{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:55:51Z","timestamp":1773809751587,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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>Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here, we used nine bands from multispectral high-to-medium resolution (10\u201360 m) satellite data (Sentinel-2) and machine learning to predict local vegetation plot characteristics over a broad area (approx. 30,000 km2) in terms of plants\u2019 preferences for soil moisture, soil fertility, and pH, mirroring the levels of the corresponding actual soil factors. These factors are believed to be among the most important for local plant community composition. Our results showed that there are clear links between the Sentinel-2 data and plants\u2019 abiotic soil preferences, and using solely satellite data we achieved predictive powers between 26 and 59%, improving to around 70% when habitat information was included as a predictor. This shows that plants\u2019 abiotic soil preferences can be detected quite well from space, but also that retrieving soil characteristics using satellites is complicated and that perfect detection of soil conditions using remote sensing\u2014if at all possible\u2014needs further methodological and data development.<\/jats:p>","DOI":"10.3390\/rs16163094","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T04:26:57Z","timestamp":1724300817000},"page":"3094","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extents"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8591-7149","authenticated-orcid":false,"given":"Jesper Erenskjold","family":"Moeslund","sequence":"first","affiliation":[{"name":"Department of Ecoscience, Aarhus University, DK-8000 Aarhus, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3932-4312","authenticated-orcid":false,"given":"Christian Fr\u00f8lund","family":"Damgaard","sequence":"additional","affiliation":[{"name":"Department of Ecoscience, Aarhus University, DK-8000 Aarhus, Denmark"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"573","DOI":"10.2307\/3246588","article-title":"Gradient Analysis of Dry Grassland Vegetation in Denmark","volume":"11","author":"Bruun","year":"2000","journal-title":"J. Veg. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Brunbjerg, A.K., Bruun, H.H., Br\u00f8ndum, L., Classen, A.T., Fog, K., Fr\u00f8slev, T.G., Goldberg, I., Hansen, M.D.D., H\u00f8ye, T.T., and L\u00e6ss\u00f8e, T. (2019). A Systematic Survey of Regional Multitaxon Biodiversity: Evaluating Strategies and Coverage. BMC Ecol., 19.","DOI":"10.1186\/s12898-019-0260-x"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1002\/rse2.314","article-title":"Using Airborne Lidar to Characterize North European Terrestrial High-Dark-Diversity Habitats","volume":"9","author":"Moeslund","year":"2023","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/avsc.12191","article-title":"European Vegetation Archive (EVA): An Integrated Database of European Vegetation Plots","volume":"19","author":"Hennekens","year":"2016","journal-title":"Appl. Veg. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1038\/s41467-023-36240-6","article-title":"Climate-Trait Relationships Exhibit Strong Habitat Specificity in Plant Communities Across Europe","volume":"14","author":"Kambach","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1111\/geb.13603","article-title":"Disturbance Indicator Values for European Plants","volume":"32","author":"Midolo","year":"2023","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1078\/1439-1791-00185","article-title":"Species Indicator Values as an Important Tool in Applied Plant Ecology\u2014A Review","volume":"4","author":"Diekmann","year":"2003","journal-title":"Basic Appl. Ecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"375","DOI":"10.7809\/b-e.00165","article-title":"NATURDATA.DK\u2014Danish Monitoring Program of Vegetation and Chemical Plant and Soil Data from Non-Forested Terrestrial Habitat Types","volume":"4","author":"Nielsen","year":"2012","journal-title":"Biodivers. Ecol."},{"key":"ref_10","unstructured":"Fredshavn, J.R., Ejrn\u00e6s, R., and Nygaard, B. (2010). Teknisk Anvisning for Kortl\u00e6gning af Terrestriske Naturtyper. TA-N3, Version 1.04, Danish Centre for Environment and Energy."},{"key":"ref_11","unstructured":"Fredshavn, J.R., Nielsen, K.E., Ejrn\u00e6s, R., and Nygaard, B. (2018). Overv\u00e5gning af Terrestriske Naturtyper. Version 4.1, Danish Centre for Environment and Energy."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1111\/1365-2745.13897","article-title":"Remote Sensing of Phenology: Towards the Comprehensive Indicators of Plant Community Dynamics from Species to Regional Scales","volume":"110","author":"Dronova","year":"2022","journal-title":"J. Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"113734","DOI":"10.1016\/j.rse.2023.113734","article-title":"Uncovering the Hidden: Leveraging Sub-Pixel Spectral Diversity to Estimate Plant Diversity from Space","volume":"296","author":"Rossi","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_14","unstructured":"European Space Agency (2015). Sentinel-2 User Handbook, European Space Agency."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112684","DOI":"10.1016\/j.rse.2021.112684","article-title":"Explaining Discrepancies between Spectral and in-Situ Plant Diversity in Multispectral Satellite Earth Observation","volume":"265","author":"Hauser","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.rse.2018.11.016","article-title":"Mapping Foliar Functional Traits and Their Uncertainties Across Three Years in a Grassland Experiment","volume":"221","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1111\/jbi.14344","article-title":"Soil Chemical Variables Improve Models of Understorey Plant Species Distributions","volume":"49","author":"Roe","year":"2022","journal-title":"J. Biogeogr."},{"key":"ref_18","first-page":"93","article-title":"Chapter Three\u2014GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties","volume":"Volume 125","author":"Sparks","year":"2014","journal-title":"Advances in Agronomy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bartels, S.F., Caners, R.T., Ogilvie, J., White, B., and Macdonald, S.E. (2018). Relating bryophyte assemblages to a remotely sensed depth-to-water index in boreal forests. Front. Plant Sci., 9.","DOI":"10.3389\/fpls.2018.00858"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3587","DOI":"10.1016\/j.rse.2011.08.020","article-title":"Spectroscopy of Canopy Chemicals in Humid Tropical Forests","volume":"115","author":"Asner","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_21","unstructured":"Ellenberg, H., Weber, H.E., D\u00fcll, R., Wirth, V., and Werner, W. (2001). Zeigerwerte von Planzen in Mitteleuropa, Erich Goltze GmbH & Co KG. [3rd ed.]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1111\/j.1365-2664.2005.01064.x","article-title":"Imaging Spectroscopy as a Tool for Mapping Ellenberg Indicator Values","volume":"42","author":"Schmidtlein","year":"2005","journal-title":"J. Appl. Ecol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.ecolind.2016.01.049","article-title":"Airborne Hyperspectral Data Predict Ellenberg Indicator Values for Nutrient and Moisture Availability in Dry Grazed Grasslands within a Local Agricultural Landscape","volume":"66","author":"Prentice","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pang, H., Zhang, A., Yin, S., Zhang, J., Dong, G., He, N., Qin, W., and Wei, D. (2022). Estimating Carbon, Nitrogen, and Phosphorus Contents of West\u2013East Grassland Transect in Inner Mongolia Based on Sentinel-2 and Meteorological Data. Remote Sens., 14.","DOI":"10.3390\/rs14020242"},{"key":"ref_25","unstructured":"Council of the European Communities (1992). Council Directive 92\/43\/EEC of 21 May 1992 on the Conservation of Natural Habitats and of Wild Fauna and Flora, Council of the European Communities."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"225","DOI":"10.2307\/3236802","article-title":"Reliability of Ellenberg Indicator Values for Moisture, Nitrogen and Soil Reaction: A Comparison with Field Measurements","volume":"11","author":"Schaffers","year":"2000","journal-title":"J. Veg. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1111\/avsc.12020","article-title":"Cost-Effective Assessment of Conservation Status of Fens","volume":"16","author":"Andersen","year":"2013","journal-title":"Appl. Veg. Sci."},{"key":"ref_28","unstructured":"Hagolle, O., Huc, M., Desjardins, C., Auer, S., and Richter, R. (2017). MAJA ATBD\u2014Algorithm Theoretical Basis Document, Centre National d\u2019\u00c9tudes Spatiales."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.3390\/rs70302668","article-title":"A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VEN\u03bcS and Sentinel-2 Images","volume":"7","author":"Hagolle","year":"2015","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Doxani, G., Vermote, E., Roger, J.-C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., and Li, F. (2018). Atmospheric Correction Inter-Comparison Exercise. Remote Sens., 10.","DOI":"10.3390\/rs10020352"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Colin, J., Hagolle, O., Landier, L., Coustance, S., Kettig, P., Meygret, A., Osman, J., and Vermote, E. (2023). Assessment of the Performance of the Atmospheric Correction Algorithm MAJA for Sentinel-2 Surface Reflectance Estimates. Remote Sens., 15.","DOI":"10.3390\/rs15102665"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1111\/2041-210X.13036","article-title":"Landscape History Confounds the Ability of the NDVI to Detect Fine-Scale Variation in Grassland Communities","volume":"9","author":"Prentice","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v067.i01","article-title":"Fitting Linear Mixed-Effects Models Using lme4","volume":"67","author":"Bates","year":"2015","journal-title":"J. Stat. Softw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.ecolind.2018.03.081","article-title":"Predicting Habitat Quality of Protected Dry Grasslands Using Landsat NDVI Phenology","volume":"91","author":"Weber","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e01907","DOI":"10.1002\/eap.1907","article-title":"Light Detection and Ranging Explains Diversity of Plants, Fungi, Lichens, and Bryophytes Across Multiple Habitats and Large Geographic Extent","volume":"29","author":"Moeslund","year":"2019","journal-title":"Ecol. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1111\/j.1654-1103.2002.tb02047.x","article-title":"Validity of Ellenberg Indicator Values Judged from Physico-chemical Field Measurements","volume":"13","author":"Wamelink","year":"2002","journal-title":"J. Veg. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1111\/j.1654-1103.2004.tb02328.x","article-title":"Measurement Errors and Regression to the Mean Cannot Explain Bias in Average Ellenberg Indicator Values","volume":"15","author":"Wamelink","year":"2004","journal-title":"J. Veg. Sci."},{"key":"ref_38","first-page":"843","article-title":"Bias in Ellenberg Indicator Values? Problems with Detection of the Effect of Vegetation Type","volume":"15","author":"Smart","year":"2004","journal-title":"J. Veg. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Amani, M., Foroughnia, F., Moghimi, A., Mahdavi, S., and Jin, S. (2023). Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms. Remote Sens., 15.","DOI":"10.3390\/rs15174135"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gaffney, R., Augustine, D.J., Kearney, S.P., and Porensky, L.M. (2021). Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales. Remote Sens., 13.","DOI":"10.3390\/rs13224603"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s10661-020-8216-3","article-title":"Integrating Drone Imagery with Existing Rangeland Monitoring Programs","volume":"192","author":"Gillan","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1002\/rse2.146","article-title":"Convolutional Neural Networks Accurately Predict Cover Fractions of Plant Species and Communities in Unmanned Aerial Vehicle imagery","volume":"6","author":"Kattenborn","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_43","unstructured":"The Danish Environmental Protection Agency (2024, April 19). New Technology Can Map Nature Areas. Available online: https:\/\/mst.dk\/nyheder\/2022\/marts\/ny-teknologi-kan-kortlaegge-naturomraader."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Marcinkowska-Ochtyra, A., Ochtyra, A., Raczko, E., and Kope\u0107, D. (2023). Natura 2000 Grassland Habitats Mapping Based on Spectro-temporal Dimension of Sentinel-2 Images with Machine Learning. Remote Sens., 15.","DOI":"10.3390\/rs15051388"},{"key":"ref_45","first-page":"042406","article-title":"Habitat Classification Using Convolutional Neural Networks and Multitemporal Multispectral Aerial Imagery","volume":"15","author":"Boydell","year":"2021","journal-title":"J. Appl. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3094\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:40:53Z","timestamp":1760110853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"references-count":45,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16163094"],"URL":"https:\/\/doi.org\/10.3390\/rs16163094","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}