{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T23:21:53Z","timestamp":1778628113214,"version":"3.51.4"},"reference-count":68,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T00:00:00Z","timestamp":1647820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution Earth observation data is routinely used to monitor tropical forests. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. In this study, we demonstrate the potential of combining Sentinel-1 (S1) SAR and Sentinel-2 (S2) optical sensors in order to map the tree cover in East Africa. The overall methodology consists of: (i) the generation of S1 and S2 layers, (ii) the collection of an expert-based training\/validation dataset and (iii) the classification of the satellite data. Three different classification workflows, together with different approaches to incorporating the spatial information to train the classifiers, are explored. Two types of maps were derived from these mapping approaches over Tanzania: (i) binary tree cover\u2013no tree cover (TC\/NTC) maps, and (ii) maps of the canopy cover classes. The overall accuracy of the maps is &gt;95% for the TC\/NTC maps and &gt;85% for the forest types maps. Considering the neighboring pixels for training the classification improved the mapping of the areas that are covered by 1\u201310% tree cover. The study relied on open data and publicly available tools and can be integrated into national monitoring systems.<\/jats:p>","DOI":"10.3390\/rs14061522","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T21:48:42Z","timestamp":1647899322000},"page":"1522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Mapping Canopy Cover in African Dry Forests from the Combined Use of Sentinel-1 and Sentinel-2 Data: Application to Tanzania for the Year 2018"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4234-0586","authenticated-orcid":false,"given":"Astrid","family":"Verhegghen","sequence":"first","affiliation":[{"name":"Joint Research Centre (JRC), Directorate D\u2014Sustainable Resources, European Commission, Via E. Fermi, 2749, I-21027 Ispra, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klara","family":"Kuzelova","sequence":"additional","affiliation":[{"name":"ARHS Developments S.A., 4370 Esch-sur-Alzette, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2262-0580","authenticated-orcid":false,"given":"Vasileios","family":"Syrris","sequence":"additional","affiliation":[{"name":"Joint Research Centre (JRC), Directorate D\u2014Sustainable Resources, European Commission, Via E. Fermi, 2749, I-21027 Ispra, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugh","family":"Eva","sequence":"additional","affiliation":[{"name":"Joint Research Centre (JRC), Directorate D\u2014Sustainable Resources, European Commission, Via E. Fermi, 2749, I-21027 Ispra, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Achard","sequence":"additional","affiliation":[{"name":"Joint Research Centre (JRC), Directorate D\u2014Sustainable Resources, European Commission, Via E. Fermi, 2749, I-21027 Ispra, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sandker, M., Carrillo, O., Leng, C., Lee, D., D\u2019Annunzio, R., and Fox, J. (2021). The Importance of High\u2013Quality Data for REDD+ Monitoring and Reporting. Forests, 12.","DOI":"10.3390\/f12010099"},{"key":"ref_2","unstructured":"(2017). Voluntary Guidelines on National Forest Monitoring, Food and Agriculture Organization of the United Nations."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"054029","DOI":"10.1088\/1748-9326\/abd81b","article-title":"An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005\u20132020","volume":"16","author":"Nesha","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_4","unstructured":"(2021, January 09). UNFCCC National Forest Monitoring System. Available online: https:\/\/redd.unfccc.int\/fact-sheets\/national-forest-monitoring-system.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"eabe1603","DOI":"10.1126\/sciadv.abe1603","article-title":"Long-term (1990\u20132019) monitoring of forest cover changes in the humid tropics","volume":"7","author":"Vancutsem","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2014.08.017","article-title":"Global, Landsat-based forest-cover change from 1990 to 2000","volume":"155","author":"Kim","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"15","article-title":"Mapping and estimating land change between 2001 and 2013 in a heterogeneous landscape in West Africa: Loss of forestlands and capacity building opportunities","volume":"63","author":"Badjana","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2017.12.030","article-title":"An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR","volume":"206","author":"Bouvet","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1126\/science.aam6527","article-title":"The extent of forest in dryland biomes","volume":"356","author":"Bastin","year":"2017","journal-title":"Science"},{"key":"ref_11","unstructured":"(2020, June 12). INPE Projeto Prodes\u2014Monitoramento Da Floresta Amaz\u00f4nica Brasileira Por Sat\u00e9lite. Available online: http:\/\/www.obt.inpe.br\/OBT\/assuntos\/programas\/amazonia\/prodes."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"GB2008","DOI":"10.1029\/2003GB002142","article-title":"Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s","volume":"18","author":"Achard","year":"2004","journal-title":"Global Biogeochem. Cycles"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2016.01.006","article-title":"Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision","volume":"175","author":"Solberg","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.rse.2018.06.044","article-title":"Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data","volume":"216","author":"Baumann","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Timberlake, J., Chidumayo, E., and Sawadogo, L. (2010). Distribution and Characteristics of African Dry Forests and Woodlands. The Dry Forests and Woodlands of Africa: Managing for Products and Services, Earthscan.","DOI":"10.4324\/9781849776547"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1177\/0309133307076107","article-title":"Tropical savannas and associated forests: Vegetation and plant ecology","volume":"31","author":"Furley","year":"2007","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_17","unstructured":"FAO (2019). Trees, Forests and Land Use in Drylands: The First Global Assessment, Food and Agriculture Organization of the United Nations."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.rse.2017.10.034","article-title":"Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2","volume":"204","author":"Reiche","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Odongo-Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., and Pickens, A. (2021). Forest disturbance alerts for the Congo Basin using Sentinel-1. Environ. Res. Lett., 16.","DOI":"10.1088\/1748-9326\/abd0a8"},{"key":"ref_20","first-page":"417","article-title":"Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program","volume":"40","author":"Belward","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3389\/fenvs.2020.00004","article-title":"Toward Operational Mapping of Woody Canopy Cover in Tropical Savannas Using Google Earth Engine","volume":"8","author":"Anchang","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1002\/rse2.139","article-title":"Combining optical and radar satellite image time series to map natural vegetation: Savannas as an example","volume":"6","author":"Lopes","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111465","DOI":"10.1016\/j.rse.2019.111465","article-title":"From woody cover to woody canopies: How Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas","volume":"234","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shimizu, K., Ota, T., and Mizoue, N. (2019). Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests. Remote Sens., 11.","DOI":"10.3390\/rs11161899"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Heckel, K., Urban, M., Schratz, P., Mahecha, M., and Schmullius, C. (2020). Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12020302"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111611","DOI":"10.1016\/j.rse.2019.111611","article-title":"Mapping smallholder and large-scale cropland dynamics with a flexible classification system and pixel-based composites in an emerging frontier of Mozambique","volume":"239","author":"Bey","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.landusepol.2018.11.049","article-title":"Open-access cloud resources contribute to mainstream REDD+: The case of Mozambique","volume":"82","author":"Lopes","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"150","DOI":"10.3389\/fenvs.2019.00150","article-title":"Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities","volume":"7","author":"Saah","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_30","first-page":"101979","article-title":"Primitives as building blocks for constructing land cover maps","volume":"85","author":"Saah","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","unstructured":"McSweeney, C., New, M., and Lizcano, G. (2021, December 01). UNDP Climate Change Country Profiles: Tanzania. Available online: https:\/\/digital.library.unt.edu\/ark:\/67531\/metadc226754\/."},{"key":"ref_32","unstructured":"MWE (2014). Tanzania Second National Communication to the United Nations Framework Convention on Climate Change, Division of Environment Tanzania."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.egypro.2011.09.036","article-title":"Review of Biomass Energy Dependency in Tanzania","volume":"9","author":"Felix","year":"2011","journal-title":"Energy Procedia"},{"key":"ref_34","first-page":"8","article-title":"Moist forests of Tanzania","volume":"8","author":"Lovett","year":"1985","journal-title":"Swara"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1111\/j.1095-8312.1998.tb00337.x","article-title":"Coastal forests of eastern Africa: Status, endemism patterns and their potential causes","volume":"64","author":"Burgess","year":"1998","journal-title":"Biol. J. Linn. Soc."},{"key":"ref_36","unstructured":"Isango, J. (2007, January 6\u201312). Stand Structure and Tree Species Composition of Tanzania Miombo Woodlands: A Case Study from Miombo Woodlands of Community Based Forest Management in Iringa District; Working Papers of the Finnish Forest Research Institute. Proceedings of the 1st MITIMIOMBO Project Workshop, Morogoro, Tanzania."},{"key":"ref_37","unstructured":"FAO (2015). Global Forest Resources Assessment 2015, Food and Agriculture Organization of the United Nations."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Housman, I.W., Chastain, R.A., and Finco, M.V. (2018). An evaluation of forest health insect and disease survey data and satellite-based remote sensing forest change detection methods: Case studies in the United States. Remote Sens., 10.","DOI":"10.20944\/preprints201805.0360.v1"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112232","DOI":"10.1016\/j.rse.2020.112232","article-title":"Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2","volume":"253","author":"Meroni","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2013A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"39","article-title":"Tropical forest cover density mapping","volume":"43","author":"Rikimaru","year":"2002","journal-title":"Trop. Ecol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hojas Gasc\u00f3n, L., Ceccherini, G., Garc\u00eda Haro, F., Avitabile, V., and Eva, H. (2019). The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania. Forests, 10.","DOI":"10.3390\/f10020107"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"(2021, September 21). ESA Sentinel-1 SAR User Guide. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-1-sar."},{"key":"ref_48","unstructured":"(2021, September 21). ESA The Sentinel-1 Toolbox\u2014Version 7. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/866toolboxes\/sentinel-1."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Frison, P.L., Fruneau, B., Kmiha, S., Soudani, K., Dufr\u00eane, E., Le Toan, T., Koleck, T., Villard, L., Mougin, E., and Rudant, J.P. (2018). Potential of Sentinel-1 data for monitoring temperate mixed forest phenology. Remote Sens., 10.","DOI":"10.3390\/rs10122049"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Schaepman, M.E., and Small, D. (2017). Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland. Remote Sens., 10.","DOI":"10.3390\/rs10010055"},{"key":"ref_51","unstructured":"(2015). National Forest Resources Monitoring and Assessment of Tanzania Mainland (NAFORMA), Main results."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13021-019-0120-1","article-title":"Carbon stocks for different land cover types in Mainland Tanzania","volume":"14","author":"Mauya","year":"2019","journal-title":"Carbon Balance Manag."},{"key":"ref_53","unstructured":"FAO (2016). Map Accuracy Assessment and Area Estimation Map Accuracy Assessment and Area Estimation: A Practical Guide. National Forest Monitoring Assessment Working Paper, Food and Agriculture Organization of the United Nations."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bey, A., S\u00e1nchez-Paus D\u00edaz, A., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., Bastin, J.-F., Moore, R., Federici, S., and Rezende, M. (2016). Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation. Remote Sens., 8.","DOI":"10.3390\/rs8100807"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10712-019-09533-z","article-title":"Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery","volume":"40","author":"Schepaschenko","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.future.2017.11.007","article-title":"A versatile data-intensive computing platform for information retrieval from big geospatial data","volume":"81","author":"Soille","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_57","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0034-4257(98)00010-8","article-title":"Design and analysis for thematic map accuracy assessment: Fundamental principles","volume":"64","author":"Stehman","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1016\/j.rse.2011.06.007","article-title":"Pixels, blocks of pixels, and polygons: Choosing a spatial unit for thematic accuracy assessment","volume":"115","author":"Stehman","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112470","DOI":"10.1016\/j.rse.2021.112470","article-title":"Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record","volume":"261","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lima, T.A. (2019). Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon. Remote Sens., 8.","DOI":"10.3390\/rs11080961"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Verhegghen, A., Eva, H., Ceccherini, G., Achard, F., Gond, V., Gourlet-Fleury, S., and Cerutti, P. (2016). The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests. Remote Sens., 8.","DOI":"10.3390\/rs8120986"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"107488","DOI":"10.1016\/j.dib.2021.107488","article-title":"Pan-Tropical Sentinel-2 Cloud-Free Annual Composite","volume":"39","author":"Simonetti","year":"2021","journal-title":"Data Brief"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1007\/s10661-015-4817-7","article-title":"Monitoring trees outside forests: A review","volume":"187","author":"Schnell","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/s41586-020-2824-5","article-title":"An unexpectedly large count of trees in the West African Sahara and Sahel","volume":"587","author":"Brandt","year":"2020","journal-title":"Nature"},{"key":"ref_67","unstructured":"FAO (2011). Assessing forest degradation\u2014Towards the development of globally applicable guidelines. Forest Resources Assessment Working Paper 117, Food and Agriculture Organization of the United Nations."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Langner, A., Miettinen, J., Kukkonen, M., Vancutsem, C., Simonetti, D., Vieilledent, G., Verhegghen, A., Gallego, J., and Stibig, H.J. (2018). Towards operational monitoring of forest canopy disturbance in evergreen rain forests: A test case in continental Southeast Asia. Remote Sens., 10.","DOI":"10.3390\/rs10040544"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:40:38Z","timestamp":1760136038000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,21]]},"references-count":68,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061522"],"URL":"https:\/\/doi.org\/10.3390\/rs14061522","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,21]]}}}