{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T21:51:56Z","timestamp":1782856316981,"version":"3.54.5"},"reference-count":215,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32071890"],"award-info":[{"award-number":["32071890"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFD1700904"],"award-info":[{"award-number":["2021YFD1700904"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GZS2021007"],"award-info":[{"award-number":["GZS2021007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Technologies R&amp;D Program of China during the 14th Five-Year Plan period","award":["32071890"],"award-info":[{"award-number":["32071890"]}]},{"name":"National Key Technologies R&amp;D Program of China during the 14th Five-Year Plan period","award":["2021YFD1700904"],"award-info":[{"award-number":["2021YFD1700904"]}]},{"name":"National Key Technologies R&amp;D Program of China during the 14th Five-Year Plan period","award":["GZS2021007"],"award-info":[{"award-number":["GZS2021007"]}]},{"name":"Henan Center for Outstanding Overseas Scientists","award":["32071890"],"award-info":[{"award-number":["32071890"]}]},{"name":"Henan Center for Outstanding Overseas Scientists","award":["2021YFD1700904"],"award-info":[{"award-number":["2021YFD1700904"]}]},{"name":"Henan Center for Outstanding Overseas Scientists","award":["GZS2021007"],"award-info":[{"award-number":["GZS2021007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sustainability"],"abstract":"<jats:p>Remote sensing (RS) techniques offer advantages over other methods for measuring soil properties, including large-scale coverage, a non-destructive nature, temporal monitoring, multispectral capabilities, and rapid data acquisition. This review highlights the different detection methods, types, parts, and applications of RS techniques in soil measurements, as well as the advantages and disadvantages of the measurements of soil properties. The choice of the methods depends on the specific requirements of the soil measurements task because it is important to consider the advantages and limitations of each method, as well as the specific context and objective of the soil measurements, to determine the most suitable RS technique. This paper follows a well-structured arrangement after investigating the existing literature to ensure a well-organized, coherent review and covers all the essential aspects related to studying the advancement of using RS in the measurements of soil properties. While several remote sensing methods are available, this review suggests spectral reflectance, which entails satellite remote sensing and other tools based on its global coverage, high spatial resolution, long-term monitoring capabilities, non-invasiveness, and cost effectiveness. Conclusively, RS has improved soil property measurements using various methods, but more research is needed for calibration, sensor fusion, artificial intelligence, validation, and machine learning applications to enhance accuracy and applicability.<\/jats:p>","DOI":"10.3390\/su152115444","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T12:07:45Z","timestamp":1698667665000},"page":"15444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":171,"title":["Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6745-378X","authenticated-orcid":false,"given":"Mukhtar Iderawumi","family":"Abdulraheem","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"},{"name":"Henan International Joint Laboratory of Laser Technology in Agriculture Science, Zhengzhou 450002, China"},{"name":"State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"},{"name":"Henan International Joint Laboratory of Laser Technology in Agriculture Science, Zhengzhou 450002, China"},{"name":"State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9457-6267","authenticated-orcid":false,"given":"Ata Jahangir","family":"Moshayedi","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aitazaz A.","family":"Farooque","sequence":"additional","affiliation":[{"name":"Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada"},{"name":"Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peter\u2019s Bay, PE C1A 4P3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiandong","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"},{"name":"Henan International Joint Laboratory of Laser Technology in Agriculture Science, Zhengzhou 450002, China"},{"name":"State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s40518-017-0082-4","article-title":"Soil as a Basic Nexus Tool: Soils at the Center of the Food\u2013Energy\u2013Water Nexus","volume":"4","author":"Lal","year":"2017","journal-title":"Curr. Sustain.\/Renew. Energy Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1071\/SR19067","article-title":"Soil biodiversity and biogeochemical function in managed ecosystems","volume":"58","author":"Chen","year":"2020","journal-title":"Soil Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e00398","DOI":"10.1016\/j.geodrs.2021.e00398","article-title":"Soils and sustainable development goals of the United Nations: An International Union of Soil Sciences perspective","volume":"25","author":"Lal","year":"2021","journal-title":"Geoderma Reg."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Motia, S., and Reddy, S.R.N. (2021). Exploration of Machine Learning Methods for Prediction and Assessment of Soil Properties for Agricultural Soil Management: A Quantitative Evaluation, IOP Publishing.","DOI":"10.1088\/1742-6596\/1950\/1\/012037"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3464","DOI":"10.3390\/rs15143464","article-title":"Remote Sensing for Soil Organic Carbon Mapping and Monitoring","volume":"15","author":"Chabrillat","year":"2023","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.3390\/rs11202356","article-title":"Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity\u2014Part I: Soil Characteristics","volume":"11","author":"Lausch","year":"2019","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Datta, D., Paul, M., Murshed, M., Teng, S.W., and Schmidtke, L. (2022). Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models. Sensors, 22.","DOI":"10.3390\/s22207998"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2540","DOI":"10.3390\/rs15102540","article-title":"Challenges and Opportunities in Remote Sensing for Soil Salinization Mapping and Monitoring: A Review","volume":"15","author":"Sahbeni","year":"2023","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114863","DOI":"10.1016\/j.geoderma.2020.114863","article-title":"Multi-algorithm comparison to predict soil organic matter and soil moisture content from cell phone images","volume":"385","author":"Taneja","year":"2021","journal-title":"Geoderma"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1007\/s40999-019-00421-6","article-title":"Settlement Prediction Using Support Vector Machine (SVM)-Based Compressibility Models: A Case Study","volume":"17","author":"Kirts","year":"2019","journal-title":"Int. J. Civ. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Heil, J., J\u00f6rges, C., and Stumpe, B. (2022). Fine-Scale Mapping of Soil Organic Matter in Agricultural Soils Using UAVs and Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14143349"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.3390\/rs11121443","article-title":"Unmanned Aerial Vehicle for Remote Sensing Applications\u2014A Review","volume":"11","author":"Yao","year":"2019","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1093\/aepp\/ppx056","article-title":"Big Data in Agriculture: A Challenge for the Future","volume":"40","author":"Coble","year":"2018","journal-title":"Appl. Econ. Perspect. Policy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.3390\/rs13061204","article-title":"A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges","volume":"13","author":"Delavarpour","year":"2021","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Khanal, S., Kc, K., Fulton, J.P., Shearer, S., and Ozkan, E. (2020). Remote sensing in agriculture\u2014Accomplishments, limitations, and opportunities. Remote Sens., 12.","DOI":"10.3390\/rs12223783"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.compag.2017.05.001","article-title":"An overview of current and potential applications of thermal remote sensing in precision agriculture","volume":"139","author":"Khanal","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"641","DOI":"10.3390\/rs10040641","article-title":"On the use of unmanned aerial systems for environmental monitoring","volume":"10","author":"Manfreda","year":"2018","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.1007\/s13762-019-02310-w","article-title":"Detecting vegetation stress as a soil contamination proxy: A review of optical proximal and remote sensing techniques","volume":"16","author":"Gholizadeh","year":"2019","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_19","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.3390\/s21103457","article-title":"Novel Weight-Based Approach for Soil Moisture Content Estimation via Synthetic Aperture Radar, Multispectral and Thermal Infrared Data Fusion","volume":"21","author":"Yahia","year":"2021","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Muhadi, N.A., Abdullah, A.F., Bejo, S.K., Mahadi, M.R., and Mijic, A. (2020). The Use of LiDAR-Derived DEM in Flood Applications: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142308"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1007\/s10712-020-09609-1","article-title":"Remote Sensing for Assessing Landslides and Associated Hazards","volume":"41","author":"Lissak","year":"2020","journal-title":"Surv. Geophys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"960","DOI":"10.3390\/s22030960","article-title":"Remote Sensing Methods for Flood Prediction: A Review","volume":"22","author":"Munawar","year":"2022","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.jhydrol.2018.04.027","article-title":"Remote sensing, hydrological modeling and in situ observations in snow cover research: A review","volume":"561","author":"Dong","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1038\/s43017-020-00122-y","article-title":"Uniting remote sensing, crop modelling and economics for agricultural risk management","volume":"2","author":"Benami","year":"2021","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106844","DOI":"10.1016\/j.compag.2022.106844","article-title":"A comprehensive review of remote sensing platforms, sensors, and applications in nut crops","volume":"197","author":"Jafarbiglu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","article-title":"Remote sensing methods for power line corridor surveys","volume":"119","author":"Matikainen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Brauchle, J., Bayer, S., and Berger, R. (2018). Image and Video Technology, Springer International Publishing."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.agron.2014.12.004","article-title":"Fusion of Soil and Remote Sensing Data to Model Soil Properties","volume":"131","author":"Grunwald","year":"2015","journal-title":"Adv. Agron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s11119-010-9158-5","article-title":"Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery","volume":"12","author":"Roberts","year":"2011","journal-title":"Precis. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1007\/s11119-020-09733-3","article-title":"Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data","volume":"22","author":"Argento","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.3390\/rs15041106","article-title":"Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation","volume":"15","author":"Angelopoulou","year":"2023","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.compag.2015.02.004","article-title":"Energy efficient automated control of irrigation in agriculture by using wireless sensor networks","volume":"113","author":"Nikolidakis","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon","volume":"179","author":"Castaldi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.landusepol.2018.10.004","article-title":"Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers","volume":"80","author":"Barnes","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/rse2.44","article-title":"UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring","volume":"4","author":"Sankey","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_39","unstructured":"Kumar, S., Meena, R.S., Sheoran, S., Jangir, C.K., Jhariya, M.K., Banerjee, A., and Raj, A. (2022). Natural Resources Conservation and Advances for Sustainability, Elsevier."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ahmadi, A., Emami, M., Daccache, A., and He, L. (2021). Soil properties prediction for precision agriculture using visible and near-infrared spectroscopy: A systematic review and meta-analysis. Agronomy, 11.","DOI":"10.3390\/agronomy11030433"},{"key":"ref_41","first-page":"1","article-title":"Effects of magnetic field on pre-treament of seedlings and germination","volume":"6","author":"Iderawumi","year":"2020","journal-title":"J. Agric. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1029\/2018RG000598","article-title":"Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review","volume":"56","author":"Huang","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"115232","DOI":"10.1016\/j.geoderma.2021.115232","article-title":"Using remote sensors to predict soil properties: Radiometry and peat depth in Dartmoor, UK","volume":"403","author":"Marchant","year":"2021","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111747","DOI":"10.1016\/j.rse.2020.111747","article-title":"The application of Unmanned Aerial Vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems","volume":"242","author":"Navarro","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/jimaging4040052","article-title":"Contribution of Remote Sensing on Crop Models: A Review","volume":"4","author":"Kasampalis","year":"2018","journal-title":"J. Imaging"},{"key":"ref_46","first-page":"192","article-title":"Remote sensing applications in soil survey and mapping: A Review","volume":"7","author":"Singh","year":"2016","journal-title":"Int. J. Geomat. Geosci."},{"key":"ref_47","first-page":"102856","article-title":"Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry","volume":"112","author":"Jurado","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","first-page":"860","article-title":"Hyperspectral remote sensing: Opportunities, status and challenges for rapid soil assessment in India","volume":"108","author":"Das","year":"2015","journal-title":"Curr. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1007\/s12594-017-0759-8","article-title":"Remote sensing for recognition and monitoring of vegetation affected by soil properties","volume":"90","author":"Sashikkumar","year":"2017","journal-title":"J. Geol. Soc. India"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating multispectral images and vegetation indices for precision farming applications from UAV images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111383","DOI":"10.1016\/j.rse.2019.111383","article-title":"Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_52","first-page":"293","article-title":"Satellite Meteorological Parameters","volume":"Volume 1","year":"2018","journal-title":"Global Satellite Meteorological Observation (GSMO) Theory"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11601","DOI":"10.3390\/su151511601","article-title":"E-Nose-Driven Advancements in Ammonia Gas Detection: A Comprehensive Review from Traditional to Cutting-Edge Systems in Indoor to Outdoor Agriculture","volume":"15","author":"Moshayedi","year":"2023","journal-title":"Sustainability"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.3390\/rs13152970","article-title":"Non-Destructive Biomass Estimation in Mediterranean Alpha Steppes: Improving Traditional Methods for Measuring Dry and Green Fractions by Combining Proximal Remote Sensing Tools","volume":"13","author":"Maggioli","year":"2021","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1007\/s12665-014-3613-y","article-title":"Hyperspectral remote sensing data derived spectral indices in characterizing salt-affected soils: A case study of Indo-Gangetic plains of India","volume":"73","author":"Kumar","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.jappgeo.2015.03.009","article-title":"Improved estimation of soil clay content by the fusion of remote hyperspectral and proximal geophysical sensing","volume":"116","author":"Ciampalini","year":"2015","journal-title":"J. Appl. Geophys."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.3390\/rs15123170","article-title":"The Distribution of Surface Soil Moisture over Space and Time in Eastern Taylor Valley, Antarctica","volume":"15","author":"Salvatore","year":"2023","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2018.04.047","article-title":"Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"1","article-title":"Application of near-infrared reflectance for quantitative assessment of soil properties","volume":"21","author":"Mohamed","year":"2018","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"113468","DOI":"10.1016\/j.sna.2022.113468","article-title":"A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection","volume":"338","author":"Zahir","year":"2022","journal-title":"Sens. Actuators A Phys."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3390\/min12040394","article-title":"Indicator Minerals, Pathfinder Elements, and Portable Analytical Instruments in Mineral Exploration Studies","volume":"12","author":"Balaram","year":"2022","journal-title":"Minerals"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1111\/1365-2745.13957","article-title":"Thermal remote sensing for plant ecology from leaf to globe","volume":"110","author":"Farella","year":"2022","journal-title":"J. Ecol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MGRS.2018.2889610","article-title":"Longwave Infrared Hyperspectral Imaging: Principles, Progress, and Challenges","volume":"7","author":"Manolakis","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"91","DOI":"10.3390\/s18010091","article-title":"A Review of Oil Spill Remote Sensing","volume":"18","author":"Fingas","year":"2018","journal-title":"Sensors"},{"key":"ref_65","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","author":"Petropoulos","year":"2015","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_66","first-page":"563015","article-title":"Analysis of the Dielectric Constant of Saline-Alkali Soils and the Effect on Radar Backscattering Coefficient: A Case Study of Soda Alkaline Saline Soils in Western Jilin Province Using RADARSAT-2 Data","volume":"2014","author":"Li","year":"2014","journal-title":"Sci. World J."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"15","DOI":"10.4236\/ars.2015.41002","article-title":"Interrelationship analysis of L-band backscattering intensity and soil dielectric constant for soil moisture retrieval using PALSAR data","volume":"4","author":"Gharechelou","year":"2015","journal-title":"Adv. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.3390\/rs12183102","article-title":"Sensing archaeology in the north: The use of non-destructive geophysical and remote sensing methods in archaeology in Scandinavian and North Atlantic territories","volume":"12","author":"Bates","year":"2020","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Iftimie, N., Savin, A., Steigmann, R., and Dobrescu, G.S. (2021). Underground pipeline identification into a non-destructive case study based on ground-penetrating radar imaging. Remote Sens., 13.","DOI":"10.3390\/rs13173494"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"103107","DOI":"10.1016\/j.jnca.2021.103107","article-title":"A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0","volume":"187","author":"Raj","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.scitotenv.2018.02.204","article-title":"High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia","volume":"630","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"904561","DOI":"10.1155\/2011\/904561","article-title":"Remote sensing of soil","volume":"2011","author":"Zribi","year":"2011","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.3390\/agronomy13071868","article-title":"Using Remote and Proximal Sensing in Organic Agriculture to Assess Yield and Environmental Performance","volume":"13","author":"Schuster","year":"2023","journal-title":"Agronomy"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compag.2004.03.002","article-title":"On-the-go soil sensors for precision agriculture","volume":"44","author":"Adamchuk","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.iswcr.2023.03.002","article-title":"Remote sensing of soil degradation: Progress and perspective","volume":"11","author":"Wang","year":"2023","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.isprsjprs.2022.03.013","article-title":"Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives","volume":"187","author":"Li","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1038\/s41467-021-22047-w","article-title":"A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems","volume":"12","author":"Zhu","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Shaik, R.U., Periasamy, S., and Zeng, W. (2023). Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications. Remote Sens., 15.","DOI":"10.3390\/rs15051378"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1109\/TGRS.2012.2213604","article-title":"A sparse image fusion algorithm with application to pan-sharpening","volume":"51","author":"Zhu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.3390\/rs11171968","article-title":"A New Retrieval Algorithm for Soil Moisture Index from Thermal Infrared Sensor On-Board Geostationary Satellites over Europe and Africa and Its Validation","volume":"11","author":"Ghilain","year":"2019","journal-title":"Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Viscarra Rossel, R.A., McBratney, A.B., and Minasny, B. (2010). Proximal Soil Sensing, Springer.","DOI":"10.1007\/978-90-481-8859-8"},{"key":"ref_83","unstructured":"Pandey, P.C., Srivastava, P.K., Balzter, H., Bhattacharya, B., and Petropoulos, G.P. (2020). Hyperspectral Remote Sensing, Elsevier."},{"key":"ref_84","first-page":"340","article-title":"Panchromatic and multispectral remote sensing image fusion using machine learning for classifying bucolic and farming region","volume":"15","author":"Kumar","year":"2018","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Eldeeb, M.A., Dhamu, V.N., Paul, A., Muthukumar, S., and Prasad, S. (2023). Electrochemical Soil Nitrate Sensor for In Situ Real-Time Monitoring. Micromachines, 14.","DOI":"10.3390\/mi14071314"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"308","DOI":"10.3390\/rs13020308","article-title":"Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery","volume":"13","author":"Biney","year":"2021","journal-title":"Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3390\/futuretransp3010012","article-title":"A Secure Traffic Police Remote Sensing Approach via a Deep Learning-Based Low-Altitude Vehicle Speed Detector through UAVs in Smart Cites: Algorithm, Implementation and Evaluation","volume":"3","author":"Moshayedi","year":"2023","journal-title":"Future Transp."},{"key":"ref_88","first-page":"1","article-title":"The use of light detection and ranging (LiDAR) technology and GIS in the assessment and mapping of bioresources in Davao Region, Mindanao Island, Philippines","volume":"13","author":"Novero","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"del-Moral-Mart\u00ednez, I., Rosell-Polo, J.R., Company, J., Sanz, R., Escola, A., Masip, J., Martinez-Casasnovas, J.A., and Arn\u00f3, J. (2016). Mapping vineyard leaf area using mobile terrestrial laser scanners: Should rows be scanned on-the-go or discontinuously sampled?. Sensors, 16.","DOI":"10.3390\/s16010119"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"8250","DOI":"10.3390\/rs70708250","article-title":"Estimation of Surface Soil Moisture from Thermal Infrared Remote Sensing Using an Improved Trapezoid Method","volume":"7","author":"Yang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.3390\/s20092444","article-title":"Applying Infrared Thermography to Soil Surface Temperature Monitoring: Case Study of a High-Resolution 48 h Survey in a Vineyard (Anadia, Portugal)","volume":"20","author":"Frodella","year":"2020","journal-title":"Sensors"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Zedler, M., Tse, S.W., Ruiz-Gonzalez, A., and Haseloff, J. (2023). Paper-Based Multiplex Sensors for the Optical Detection of Plant Stress. Micromachines, 14.","DOI":"10.3390\/mi14020314"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.geoderma.2016.04.019","article-title":"Using legacy data for correction of soil surface clay content predicted from VNIR\/SWIR hyperspectral airborne images","volume":"276","author":"Gomez","year":"2016","journal-title":"Geoderma"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"107","DOI":"10.13031\/jash.22.11260","article-title":"Object detection for agricultural and construction environments using an ultrasonic sensor","volume":"22","author":"Dvorak","year":"2016","journal-title":"J. Agric. Saf. Health"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"084010","DOI":"10.1088\/1748-9326\/ac7ed8","article-title":"Remote sensing-based vegetation and soil moisture constraints reduce irrigation estimation uncertainty","volume":"17","author":"Nie","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s00216-010-3672-1","article-title":"Chemical sensing and imaging with pulsed terahertz radiation","volume":"397","author":"Walther","year":"2010","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2109357","DOI":"10.1002\/adma.202109357","article-title":"Wearable Pressure Sensors for Pulse Wave Monitoring","volume":"34","author":"Meng","year":"2022","journal-title":"Adv. Mater."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"229462","DOI":"10.1016\/j.jpowsour.2021.229462","article-title":"Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement","volume":"489","author":"Wei","year":"2021","journal-title":"J. Power Sources"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.still.2012.08.005","article-title":"Calibration of an on-line sensor for measurement of topsoil bulk density in all soil textures","volume":"126","author":"Quraishi","year":"2013","journal-title":"Soil Tillage Res."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1007\/s11277-017-4948-y","article-title":"Wireless soil monitoring sensor for sprinkler irrigation automation system","volume":"98","author":"Nagarajan","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Pei, X., Sudduth, K.A., Veum, K.S., and Li, M. (2019). Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe. Sensors, 19.","DOI":"10.3390\/s19051011"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Foldager, F.F., Pedersen, J.M., Haubro Skov, E., Evgrafova, A., and Green, O. (2019). LiDAR-Based 3D Scans of Soil Surfaces and Furrows in Two Soil Types. Sensors, 19.","DOI":"10.3390\/s19030661"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1007\/s11119-012-9280-7","article-title":"Sensor data fusion to predict multiple soil properties","volume":"13","author":"Mahmood","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.still.2012.10.002","article-title":"Use of a triple-sensor fusion system for on-the-go measurement of soil compaction","volume":"128","author":"Sharifi","year":"2013","journal-title":"Soil Tillage Res."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Messina, G., Pe\u00f1a, J.M., Vizzari, M., and Modica, G. (2020). A comparison of UAV and satellites multispectral imagery in monitoring onion crop. An application in the \u2018Cipolla Rossa di Tropea\u2019 (Italy). Remote Sens., 12.","DOI":"10.3390\/rs12203424"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1080\/02626667.2017.1334166","article-title":"A revisit of NRCS-CN inspired models coupled with RS and GIS for runoff estimation","volume":"62","author":"Verma","year":"2017","journal-title":"Hydrol. Sci. J."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_108","unstructured":"Wong, M.S., Zhu, X., Abbas, S., Kwok, C.Y.T., and Wang, M. (2021). Urban Informatics, Springer."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s13201-021-01419-z","article-title":"Monitoring and investigating dust phenomenon on using remote sensing science, geographical information system and statistical methods","volume":"11","author":"Abdollahi","year":"2021","journal-title":"Appl. Water Sci."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"111291","DOI":"10.1016\/j.rse.2019.111291","article-title":"Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities","volume":"232","author":"West","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1080\/15481603.2019.1690780","article-title":"Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","volume":"57","author":"Gumma","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13717-020-00255-4","article-title":"Current and near-term advances in Earth observation for ecological applications","volume":"10","author":"Ustin","year":"2021","journal-title":"Ecol. Process."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"113195","DOI":"10.1016\/j.rse.2022.113195","article-title":"Fifty years of Landsat science and impacts","volume":"280","author":"Wulder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Hemati, M., Hasanlou, M., Mahdianpari, M., and Mohammadimanesh, F. (2021). A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth. Remote Sens., 13.","DOI":"10.3390\/rs13152869"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.actaastro.2017.04.034","article-title":"Towards disruptions in Earth observation? New Earth Observation systems and markets evolution: Possible scenarios and impacts","volume":"137","author":"Denis","year":"2017","journal-title":"Acta Astronaut."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Gilliam, A.D., Pollard, T.B., Neff, A., Dong, Y., Sorensen, S., Wagner, R., Chew, S., Rovito, T.V., and Mundy, J.L. (2018, January 12\u201315). SatTel: A Framework for Commercial Satellite Imagery Exploitation. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00037"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"096044","DOI":"10.1117\/1.JRS.9.096044","article-title":"Validation of DigitalGlobe WorldView-3 Earth imaging satellite shortwave infrared bands for mineral mapping","volume":"9","author":"Fred","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","article-title":"Historical background and current developments for mapping burned area from satellite Earth observation","volume":"225","author":"Chuvieco","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.actaastro.2015.09.021","article-title":"The current and potential role of satellite remote sensing in the campaign against malaria","volume":"121","author":"Kazansky","year":"2016","journal-title":"Acta Astronaut."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.3390\/rs10081230","article-title":"Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment","volume":"10","author":"Schumann","year":"2018","journal-title":"Remote Sens."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.3390\/rs12122030","article-title":"Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the U.S. Southern Great Plains","volume":"12","author":"Jiang","year":"2020","journal-title":"Remote Sens."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.biocon.2014.11.048","article-title":"Free and open-access satellite data are key to biodiversity conservation","volume":"182","author":"Turner","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/MGRS.2017.2688704","article-title":"The ESA\u2019s Earth Observation Open Science Program [Space Agencies]","volume":"5","author":"Mathieu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.3390\/rs13153003","article-title":"European Space Agency (ESA) Calibration\/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability","volume":"13","author":"Niro","year":"2021","journal-title":"Remote Sens."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.spacepol.2015.01.001","article-title":"Open data policies and satellite Earth observation","volume":"32","author":"Harris","year":"2015","journal-title":"Space Policy"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.3390\/s8052986","article-title":"Hydrologic Remote Sensing and Land Surface Data Assimilation","volume":"8","author":"Moradkhani","year":"2008","journal-title":"Sensors"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"424178","DOI":"10.1155\/2013\/424178","article-title":"Remote Sensing of Soil Moisture","volume":"2013","author":"Lakshmi","year":"2013","journal-title":"ISRN Soil Sci."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2014.03.009","article-title":"Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites","volume":"103","author":"Belward","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.3390\/rs15071938","article-title":"Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters","volume":"15","author":"Adjovu","year":"2023","journal-title":"Remote Sens."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/rs8010070","article-title":"A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring","volume":"8","author":"Joshi","year":"2016","journal-title":"Remote Sens."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Pereira, P., Brevik, E.C., Mu\u00f1oz-Rojas, M., and Miller, B.A. (2017). Soil Mapping and Process Modeling for Sustainable Land Use Management, Elsevier.","DOI":"10.1016\/B978-0-12-805200-6.00002-5"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"111280","DOI":"10.1016\/j.rse.2019.111280","article-title":"Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907\u20132017)","volume":"232","author":"Luo","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Visser, S., Keesstra, S., Maas, G., de Cleen, M., and Molenaar, C. (2019). Soil as a Basis to Create Enabling Conditions for Transitions towards Sustainable Land Management as a Key to Achieve the SDGs by 2030. Sustainability, 11.","DOI":"10.3390\/su11236792"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"037550","DOI":"10.1149\/1945-7111\/ab69fe","article-title":"Perspective\u2014Electrochemical Sensors for Soil Quality Assessment","volume":"167","author":"Ali","year":"2020","journal-title":"J. Electrochem. Soc."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"103251","DOI":"10.1016\/j.agsy.2021.103251","article-title":"Manure management and soil biodiversity: Towards more sustainable food systems in the EU","volume":"194","author":"Lugato","year":"2021","journal-title":"Agric. Syst."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1038\/s43016-022-00584-x","article-title":"A precision compost strategy aligning composts and application methods with target crops and growth environments can increase global food production","volume":"3","author":"Zhao","year":"2022","journal-title":"Nat. Food"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Lichtfouse, E. (2017). Sustainable Agriculture Reviews, Springer International Publishing.","DOI":"10.1007\/978-3-319-48006-0"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"04017037","DOI":"10.1061\/(ASCE)IR.1943-4774.0001222","article-title":"Assessment of field spatial and temporal variabilities to delineate site-specific management zones for variable-rate irrigation","volume":"143","author":"Yari","year":"2017","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Wang, J., Peng, J., Li, H., Yin, C., Liu, W., Wang, T., and Zhang, H. (2021). Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China. Remote Sens., 13.","DOI":"10.3390\/rs13020305"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4108\/airo.v2i1.3056","article-title":"E-Nose design and structures from statistical analysis to application in robotic: A compressive review","volume":"2","author":"Moshayedi","year":"2023","journal-title":"EAI Endorsed Trans. AI Robot."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.isprsjprs.2019.11.018","article-title":"Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review","volume":"159","author":"Gao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rse.2016.02.056","article-title":"Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel","volume":"177","author":"Tian","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.asr.2021.10.024","article-title":"Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data","volume":"69","author":"Aksoy","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, T., Zhou, P., Shao, Y., and Gao, S. (2017). Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements. Remote Sens., 9.","DOI":"10.3390\/rs9020104"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Transon, J., D\u2019Andrimont, R., Maugnard, A., and Defourny, P. (2018). Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sens., 10.","DOI":"10.3390\/rs10020157"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10712-018-9485-z","article-title":"Synergies of Spaceborne Imaging Spectroscopy with Other Remote Sensing Approaches","volume":"40","author":"Guanter","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_151","unstructured":"Song, X., Yan, G., Wang, J., Liu, L., Xue, X., Li, C., and Huang, W. (2007). Remote Sensing for Agriculture, Ecosystems, and Hydrology IX, SPIE. 67420M."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1080\/01904167.2021.1884702","article-title":"Determination of soil nutrients (NPK) using optical methods: A mini review","volume":"44","author":"Potdar","year":"2021","journal-title":"J. Plant Nutr."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"105520","DOI":"10.1016\/j.still.2022.105520","article-title":"Influence of soil physical and chemical properties on mechanical characteristics under different cultivation durations with Mollisols","volume":"224","author":"Lin","year":"2022","journal-title":"Soil Tillage Res."},{"key":"ref_154","first-page":"82","article-title":"Soil Density Evaluation Using Solid-State Lidar","volume":"2022","author":"Shoemaker","year":"2022","journal-title":"Geo-Congress"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Debnath, S., Paul, M., and Debnath, T. (2023). Applications of LiDAR in Agriculture and Future Research Directions. J. Imaging, 9.","DOI":"10.3390\/jimaging9030057"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"14662","DOI":"10.3390\/s131114662","article-title":"Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor","volume":"13","author":"Moreno","year":"2013","journal-title":"Sensors"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Li, C., Xu, Y., Liu, Z., Tao, S., Li, F., and Fang, J. (2016). Estimation of Forest Topsoil Properties Using Airborne LiDAR-Derived Intensity and Topographic Factors. Remote Sens., 8.","DOI":"10.3390\/rs8070561"},{"key":"ref_158","unstructured":"Lin, J., Wang, M., Zhang, M., Zhang, Y., and Chen, L. (2007). International Conference on Computer and Computing Technology in Agriculture, Wuyishan, China, 18\u201320 August 2007, Springer."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Vasques, G.M., Rodrigues, H.M., Coelho, M.R., Baca, J.F.M., Dart, R.O., Oliveira, R.P., Teixeira, W.G., and Ceddia, M.B. (2020). Field proximal soil sensor fusion for improving high-resolution soil property maps. Soil Syst., 4.","DOI":"10.3390\/soilsystems4030052"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"16263","DOI":"10.3390\/s131216263","article-title":"Proximal gamma-ray spectroscopy to predict soil properties using windows and full-spectrum analysis methods","volume":"13","author":"Mahmood","year":"2013","journal-title":"Sensors"},{"key":"ref_161","unstructured":"Veeke, S.v.d., Koomans, R., and Limburg, H. (2020, January 4\u20136). Using a gamma-ray spectrometer for soil moisture monitoring: Development of the the gamma Soil Moisture Sensor (gSMS). Proceedings of the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento, Italy."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Gray, P.C., Ridge, J.T., Poulin, S.K., Seymour, A.C., Schwantes, A.M., Swenson, J.J., and Johnston, D.W. (2018). Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments. Remote Sens., 10.","DOI":"10.3390\/rs10081257"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"2007764","DOI":"10.1002\/adma.202007764","article-title":"Soil Sensors and Plant Wearables for Smart and Precision Agriculture","volume":"33","author":"Yin","year":"2021","journal-title":"Adv. Mater."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"9278","DOI":"10.1109\/JIOT.2021.3056586","article-title":"A Self-Powered, Real-Time, LoRaWAN IoT-Based Soil Health Monitoring System","volume":"8","author":"Ramson","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1007\/s11277-019-06964-0","article-title":"(t,n): Sensor Stipulation with THAM Index for Smart Agriculture Decision-Making IoT System","volume":"111","author":"Mekala","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"104943","DOI":"10.1016\/j.compag.2019.104943","article-title":"Monitoring plant diseases and pests through remote sensing technology: A review","volume":"165","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2015.10.012","article-title":"Geospatial big data handling theory and methods: A review and research challenges","volume":"115","author":"Li","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/jimaging5050052","article-title":"Deep learning meets hyperspectral image analysis: A multidisciplinary review","volume":"5","author":"Signoroni","year":"2019","journal-title":"J. Imaging"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"11373","DOI":"10.1007\/s11227-021-04292-4","article-title":"IoHT-based deep learning controlled robot vehicle for paralyzed patients of smart cities","volume":"78","author":"Calp","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.apm.2019.12.016","article-title":"Selecting appropriate machine learning methods for digital soil mapping","volume":"81","author":"Khaledian","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.3390\/rs14051114","article-title":"Toward automated machine learning-based hyperspectral image analysis in crop yield and biomass estimation","volume":"14","author":"Li","year":"2022","journal-title":"Remote Sens."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"4883","DOI":"10.3390\/su13094883","article-title":"Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture","volume":"13","author":"Khan","year":"2021","journal-title":"Sustainability"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1007\/s11119-020-09711-9","article-title":"Remote sensing and machine learning for crop water stress determination in various crops: A critical review","volume":"21","author":"Virnodkar","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1038\/s41598-018-21530-7","article-title":"Georeferenced soil provenancing with digital signatures","volume":"8","author":"Tighe","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.3390\/rs11111350","article-title":"Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts","volume":"11","author":"Chamizo","year":"2019","journal-title":"Remote Sens."},{"key":"ref_176","first-page":"16","article-title":"A Synoptic Review on Deriving Bathymetry Information Using Remote Sensing Technologies: Models, Methods and Comparisons","volume":"4","author":"Jawak","year":"2015","journal-title":"Adv. Remote Sens."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"127470","DOI":"10.1016\/j.conbuildmat.2022.127470","article-title":"Infrared thermography for the investigation of physical\u2013chemical properties and thermal durability of Tunisian limestone rocks","volume":"339","author":"Mezza","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"120579","DOI":"10.1016\/j.apenergy.2022.120579","article-title":"Remote sensing of photovoltaic scenarios: Techniques, applications and future directions","volume":"333","author":"Chen","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3390\/mti4030046","article-title":"A Multimodal Facial Emotion Recognition Framework through the Fusion of Speech with Visible and Infrared Images","volume":"4","author":"Siddiqui","year":"2020","journal-title":"Multimodal Technol. Interact."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.02.002","article-title":"Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters","volume":"150","author":"Schwarz","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.rse.2015.07.019","article-title":"Distribution of near-surface permafrost in Alaska: Estimates of present and future conditions","volume":"168","author":"Pastick","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.rse.2016.08.018","article-title":"Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences","volume":"186","author":"Eitel","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"105701","DOI":"10.1016\/j.marenvres.2022.105701","article-title":"Ocean water quality monitoring using remote sensing techniques: A review","volume":"180","author":"Mohseni","year":"2022","journal-title":"Mar. Environ. Res."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.wasman.2020.12.019","article-title":"Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics","volume":"122","author":"Mallet","year":"2021","journal-title":"Waste Manag."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"3879","DOI":"10.3390\/rs6053879","article-title":"Supporting global environmental change research: A review of trends and knowledge gaps in urban remote sensing","volume":"6","author":"Wentz","year":"2014","journal-title":"Remote Sens."},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Boccardo, P., and Giulio Tonolo, F. (2015). Remote Sensing Role in Emergency Mapping for Disaster Response, Springer.","DOI":"10.1007\/978-3-319-09048-1_3"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40677-017-0073-1","article-title":"Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning","volume":"4","author":"Casagli","year":"2017","journal-title":"Geoenvironmental Disasters"},{"key":"ref_188","first-page":"391","article-title":"Remote Sensing Techniques for Bridge Deformation Monitoring at Millimetric Scale: Investigating the Potential of Satellite Radar Interferometry, Airborne Laser Scanning and Ground-Based Mobile Laser Scanning","volume":"90","author":"Dorninger","year":"2022","journal-title":"PFG J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Dainelli, R., Toscano, P., Di Gennaro, S.F., and Matese, A. (2021). Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing\u2014A Systematic Review. Part II: Research Applications. Forests, 12.","DOI":"10.3390\/f12040397"},{"key":"ref_190","first-page":"19","article-title":"Recent trends and long-standing problems in archaeological remote sensing","volume":"1","author":"Opitz","year":"2018","journal-title":"J. Comput. Appl. Archaeol."},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Pande, C.B., and Moharir, K.N. (2023). Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems, Springer International Publishing.","DOI":"10.1007\/978-3-031-19059-9"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"41","DOI":"10.24057\/2071-9388-2020-117","article-title":"Monitoring land use and land cover changes using geospatial techniques, a case study of Fateh Jang, Attock, Pakistan","volume":"14","author":"Tariq","year":"2021","journal-title":"Geogr. Environ. Sustain."},{"key":"ref_193","doi-asserted-by":"crossref","unstructured":"Gantimurova, S., Parshin, A., and Erofeev, V. (2021). GIS-Based Landslide Susceptibility Mapping of the Circum-Baikal Railway in Russia Using UAV Data. Remote Sens., 13.","DOI":"10.3390\/rs13183629"},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s13762-021-03801-5","article-title":"UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: A meta-review","volume":"20","author":"Awais","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2019.03.014","article-title":"Simultaneous calibration of multiple hydrodynamic model parameters using satellite altimetry observations of water surface elevation in the Songhua River","volume":"225","author":"Jiang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"810214","DOI":"10.3389\/fpls.2021.810214","article-title":"Minimalizing Non-point Source Pollution Using a Cooperative Ion-Selective Electrode System for Estimating Nitrate Nitrogen in Soil","volume":"12","author":"Su","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_197","doi-asserted-by":"crossref","unstructured":"Neuendorf, F., Thiele, J., Albert, C., and von Haaren, C. (2021). Uncertainties in land use data may have substantial effects on environmental planning recommendations: A plea for careful consideration. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0260302"},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1002\/2014RG000456","article-title":"Remote sensing of drought: Progress, challenges and opportunities","volume":"53","author":"AghaKouchak","year":"2015","journal-title":"Rev. Geophys."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.3390\/rs10091465","article-title":"Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach","volume":"10","author":"Clifton","year":"2018","journal-title":"Remote Sens."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"5627","DOI":"10.5194\/hess-26-5627-2022","article-title":"On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River","volume":"26","author":"Xiao","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"107119","DOI":"10.1016\/j.compag.2022.107119","article-title":"Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming","volume":"198","author":"Rasool","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10661-019-7996-9","article-title":"Utilizing geospatial information to implement SDGs and monitor their Progress","volume":"192","author":"Avtar","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"103001","DOI":"10.1088\/1748-9326\/abaad7","article-title":"Remote sensing of forest degradation: A review","volume":"15","author":"Gao","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"e456","DOI":"10.1002\/wcc.456","article-title":"Research methods for exploring the links between climate change and conflict","volume":"8","author":"Ide","year":"2017","journal-title":"WIREs Clim. Chang."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"e1968","DOI":"10.1002\/pa.1968","article-title":"A review: The role of geospatial technology in precision agriculture","volume":"20","author":"Praveen","year":"2020","journal-title":"J. Public Aff."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"110209","DOI":"10.1109\/ACCESS.2021.3102227","article-title":"Big Data and AI Revolution in Precision Agriculture: Survey and Challenges","volume":"9","author":"Bhat","year":"2021","journal-title":"IEEE Access"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"4557","DOI":"10.1007\/s11831-022-09761-4","article-title":"Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk","volume":"29","author":"Shaikh","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"124351","DOI":"10.1016\/j.jhydrol.2019.124351","article-title":"Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S","volume":"580","author":"Yuan","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"842930","DOI":"10.3389\/fsufs.2022.842930","article-title":"A review of nutrient losses to waters from soil-and ground-based urban agriculture\u2014More nutrient balances than measurements","volume":"6","author":"Tonderski","year":"2022","journal-title":"Front. Sustain. Food Syst."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"112223","DOI":"10.1016\/j.rse.2020.112223","article-title":"Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland","volume":"253","author":"Sankey","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3390\/cli7010005","article-title":"Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat","volume":"7","author":"Shandas","year":"2019","journal-title":"Climate"},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"111215","DOI":"10.1016\/j.rse.2019.111215","article-title":"Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations","volume":"231","author":"Ma","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1109\/TGRS.2016.2553085","article-title":"A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements","volume":"54","author":"Zeng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2071-1050\/15\/21\/15444\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:14:21Z","timestamp":1760130861000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2071-1050\/15\/21\/15444"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":215,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["su152115444"],"URL":"https:\/\/doi.org\/10.3390\/su152115444","relation":{},"ISSN":["2071-1050"],"issn-type":[{"value":"2071-1050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]}}}