{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:10:25Z","timestamp":1768831825977,"version":"3.49.0"},"reference-count":88,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund","award":["L180283PKKK"],"award-info":[{"award-number":["L180283PKKK"]}]},{"name":"European Union, European Regional Development Fund","award":["L180283PKKK"],"award-info":[{"award-number":["L180283PKKK"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Advances in unmanned aerial systems (UASs) have increased the potential of remote sensing to overcome scale issues for soil moisture (SM) quantification. Regardless, optical imagery is acquired using various sensors and platforms, resulting in simpler operations for management purposes. In this respect, we predicted SM at 10 cm depth using partial least squares regression (PLSR) models based on optical UAS data and assessed the potential of this framework to provide accurate predictions across dates and sites. For this, we evaluated models\u2019 performance using several datasets and the contribution of spectral and photogrammetric predictors on the explanation of SM. The results indicated that our models predicted SM at comparable accuracies as other methods relying on more expensive and complex sensors; the best R2 was 0.73, and the root-mean-squared error (RMSE) was 13.1%. Environmental conditions affected the predictive importance of different metrics; photogrammetric-based metrics were relevant over exposed surfaces, while spectral predictors were proxies of water stress status over homogeneous vegetation. However, the models demonstrated limited applicability across times and locations, particularly in highly heterogeneous conditions. Overall, our findings indicated that integrating UAS imagery and PLSR modelling is suitable for retrieving SM measures, offering an improved method for short-term monitoring tasks.<\/jats:p>","DOI":"10.3390\/rs14102334","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"2334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["The Potential of Optical UAS Data for Predicting Surface Soil Moisture in a Peatland across Time and Sites"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0758-0656","authenticated-orcid":false,"given":"Raul Sampaio","family":"de Lima","sequence":"first","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0077-3770","authenticated-orcid":false,"given":"Kai-Yun","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ants","family":"Vain","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mait","family":"Lang","sequence":"additional","affiliation":[{"name":"Institute of Forestry and Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"},{"name":"Tartu Observatory, Tartu University, Observatooriumi 1, EE-61602 T\u00f5ravere, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thaisa Fernandes","family":"Bergamo","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaupo","family":"Kokam\u00e4gi","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-1608","authenticated-orcid":false,"given":"Niall G.","family":"Burnside","sequence":"additional","affiliation":[{"name":"Centre for Aquatic Environments, School of the Environment and Technology, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton BN2 4GJ, UK"},{"name":"Centre for Earth Observation Science, School of Applied Sciences, University of Brighton, Lewes Road, Brighton BN2 4GJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7391-5530","authenticated-orcid":false,"given":"Raymond D.","family":"Ward","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"},{"name":"Centre for Aquatic Environments, School of the Environment and Technology, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton BN2 4GJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8076-7943","authenticated-orcid":false,"given":"Kalev","family":"Sepp","sequence":"additional","affiliation":[{"name":"Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.1080\/01431161.2017.1339920","article-title":"Dynamic response of NDVI to soil moisture variations during different hydrological regimes in the sahel region","volume":"38","author":"Ahmed","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11707-009-0023-7","article-title":"Satellite remote sensing applications for surface soil moisture monitoring: A review","volume":"3","author":"Wang","year":"2009","journal-title":"Front. Earth Sci. China"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s11769-015-0774-x","article-title":"Effects of water-table depth and soil moisture on plant biomass, diversity, and distribution at a seasonally flooded wetland of Poyang Lake, China","volume":"25","author":"Xu","year":"2015","journal-title":"Chin. Geogr. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jplph.2018.04.012","article-title":"Remote sensing of plant-water relations: An overview and future perspectives","volume":"227","author":"Damm","year":"2018","journal-title":"J. Plant Physiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4892","DOI":"10.1038\/s41467-020-18631-1","article-title":"Soil moisture dominates dryness stress on ecosystem production globally","volume":"11","author":"Liu","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A. (2017). Soil moisture for hydrological applications: Open questions and new opportunities. Water, 9.","DOI":"10.3390\/w9020140"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.rse.2018.10.020","article-title":"Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales","volume":"219","author":"Joiner","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s10661-016-5669-5","article-title":"Re-vegetation processes in cutaway peat production fields in Estonia in relation to peat quality and water regime","volume":"188","author":"Orru","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1038\/ngeo1323","article-title":"Drought-induced carbon loss in peatlands","volume":"4","author":"Fenner","year":"2011","journal-title":"Nat. Geosci."},{"key":"ref_10","first-page":"1261","article-title":"Fire severity is more sensitive to low fuel moisture content on Calluna heathlands than on peat bogs","volume":"616\u2013617","author":"Davies","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1071\/WF19193","article-title":"Soil moisture as an indicator of growing-season herbaceous fuel moisture and curing rate in grasslands","volume":"30","author":"Sharma","year":"2020","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1177\/0309133310365595","article-title":"Ecosystem services of peatlands: Implications for restoration","volume":"34","author":"Kimmel","year":"2010","journal-title":"Prog. Phys. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1016\/j.scitotenv.2016.12.104","article-title":"Including hydrological self-regulating processes in peatland models: Effects on peatmoss drought projections","volume":"580","author":"Nijp","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e2649","DOI":"10.7717\/peerj.2649","article-title":"Regional variation in fire weather controls the reported occurrence of Scottish wildfires","volume":"4","author":"Davies","year":"2016","journal-title":"PeerJ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1111\/j.1365-2486.2006.01292.x","article-title":"Contemporary carbon balance and late Holocene carbon accumulation in a northern peatland","volume":"13","author":"Roulet","year":"2007","journal-title":"Glob. Chang. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1038\/ncomms1523","article-title":"Experimental drying intensifies burning and carbon losses in a northern peatland","volume":"2","author":"Turetsky","year":"2011","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3505","DOI":"10.1002\/2014WR016102","article-title":"Abiotic and biotic controls of soil moisture spatiotemporal variability and the occurrence of hysteresis","volume":"51","author":"Fatichi","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.rse.2018.12.024","article-title":"Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle","volume":"222","author":"Wigmore","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2013.06.004","article-title":"Estimation of soil moisture using optical\/thermal infrared remote sensing in the Canadian Prairies","volume":"83","author":"Berg","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e13990","DOI":"10.1002\/hyp.13990","article-title":"The role of landscape morphology on soil moisture variability in semi-arid ecosystems","volume":"35","author":"Srivastava","year":"2021","journal-title":"Hydrol. Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Millard, K., Thompson, D.K., Parisien, M.A., and Richardson, M. (2018). Soil moisture monitoring in a temperate peatland using multi-sensor remote sensing and linear mixed effects. Remote Sens., 10.","DOI":"10.3390\/rs10060903"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.pce.2015.02.009","article-title":"Surface soil moisture retrievals from remote sensing: Current status, products & future trends","volume":"83\u201384","author":"Petropoulos","year":"2015","journal-title":"Phys. Chem. Earth"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lu, F., Sun, Y., and Hou, F. (2020). Using UAV visible images to estimate the soil moisture of steppe. Water, 12.","DOI":"10.3390\/w12092334"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, D., and Zhou, G. (2016). Estimation of soil moisture from optical and thermal remote sensing: A review. Sensors, 16.","DOI":"10.3390\/s16081308"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.5194\/hess-25-2739-2021","article-title":"Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques","volume":"25","author":"Araya","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","unstructured":"Senyurek, V., Farhad, M., Gurbuz, A.C., Kurum, M., and Moorhead, R. (2021). SoilMoistureMapper: A GNSS-R approach for soil moisture retrieval on UAV. AI for Agriculture and Food Systems, Association for the Advancement of Artificial Intelligence."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.05.026","article-title":"Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)","volume":"197","author":"Amani","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hajdu, I., Yule, I., and Dehghan-shoar, M.H. (2018, January 22\u201327). Modelling of Near-Surface Soil Moisture Using Machine Learning and Multi-Temporal Sentinel 1 Images in New Zealand. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518657"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"West, H., Quinn, N., Horswell, M., and White, P. (2018). Assessing vegetation response to soil moisture fluctuation under extreme drought using sentinel-2. Water, 10.","DOI":"10.3390\/w10070838"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS- MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.envexpbot.2019.01.005","article-title":"Impact of warming and reduced precipitation on photosynthetic and remote sensing properties of peatland vegetation","volume":"160","author":"Rastogi","year":"2019","journal-title":"Environ. Exp. Bot."},{"key":"ref_32","unstructured":"Rouse, J.H., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, MD, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3491","DOI":"10.5194\/bg-16-3491-2019","article-title":"Assessing the peatland hummock-hollow classification framework using high-resolution elevation models: Implications for appropriate complexity ecosystem modeling","volume":"16","author":"Moore","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s40725-019-00094-3","article-title":"Structure from Motion Photogrammetry in Forestry: A Review","volume":"5","author":"Iglhaut","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lendzioch, T., Langhammer, J., Vl\u010dek, L., and Mina\u0159\u00edk, R. (2021). Mapping the groundwater level and soil moisture of a montane peat bog using uav monitoring and machine learning. Remote Sens., 13.","DOI":"10.5194\/egusphere-egu21-6687"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e6926","DOI":"10.7717\/peerj.6926","article-title":"Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring","volume":"7","author":"Ge","year":"2019","journal-title":"PeerJ"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s11306-020-1640-0","article-title":"Migrating from partial least squares discriminant analysis to artificial neural networks: A comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks","volume":"16","author":"Mendez","year":"2020","journal-title":"Metabolomics"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107530","DOI":"10.1016\/j.agwat.2022.107530","article-title":"Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning","volume":"264","author":"Cheng","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Salkind, N.J. (2007). Partial Least Square Regression. Encyclopedia of Measurement and Statistics, Sage.","DOI":"10.4135\/9781412952644"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Maimaitiyiming, M., Ghulam, A., Bozzolo, A., Wilkins, J.L., and Kwasniewski, M.T. (2017). Early detection of plant physiological responses to different levels of water stress using reflectance spectroscopy. Remote Sens., 9.","DOI":"10.3390\/rs9070745"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1207\/s15328031us0304_4","article-title":"A beginner\u2019s guide to partial least squares analysis, Understanding Statistics\u201d. Statistical Issues in Psychology and Social Sciences, Volume 3","volume":"3","author":"Haenlein","year":"2004","journal-title":"Underst. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1111\/j.1600-0706.2008.16881.x","article-title":"Partial least squares regression as an alternative to current regression methods used in ecology","volume":"118","author":"Carrascal","year":"2009","journal-title":"Oikos"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3346","DOI":"10.1080\/01431161.2019.1701723","article-title":"Regional scale soil moisture content estimation based on multi-source remote sensing parameters","volume":"41","author":"Ainiwaer","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2017.10.007","article-title":"Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference","volume":"204","author":"Puliti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_46","unstructured":"Paal, J. (2011). J\u00e4\u00e4ksood, Nende Kasutamine ja Korrastamine, Eesti Turbaliit. [1st ed.]."},{"key":"ref_47","unstructured":"(2021, May 18). Estonian Weather Service Climate Normals. Available online: http:\/\/www.ilmateenistus.ee\/kliima\/kliimanormid\/ohutemperatuur\/?lang=en."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.2112\/JCOASTRES-D-15-00065.1","article-title":"Importance of Microtopography in Determining Plant Community Distribution in Baltic Coastal Wetlands","volume":"32","author":"Ward","year":"2016","journal-title":"J. Coast. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1111\/j.1654-1103.2007.tb02578.x","article-title":"Use of vegetation classification and plant indicators to assess grazing abandonment in Estonian coastal wetlands","volume":"18","author":"Burnside","year":"2007","journal-title":"J. Veg. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10750-011-0826-x","article-title":"Differential responses of abandoned wet grassland plant communities to reinstated cutting management","volume":"692","author":"Berg","year":"2012","journal-title":"Hydrobiologia"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.2136\/sssaj2010.0238","article-title":"WET Sensor Performance in Organic and Inorganic Media with Heterogeneous Moisture Distribution","volume":"75","author":"Kargas","year":"2011","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3846\/gac.2018.2023","article-title":"Modernization of the estonian national gnss reference station network","volume":"44","author":"Metsar","year":"2018","journal-title":"Geod. Cartogr."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Toma\u0161t\u00edk, J., Mokro\u0161, M., Surov\u00fd, P., Grzn\u00e1rov\u00e1, A., and Mergani\u010d, J. (2019). UAV RTK \/ PPK Method\u2014An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote Sens., 11.","DOI":"10.3390\/rs11060721"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.3390\/rs71013895","article-title":"Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure","volume":"7","author":"Dandois","year":"2015","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fraser, B.T., and Congalton, R.G. (2018). Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments. Remote Sens., 10.","DOI":"10.3390\/rs10060908"},{"key":"ref_56","unstructured":"Daniel Girardeau-Montaut (2020, December 01). CloudCompare. Available online: https:\/\/www.danielgm.net\/cc\/."},{"key":"ref_57","unstructured":"Roussel, J.-R., and Auty, D. (2020, December 01). lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. Available online: https:\/\/cran.r-project.org\/web\/packages\/lidR\/lidR.pdf."},{"key":"ref_58","unstructured":"R Core Team (2018). A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1672\/0277-5212(2007)27[1081:COMAII]2.0.CO;2","article-title":"Characterization of microtopography and its influence on vegetation patterns in created wetlands","volume":"27","author":"Moser","year":"2007","journal-title":"Wetlands"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for Automated Geoscientific Analyses (SAGA) v. 2.1.4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1080\/01431160500196398","article-title":"Determination of green herbage ratio in grasslands using spectral reflectance. Methods and ground measurements","volume":"28","author":"Gianelle","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral Reflectance Changes Associated with Autumn Senescence Features and Relation to Chlorophyll Estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mishra, S., and Datta-Gupta, A. (2018). Data-Driven Modeling. Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences, Elsevier.","DOI":"10.1016\/B978-0-12-803279-4.00008-0"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.eja.2012.12.001","article-title":"A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems","volume":"46","author":"Delegido","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_67","unstructured":"Mevik, B.-H., Wehrens, R., and Liland, K.H. (2022, February 08). pls: Partial Least Squares and Principal Component Regression. Available online: https:\/\/cran.r-project.org\/web\/packages\/pls\/pls.pdf."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kachamba, D.J., \u00d8rka, H.O., Gobakken, T., Eid, T., and Mwase, W. (2016). Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sens., 8.","DOI":"10.3390\/rs8110968"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Li, K.Y., Burnside, N.G., de Lima, R.S., Peci\u00f1a, M.V., Sepp, K., Yang, M., Der Raet, J., Vain, A., Selge, A., and Sepp, K. (2021). The application of an unmanned aerial system and machine learning techniques for red clover-grass mixture yield estimation under variety performance trials. Remote Sens., 13.","DOI":"10.3390\/rs13101994"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1002\/cem.887","article-title":"Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)","volume":"18","author":"Mevik","year":"2004","journal-title":"J. Chemom."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.chemolab.2012.07.010","article-title":"A review of variable selection methods in Partial Least Squares Regression","volume":"118","author":"Mehmood","year":"2012","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/cem.2627","article-title":"Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS)","volume":"28","author":"Eriksson","year":"2014","journal-title":"J. Chemom."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"38","DOI":"10.2747\/0272-3646.29.1.38","article-title":"The relationship between soil moisture and NDVI near Barrow, Alaska","volume":"29","author":"Engstrom","year":"2008","journal-title":"Phys. Geogr."},{"key":"ref_74","first-page":"176","article-title":"Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data","volume":"50","author":"Amani","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"108471","DOI":"10.1016\/j.agrformet.2021.108471","article-title":"Estimating root zone soil moisture across diverse land cover types by integrating in-situ and remotely sensed data","volume":"307","author":"Wyatt","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"5689","DOI":"10.5194\/bg-12-5689-2015","article-title":"Modeling micro-topographic controls on boreal peatland hydrology and methane fluxes","volume":"12","author":"Runkle","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isprsjprs.2021.08.014","article-title":"Developing bare-earth digital elevation models from structure-from-motion data on barrier islands","volume":"180","author":"Enwright","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1007\/s10021-020-00481-z","article-title":"Characterizing Peatland Microtopography Using Gradient and Microform-Based Approaches","volume":"23","author":"Graham","year":"2020","journal-title":"Ecosystems"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.2134\/agronj2007.0306","article-title":"Sensitivity of narrow-band and broad-band indices for assessing nitrogen availability and water stress in an annual crop","volume":"100","author":"Perry","year":"2008","journal-title":"Agron. J."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2934","DOI":"10.1109\/JSTARS.2019.2918487","article-title":"Determining Effective Meter-Scale Image Data and Spectral Vegetation Indices for Tropical Forest Tree Species Differentiation","volume":"12","author":"Cross","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11119-005-2324-5","article-title":"Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status","volume":"6","author":"Hunt","year":"2005","journal-title":"Precis. Agric."},{"key":"ref_84","first-page":"103","article-title":"A visible band index for remote sensing leaf chlorophyll content at the Canopy scale","volume":"21","author":"Hunt","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_85","first-page":"170","article-title":"SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity","volume":"50","author":"Quintano","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.jhydrol.2014.03.019","article-title":"Burned and unburned peat water repellency: Implications for peatland evaporation following wildfire","volume":"513","author":"Kettridge","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1080\/01431161.2016.1271477","article-title":"Combining ground-based measurements and MODIS-based spectral vegetation indices to track biomass accumulation in post-fire chaparral","volume":"38","author":"Uyeda","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","unstructured":"Reisfeld, B., and Mayeno, A.N. (2013). Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression. Computational Toxicology: Volume II, Humana Press."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2334\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:09:40Z","timestamp":1760137780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,12]]},"references-count":88,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102334"],"URL":"https:\/\/doi.org\/10.3390\/rs14102334","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,12]]}}}