Flexible Approach Based on Boosting","volume":"61","author":"Mayr","year":"2012","journal-title":"J. R. Stat. Soc. Ser. C (Appl. Stat.)"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1111\/j.1467-9876.2005.00510.x","article-title":"Generalized Additive Models for Location Scale and Shape","volume":"54","author":"Stasinopoulos","year":"2005","journal-title":"J. R. Stat. Soc. Ser. C (Appl. Stat.)"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Schmid, M., Wickler, F., Maloney, K.O., Mitchell, R., Fenske, N., and Mayr, A. (2013). Boosted Beta Regression. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0061623"},{"key":"ref_102","unstructured":"Hastie, T., and Tibshirani, R. (1990). Generalized Additive Models, CRC Press."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1198\/016214503000125","article-title":"Boosting with the L 2 Loss: Regression and Classification","volume":"98","author":"Yu","year":"2003","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3414\/ME11-02-0030","article-title":"The Importance of Knowing When to Stop","volume":"51","author":"Mayr","year":"2012","journal-title":"Methods Inf. Med."},{"key":"ref_105","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"436","DOI":"10.3414\/13100122","article-title":"Discussion of \u201cthe Evolution of Boosting Algorithms\u201d and \u201cExtending Statistical Boosting\u201d","volume":"53","author":"Gertheiss","year":"2014","journal-title":"Methods Inf. Med."},{"key":"ref_107","unstructured":"Hofner, B., Mayr, A., and Schmid, M. (2014). gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework. arXiv."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0034-4257(70)80021-9","article-title":"Physical and Physiological Basis for the Reflectance of Visible and Near-Infrared Radiation from Vegetation","volume":"1","author":"Knipling","year":"1970","journal-title":"Remote Sens. Environ."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Magnusson, M., Sigurdsson, J., Armannsson, S., Ulfarsson, M., Deborah, H., and Sveinsson, J. (October, January 26). Creating RGB Images from Hyperspectral Images Using a Color Matching Function. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323397"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"15055","DOI":"10.1038\/s41598-020-72006-6","article-title":"Effects of Water Stress on Spectral Reflectance of Bermudagrass","volume":"10","author":"Caturegli","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and Biochemical Sources of Variability in Canopy Reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/S0065-2113(06)94006-1","article-title":"The Impacts of Grazing Animals on the Quality of Soils, Vegetation, and Surface Waters in Intensively Managed Grasslands","volume":"94","author":"Bilotta","year":"2007","journal-title":"Adv. Agron."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1111\/1365-2435.12390","article-title":"Challenging the Maximum Rooting Depth Paradigm in Grasslands and Savannas","volume":"29","author":"Nippert","year":"2015","journal-title":"Funct. Ecol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1007\/s10113-017-1202-9","article-title":"Generality of Relationships between Leaf Pigment Contents and Spectral Vegetation Indices in Mallorca (Spain)","volume":"17","author":"Hallik","year":"2017","journal-title":"Reg. Environ. Chang."},{"key":"ref_115","first-page":"130","article-title":"Adaptability of White Jabon (Anthocephalus Cadamba MIQ.) Seedling from 12 Populations to Drought and Waterlogging","volume":"37","author":"Sudrajat","year":"2015","journal-title":"AGRIVITA J. Agric. Sci."},{"key":"ref_116","first-page":"221","article-title":"Semi-Empirical Indices to Assess Carotenoids\/Chlorophyll a Ratio from Leaf Spectral Reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s00442-015-3233-6","article-title":"Differential Sensitivity to Regional-Scale Drought in Six Central US Grasslands","volume":"177","author":"Knapp","year":"2015","journal-title":"Oecologia"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.compag.2018.12.003","article-title":"Estimation of Nitrogen and Carbon Content from Soybean Leaf Reflectance Spectra Using Wavelet Analysis under Shade Stress","volume":"156","author":"Chen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1137\/0905052","article-title":"The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses","volume":"5","author":"Wold","year":"1984","journal-title":"SIAM J. Sci. Stat. Comput."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.wocn.2018.09.004","article-title":"Strategies for Addressing Collinearity in Multivariate Linguistic Data","volume":"71","author":"Tomaschek","year":"2018","journal-title":"J. Phon."},{"key":"ref_121","first-page":"1341","article-title":"Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination","volume":"10","author":"Tuv","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Keydan, G.P., and Merzlyak, M.N. (2006). Three-Band Model for Noninvasive Estimation of Chlorophyll, Carotenoids, and Anthocyanin Contents in Higher Plant Leaves. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL026457"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Uarrota, V.G., Stefen, D.L.V., Leolato, L.S., Gindri, D.M., and Nerling, D. (2018). Revisiting Carotenoids and Their Role in Plant Stress Responses: From Biosynthesis to Plant Signaling Mechanisms during Stress. Antioxidants and Antioxidant Enzymes in Higher Plants, Springer.","DOI":"10.1007\/978-3-319-75088-0_10"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1080\/01431161.2013.779046","article-title":"VSDI: A Visible and Shortwave Infrared Drought Index for Monitoring Soil and Vegetation Moisture Based on Optical Remote Sensing","volume":"34","author":"Zhang","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Raza, S.e.A., Smith, H.K., Clarkson, G.J.J., Taylor, G., Thompson, A.J., Clarkson, J., and Rajpoot, N.M. (2014). Automatic Detection of Regions in Spinach Canopies Responding to Soil Moisture Deficit Using Combined Visible and Thermal Imagery. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0097612"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., and Mollenhauer, H. (2018). Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sens., 10.","DOI":"10.3390\/rs10071120"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Jawad, H.M., Nordin, R., Gharghan, S.K., Jawad, A.M., and Ismail, M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors, 17.","DOI":"10.3390\/s17081781"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13007-015-0073-7","article-title":"Hyperspectral Phenotyping on the Microscopic Scale: Towards Automated Characterization of Plant-Pathogen Interactions","volume":"11","author":"Kuska","year":"2015","journal-title":"Plant Methods"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Alonso, K., Bachmann, M., Burch, K., Carmona, E., Cerra, D., De los Reyes, R., Dietrich, D., Heiden, U., H\u00f6lderlin, A., and Ickes, J. (2019). Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors, 19.","DOI":"10.3390\/s19204471"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Loizzo, R., Guarini, R., Longo, F., Scopa, T., Formaro, R., Facchinetti, C., and Varacalli, G. (2018, January 22\u201327). PRISMA: The Italian Hyperspectral Mission. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518512"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"3961","DOI":"10.5194\/bg-17-3961-2020","article-title":"Understanding the Uncertainty in Global Forest Carbon Turnover","volume":"17","author":"Pugh","year":"2020","journal-title":"Biogeosciences"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1111\/j.1469-8137.2010.03340.x","article-title":"Assessing Uncertainties in a Second-Generation Dynamic Vegetation Model Caused by Ecological Scale Limitations","volume":"187","author":"Fisher","year":"2010","journal-title":"New Phytol."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1111\/j.1469-8137.1995.tb03064.x","article-title":"Assessment of Photosynthetic Radiation-Use Efficiency with Spectral Reflectance","volume":"131","author":"Filella","year":"1995","journal-title":"New Phytol."},{"key":"ref_135","first-page":"212","article-title":"Spectranomics: Emerging Science and Conservation Opportunities at the Interface of Biodiversity and Remote Sensing","volume":"8","author":"Asner","year":"2016","journal-title":"Glob. Ecol. Conserv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1885\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:59:25Z","timestamp":1760162365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":135,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13101885"],"URL":"https:\/\/doi.org\/10.3390\/rs13101885","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,5,11]]}}}