{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T05:22:06Z","timestamp":1773033726127,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004811","name":"World Meteorological Organization","doi-asserted-by":"publisher","award":["20170888"],"award-info":[{"award-number":["20170888"]}],"id":[{"id":"10.13039\/501100004811","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model\u2014the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets\u2014ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015\u20132021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3\u20130.9. We conducted a case study of the 2015 El Ni\u00f1o and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific.<\/jats:p>","DOI":"10.3390\/rs14163971","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T03:15:27Z","timestamp":1660706127000},"page":"3971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Evaluating Satellite Soil Moisture Datasets for Drought Monitoring in Australia and the South-West Pacific"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8323-6638","authenticated-orcid":false,"given":"Jessica","family":"Bhardwaj","sequence":"first","affiliation":[{"name":"Bureau of Meteorology, Docklands 3008, Australia"},{"name":"SPACE Research Centre, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia"}]},{"given":"Yuriy","family":"Kuleshov","sequence":"additional","affiliation":[{"name":"Bureau of Meteorology, Docklands 3008, Australia"},{"name":"SPACE Research Centre, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia"}]},{"given":"Zhi-Weng","family":"Chua","sequence":"additional","affiliation":[{"name":"Bureau of Meteorology, Docklands 3008, Australia"},{"name":"SPACE Research Centre, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia"}]},{"given":"Andrew B.","family":"Watkins","sequence":"additional","affiliation":[{"name":"Bureau of Meteorology, Docklands 3008, Australia"}]},{"given":"Suelynn","family":"Choy","sequence":"additional","affiliation":[{"name":"SPACE Research Centre, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5421-5838","authenticated-orcid":false,"given":"Qian (Chayn)","family":"Sun","sequence":"additional","affiliation":[{"name":"SPACE Research Centre, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne 3000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1038\/nature06591","article-title":"Terrestrial Ecosystem Carbon Dynamics and Climate Feedbacks","volume":"451","author":"Heimann","year":"2008","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1029\/2018RG000618","article-title":"Ground, Proximal, and Satellite Remote Sensing of Soil Moisture","volume":"57","author":"Babaeian","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2016.10.0105","article-title":"Soil Moisture Remote Sensing: State-of-the-Science","volume":"16","author":"Mohanty","year":"2017","journal-title":"Vadose Zone J."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2012.0170","article-title":"Characterizing Coarse-Scale Representativeness of in Situ Soil Moisture Measurements from the International Soil Moisture Network","volume":"12","author":"Gruber","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s41976-019-00025-7","article-title":"A Review of Satellite-Derived Soil Moisture and Its Usage for Flood Estimation","volume":"2","author":"Kim","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.1109\/36.789610","article-title":"Soil Moisture Mapping at Regional Scales Using Microwave Radiometry: The Southern Great Plains Hydrology Experiment","volume":"37","author":"Jackson","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kuleshov, Y., Kurino, T., Kubota, T., Tashima, T., and Xie, P. (2019). WMO Space-Based Weather and Climate Extremes Monitoring Demonstration Project (SEMDP): First Outcomes of Regional Cooperation on Drought and Heavy Precipitation Monitoring for Australia and Southeast Asia. Rainfall\u2014Extremes, Distribution and Properties, IntechOpen.","DOI":"10.5772\/intechopen.85824"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8377","DOI":"10.1175\/JCLI-D-16-0332.1","article-title":"Trends and Variability in Droughts in the Pacific Islands and Northeast Australia","volume":"29","author":"McGree","year":"2016","journal-title":"J. Clim."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"289","DOI":"10.3389\/fmars.2019.00289","article-title":"Lessons from the Pacific Islands\u2014Adapting to Climate Change by Supporting Social and Ecological Resilience","volume":"6","author":"Mcleod","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1175\/BAMS-D-15-00219.1","article-title":"When El Ni\u00f1o Rages: How Satellite Data Can Help Water-Stressed Islands","volume":"97","author":"Luchetti","year":"2016","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/00664677.2019.1647832","article-title":"In the Time of Frost: El Ni\u00f1o and the Political Ecology of Vulnerability in Papua New Guinea","volume":"30","author":"Jacka","year":"2020","journal-title":"Anthropol. Forum"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4015","DOI":"10.1175\/JCLI-D-13-00130.1","article-title":"The Varied Impacts of El Ni\u00f1o\u2013Southern Oscillation on Pacific Island Climates","volume":"27","author":"Murphy","year":"2014","journal-title":"J. Clim."},{"key":"ref_14","unstructured":"Intergovernmental Panel on Climate Change (IPCC), Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., P\u00e9an, C., Berger, S., Caud, N., Chen, Y., and Goldfarb, L. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chua, Z.-W., Kuleshov, Y., and Watkins, A.B. (2020). Drought Detection over Papua New Guinea Using Satellite-Derived Products. Remote Sens., 12.","DOI":"10.3390\/rs12233859"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wild, A., Chua, Z.-W., and Kuleshov, Y. (2021). Evaluation of Satellite Precipitation Estimates over the South West Pacific Region. Remote Sens., 13.","DOI":"10.3390\/rs13193929"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100319","DOI":"10.1016\/j.wace.2021.100319","article-title":"Updated Analysis of Gauge-Based Rainfall Patterns over the Western Tropical Pacific Ocean","volume":"32","author":"Wimhurst","year":"2021","journal-title":"Weather Clim. Extrem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100262","DOI":"10.1016\/j.wace.2020.100262","article-title":"Factors Affecting Extreme Rainfall Events in the South Pacific","volume":"29","author":"Pariyar","year":"2020","journal-title":"Weather Clim. Extrem."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0034-4257(99)00036-X","article-title":"A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data","volume":"70","author":"Wagner","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/JPROC.2010.2043032","article-title":"The SMOS L: New Tool for Monitoring Key Elements Ofthe Global Water Cycle","volume":"98","author":"Kerr","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhan, X., Hain, C., Yin, J., Fang, L., Li, Z., and Zhao, L. (2016, January 10\u201315). NOAA Soil Moisture Operational Product System (SMOPS) and Its Validations. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729899"},{"key":"ref_22","unstructured":"De Jeu, R., Owe, M., GES DISC, and Teng, B. (2014). AMSR2\/GCOM-W1 Surface Soil Moisture (LPRM) L3 1 Day 10 Km \u00d7 10 Km Descending V001."},{"key":"ref_23","unstructured":"O\u2019Neill, P.E., Chan, S., Njoku, E.G., Jackson, T., Bindlish, R., and Chaubell, J. (2020). SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 7."},{"key":"ref_24","unstructured":"Frost, A.J., Ramchurn, A., and Smith, A. (2018). The Australian Landscape Water Balance Model (AWRA-L v6), Bureau of Meteorology Technical Report."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The Global Land Data Assimilation System","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_26","unstructured":"Mu\u00f1oz Sabater, J. (2022, June 06). ERA5-Land Monthly Averaged Data from 1950 to 1980. Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-land-monthly-means?tab=overview."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"W07701","DOI":"10.1029\/2012WR011976","article-title":"The Murrumbidgee Soil Moisture Monitoring Network Data Set","volume":"48","author":"Smith","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5029","DOI":"10.1002\/2013WR015138","article-title":"Calibration and Correction Procedures for Cosmic-Ray Neutron Soil Moisture Probes Located across Australia","volume":"50","author":"Hawdon","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5895","DOI":"10.5194\/bg-13-5895-2016","article-title":"An Introduction to the Australian and New Zealand Flux Tower Network\u2014OzFlux","volume":"13","author":"Beringer","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4394","DOI":"10.1002\/2015GL064125","article-title":"Microwave Remote Sensing of Short-Term Droughts during Crop Growing Seasons","volume":"42","author":"Yuan","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","unstructured":"Integrated Climate Data Center (ICDC) (2022, June 06). Globally Gridded Monthly Mean ASCAT Soil Moisture Maps. Available online: https:\/\/www.fdr.uni-hamburg.de\/record\/10196."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, X., Lu, G., Wu, Z., He, H., Scanlon, T., and Dorigo, W. (2020). Triple Collocation-Based Assessment of Satellite Soil Moisture Products with in Situ Measurements in China: Understanding the Error Sources. Remote Sens., 12.","DOI":"10.3390\/rs12142275"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.rse.2016.02.045","article-title":"SMOS Disaggregated Soil Moisture Product at 1 Km Resolution: Processor Overview and First Validation Results","volume":"180","author":"Molero","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"Zhan, X., Liu, J., and Zhao, L. (2016). Soil Moisture Operational Product System (SMOPS): Algorithm Theoretical Basis Document\u2014Version 4.0."},{"key":"ref_35","unstructured":"Pablos, M., Gonz\u00e1lez-Haro, C., Piles, M., and BEC Team (2022, June 06). BEC SMOS Soil Moisture Products Description (V. 1.0). Available online: https:\/\/bec.icm.csic.es\/data\/available-products\/#SoilMoisture."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Owe, M., de Jeu, R., and Holmes, T. (2008). Multisensor Historical Climatology of Satellite-Derived Global Land Surface Moisture. J. Geophys. Res. Earth Surf., 113.","DOI":"10.1029\/2007JF000769"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"12171","DOI":"10.1038\/s41598-018-30669-2","article-title":"Detecting the Causal Effect of Soil Moisture on Precipitation Using Convergent Cross Mapping","volume":"8","author":"Wang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e2019EA000841","DOI":"10.1029\/2019EA000841","article-title":"Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques","volume":"7","author":"Mousa","year":"2020","journal-title":"Earth Sp. Sci."},{"key":"ref_39","unstructured":"(2022, June 06). Bureau of Meteorology Climate Classification Maps (K\u00f6ppen\u2014Major Classes), Available online: http:\/\/www.bom.gov.au\/jsp\/ncc\/climate_averages\/climate-classifications\/index.jsp?maptype=kpngrp#maps."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.rse.2016.09.015","article-title":"Comparison of Remotely Sensed and Modelled Soil Moisture Data Sets across Australia","volume":"186","author":"Holgate","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Scipal, K., Holmes, T., De Jeu, R., Naeimi, V., and Wagner, W. (2008). A Possible Solution for the Problem of Estimating the Error Structure of Global Soil Moisture Data Sets. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL035599"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7755","DOI":"10.1029\/97JC03180","article-title":"Toward the True Near-Surface Wind Speed: Error Modeling and Calibration Using Triple Collocation","volume":"103","author":"Stoffelen","year":"1998","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.rse.2013.06.013","article-title":"Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains","volume":"137","author":"Draper","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6229","DOI":"10.1002\/2014GL061322","article-title":"Extended Triple Collocation: Estimating Errors and Correlation Coefficients with Respect to an Unknown Target","volume":"41","author":"McColl","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ming, W., Ji, X., Zhang, M., Li, Y., Liu, C., Wang, Y., and Li, J. (2022). A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data. Remote Sens., 14.","DOI":"10.3390\/rs14071744"},{"key":"ref_46","unstructured":"NOAA National Geophysical Data Center (2009). ETOPO1 1 Arc-Minute Global Relief Model."},{"key":"ref_47","unstructured":"National Aeronautics and Space Administration (NASA) (2022, June 06). How Can I Obtain Volumetric Soil Moisture [M3 m\u22123] from the LDAS Data?, Available online: https:\/\/ldas.gsfc.nasa.gov\/faq\/LDAS#:~:text=To convert to units of,%5Bkg m-3%5D."},{"key":"ref_48","unstructured":"McKee, T.B. (1995, January 15\u201320). Drought Monitoring with Multiple Time Scales. Proceedings of the 9th Conference on Applied Climatology, Boston, MA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1175\/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2","article-title":"Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data","volume":"76","author":"Kogan","year":"1995","journal-title":"Bull.-Am. Meteorol. Soc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1111\/1467-8489.12404","article-title":"Poverty Analysis in the Lowlands of Papua New Guinea Underscores Climate Vulnerability and Need for Income Flexibility","volume":"65","author":"Schmidt","year":"2021","journal-title":"Aust. J. Agric. Resour. Econ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/LGRS.2019.2940633","article-title":"Soil Moisture Information Content in SMOS, SMAP, AMSR2, and ASCAT Level-1 Data over Selected in Situ Sites","volume":"17","author":"Link","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1109\/TGRS.2012.2184548","article-title":"The SMOS Soil Moisture Retrieval Algorithm","volume":"50","author":"Kerr","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Montzka, C., Bogena, H.R., Zreda, M., Monerris, A., Morrison, R., Muddu, S., and Vereecken, H. (2017). Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes. Remote Sens., 9.","DOI":"10.3390\/rs9020103"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.rse.2016.05.008","article-title":"Error Decomposition of Nine Passive and Active Microwave Satellite Soil Moisture Data Sets over Australia","volume":"182","author":"Su","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2011.11.017","article-title":"Evaluation of Remotely Sensed and Modelled Soil Moisture Products Using Global Ground-Based in Situ Observations","volume":"118","author":"Albergel","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1127\/0941-2948\/2013\/0399","article-title":"The ASCAT Soil Moisture Product: A Review of Its Specifications, Validation Results, and Emerging Applications","volume":"22","author":"Wagner","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_57","unstructured":"Frost, A.J., and Shokri, A. (2021). The Australian Landscape Water Balance Model (AWRA-L v7), Technical Description of the Australian Water Resources Assessment Landscape Model Version 7."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"17","DOI":"10.5194\/hess-25-17-2021","article-title":"Evaluation of 18 Satellite\u2014And Model-Based Soil Moisture Products Using in Situ Measurements from 826 Sensors","volume":"25","author":"Beck","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bhardwaj, J., Kuleshov, Y., Chua, Z.-W., Watkins, A.B., Choy, S., and Sun, Q. (2021). Building Capacity for a User-Centred Integrated Early Warning System for Drought in Papua New Guinea. Remote Sens., 13.","DOI":"10.3390\/rs13163307"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/00167487.2012.12094332","article-title":"Indigenous Knowledge and Disaster Risk Reduction","volume":"97","author":"Ilan","year":"2012","journal-title":"Geography"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1175\/WCAS-D-16-0094.1","article-title":"The Role of Traditional Knowledge in Building Adaptive Capacity for Climate Change: Perspectives from Vanuatu","volume":"9","author":"Granderson","year":"2017","journal-title":"Weather. Clim. Soc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3971\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:09:19Z","timestamp":1760141359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,16]]},"references-count":61,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14163971"],"URL":"https:\/\/doi.org\/10.3390\/rs14163971","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,16]]}}}