{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:36:10Z","timestamp":1760229370304,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFE0124200"],"award-info":[{"award-number":["2018YFE0124200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The spatial representativeness of the in-situ data is an important prerequisite for ensuring the reliability and accuracy of remote sensing product retrieval and verification. Limited by the collection cost and time window, it is essential to simultaneously collect multiple water parameter data in water tests. In the shipboard measurements, sampling design faces problems, such as heterogeneity of water quality multi-parameter spatial distribution and variability of sampling plan under multiple constraints. Aiming at these problems, a water multi-parameter sampling design method is proposed. This method constructs a regional multi-parameter weighted space based on the single-parameter sampling design and performs adaptive weighted fusion according to the spatial variation trend of each water parameter within it to obtain multi-parameter optimal sampling points. The in-situ datasets of three water parameters (chlorophyll a, total suspended matter, and Secchi-disk Depth) were used to test the spatial representativeness of the sampling method. The results showed that the sampling method could give the sampling points an excellent spatial representation in each water parameter. This method can provide a fast and efficient sampling design for in-situ data for water parameters, thereby reducing the uncertainty of inversion and the validation of water remote sensing products.<\/jats:p>","DOI":"10.3390\/rs14122780","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Water Multi-Parameter Sampling Design Method Based on Adaptive Sample Points Fusion in Weighted Space"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8338-2494","authenticated-orcid":false,"given":"Mingjian","family":"Zhai","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8369-4452","authenticated-orcid":false,"given":"Zui","family":"Tao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8913-1837","authenticated-orcid":false,"given":"Tingting","family":"Lv","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9962-9401","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0542-7455","authenticated-orcid":false,"given":"Ruoxi","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/17538947.2015.1026420","article-title":"A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic\u2013spectral classification algorithm","volume":"9","author":"Feng","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1038\/s43017-020-0067-5","article-title":"Global lake responses to climate change","volume":"1","author":"Woolway","year":"2020","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ecolind.2015.12.009","article-title":"Remote sensing for lake research and monitoring\u2013Recent advances","volume":"64","author":"Oppelt","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1080\/02626667.2018.1552001","article-title":"Using new remote sensing satellites for assessing water quality in a reservoir","volume":"64","author":"Bonansea","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Aires, F., Venot, J.-P., Massuel, S., Gratiot, N., Pham-Duc, B., and Prigent, C. (2020). Surface water evolution (2001\u20132017) at the Cambodia\/Vietnam border in the upper mekong delta using satellite MODIS observations. Remote Sens., 12.","DOI":"10.3390\/rs12050800"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s13201-018-0660-7","article-title":"Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data","volume":"8","author":"Rai","year":"2018","journal-title":"Appl. Water Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, X., and Xie, H. (2018). A review on applications of remote sensing and geographic information systems (GIS) in water resources and flood risk management. Water, 10.","DOI":"10.3390\/w10050608"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.5194\/essd-12-1141-2020","article-title":"Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018","volume":"12","author":"Tortini","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41597-021-00807-z","article-title":"A dataset of remote-sensed Forel-Ule Index for global inland waters during 2000\u20132018","volume":"8","author":"Wang","year":"2021","journal-title":"Sci. Data"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1080\/20964129.2019.1571443","article-title":"Water quality monitoring and evaluation using remote sensing techniques in China: A systematic review","volume":"5","author":"Wang","year":"2019","journal-title":"Ecosyst. Health Sustain."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.watres.2019.115162","article-title":"Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing","volume":"168","author":"Xu","year":"2020","journal-title":"Water Res."},{"key":"ref_12","unstructured":"Zang, W., Lin, J., Wang, Y., and Tao, H. (2012, January 24\u201328). Investigating small-scale water pollution with UAV remote sensing technology. Proceedings of the World Automation Congress, Puerto Vallarta, Mexico."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.rse.2018.12.007","article-title":"Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations","volume":"221","author":"Feng","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Palmer, S.C., Kutser, T., and Hunter, P.D. (2015). Remote Sensing of Inland Waters: Challenges, Progress and Future Directions, Elsevier.","DOI":"10.1016\/j.rse.2014.09.021"},{"key":"ref_15","first-page":"630","article-title":"Key methods and experiment verification for the validation of quantitative remote sensing products","volume":"6","author":"Rui","year":"2017","journal-title":"Adv. Earth Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1080\/014311600750020000","article-title":"Developments in the \u2018validation\u2019 of satellite sensor products for the study of the land surface","volume":"21","author":"Justice","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102875","DOI":"10.1016\/j.earscirev.2019.102875","article-title":"Advances in quantitative remote sensing product validation: Overview and current status","volume":"196","author":"Wu","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.scitotenv.2017.12.121","article-title":"A cost-effective and efficient framework to determine water quality monitoring network locations","volume":"624","author":"Alilou","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.scitotenv.2015.05.011","article-title":"Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake","volume":"527","author":"Kiefer","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1080\/10402381.2015.1065937","article-title":"Reservoir water quality monitoring using remote sensing with seasonal models: Case study of five central-Utah reservoirs","volume":"31","author":"Hansen","year":"2015","journal-title":"Lake Reserv. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s10750-010-0237-4","article-title":"Effect of chlorophyll sampling design on water quality assessment in thermally stratified lakes","volume":"649","author":"Noges","year":"2010","journal-title":"Hydrobiologia"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1080\/17538947.2017.1371256","article-title":"A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different stratification indicators","volume":"11","author":"Sun","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lv, T., Zhou, X., Tao, Z., Sun, X., Wang, J., Li, R., and Xie, F. (2021). Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution. Remote Sens., 13.","DOI":"10.3390\/rs13142674"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"W11410","DOI":"10.1029\/2007WR006123","article-title":"Sampling design for compliance monitoring of surface water quality: A case study in a Polder area","volume":"44","author":"Brus","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1016\/j.apm.2019.08.025","article-title":"Adaptive Kriging coupled with importance sampling strategies for time-variant hybrid reliability analysis","volume":"77","author":"Ling","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.geoderma.2009.07.005","article-title":"Sampling design optimization for multivariate soil mapping","volume":"155","author":"Heuvelink","year":"2010","journal-title":"Geoderma"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.mcm.2011.10.035","article-title":"Even sampling designs generation by efficient spatial simulated annealing","volume":"58","author":"Chen","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"113913","DOI":"10.1016\/j.geoderma.2019.113913","article-title":"Sampling design optimization for soil mapping with random forest","volume":"355","author":"Wadoux","year":"2019","journal-title":"Geoderma"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Puri, D., Borel, K., Vance, C., and Karthikeyan, R. (2017). Optimization of a water quality monitoring network using a spatially referenced water quality model and a genetic algorithm. Water, 9.","DOI":"10.3390\/w9090704"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00158-017-1648-x","article-title":"A multi-point sampling method based on kriging for global optimization","volume":"56","author":"Cai","year":"2017","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Miralha, L., and Kim, D. (2018). Accounting for and predicting the influence of spatial autocorrelation in water quality modeling. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7020064"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1016\/j.jenvman.2010.04.011","article-title":"GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa","volume":"91","author":"Yang","year":"2010","journal-title":"J. Environ. Manag."},{"key":"ref_33","first-page":"39","article-title":"Optimization of spatial sample configurations using hybrid genetic algorithm and simulated annealing","volume":"2","author":"Guedes","year":"2011","journal-title":"Chil. J. Stat."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"146113","DOI":"10.1016\/j.scitotenv.2021.146113","article-title":"Optimal sampling strategy of water quality monitoring at high dynamic lakes: A remote sensing and spatial simulated annealing integrated approach","volume":"777","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104792","DOI":"10.1016\/j.envsoft.2020.104792","article-title":"A comprehensive review on the design and optimization of surface water quality monitoring networks","volume":"132","author":"Jiang","year":"2020","journal-title":"Environ. Model. Softw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1080\/13658816.2014.948446","article-title":"Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River basin, China","volume":"29","author":"Ge","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1002\/eap.1471","article-title":"Annual precipitation regulates spatial and temporal drivers of lake water clarity","volume":"27","author":"Rose","year":"2017","journal-title":"Ecol. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, Y., Gong, Z., Zheng, Y., and Zhang, Y. (2021). Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir. Water, 13.","DOI":"10.3390\/w13202844"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"012134","DOI":"10.1088\/1755-1315\/783\/1\/012134","article-title":"Research on Baiyangdian Lake Water Body Changes and Water Quality Parameters Inversion Based on Landsat Dense Time Series Data","volume":"783","author":"Cheng","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2016.12.030","article-title":"Landsat 8 remote sensing reflectance (Rrs) products: Evaluations, intercomparisons, and enhancements","volume":"190","author":"Pahlevan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"13","article-title":"Optimal Dynamic Temporal-Spatial Paramter Inversion Methods for the Marine Integrated Element Water Quality Model Using A Data-Driven Neural Network","volume":"20","author":"Li","year":"2012","journal-title":"J. Mar. Sci. Technol."},{"key":"ref_42","first-page":"1880","article-title":"Spectral Feature Construction and Sensitivity Analysis of Water Quality Parameters Remote Sensing Inversion","volume":"41","author":"Xinhui","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S0048-9697(00)00692-6","article-title":"Detecting chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using Landsat imagery","volume":"268","author":"Giardino","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tang, X., and Huang, M. (2021). Inversion of chlorophyll-a concentration in Donghu Lake based on machine learning algorithm. Water, 13.","DOI":"10.22541\/au.161156035.58145249\/v1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1002\/wer.1460","article-title":"Inversion and distribution of total suspended matter in water based on remote sensing images\u2014A case study on Yuqiao Reservoir, China","volume":"93","author":"Cao","year":"2021","journal-title":"Water Environ. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1364\/AO.44.000412","article-title":"Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: Experimental results","volume":"44","author":"Gitelson","year":"2005","journal-title":"Appl. Opt."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"28017","DOI":"10.1007\/s11356-017-0405-4","article-title":"Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models","volume":"24","author":"Alizadeh","year":"2017","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bl\u00f6mer, J., Lammersen, C., Schmidt, M., and Sohler, C. (2016). Theoretical analysis of the k-means algorithm\u2014A survey. Algorithm Engineering, Springer.","DOI":"10.1007\/978-3-319-49487-6_3"},{"key":"ref_49","unstructured":"Su, T., and Dy, J. (2004, January 15\u201317). A deterministic method for initializing k-means clustering. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, Boca Raton, FL, USA."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nainggolan, R., Perangin-angin, R., Simarmata, E., and Tarigan, A.F. (2018, January 23\u201324). Improved the performance of the K-means cluster using the sum of squared error (SSE) optimized by using the elbow method. Proceedings of the 1st International Conference of SNIKOM 2018, Medan, Indonesia.","DOI":"10.1088\/1742-6596\/1361\/1\/012015"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1007\/s11831-020-09474-6","article-title":"State-of-the-art and comparative review of adaptive sampling methods for kriging","volume":"28","author":"Fuhg","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02693799008941549","article-title":"Kriging: A method of interpolation for geographical information systems","volume":"4","author":"Oliver","year":"1990","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1111\/j.1365-2389.2011.01364.x","article-title":"Sampling for validation of digital soil maps","volume":"62","author":"Brus","year":"2011","journal-title":"Eur. J. Soil Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105509","DOI":"10.1016\/j.catena.2021.105509","article-title":"Spatial variability-based sample size allocation for stratified sampling","volume":"206","author":"Shao","year":"2021","journal-title":"Catena"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1016\/j.envsoft.2005.03.007","article-title":"A Bayesian method for computing sample size and cost requirements for stratified random sampling of pond water","volume":"21","author":"Bartolucci","year":"2006","journal-title":"Environ. Model. Softw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s41240-018-0102-3","article-title":"The systematic sampling for inferring the survey indices of Korean groundfish stocks","volume":"21","author":"Hyun","year":"2018","journal-title":"Fish. Aquat. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"195","DOI":"10.5057\/ijae.IJAE-D-15-00043","article-title":"Comparison of optimized selection methods of sampling sites network for water quality monitoring in a river","volume":"15","author":"Liyanage","year":"2016","journal-title":"Int. J. Affect. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ecolmodel.2016.08.016","article-title":"Water quality assessment analysis by using combination of Bayesian and genetic algorithm approach in an urban lake, China","volume":"339","author":"Yang","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/LGRS.2013.2274453","article-title":"Spatial sampling design for estimating regional GPP with spatial heterogeneities","volume":"11","author":"Wang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","first-page":"1","article-title":"Multiobjective simulated annealing: Principles and algorithm variants","volume":"2019","author":"Amine","year":"2019","journal-title":"Adv. Oper. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s10661-020-8228-z","article-title":"Application of particle swarm optimization to water management: An introduction and overview","volume":"192","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_62","unstructured":"Ding, P. (2021). The Integral of Second-order Directional Derivative. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/15230406.2013.762138","article-title":"Assessment of regression kriging for spatial interpolation\u2013comparisons of seven GIS interpolation methods","volume":"40","author":"Meng","year":"2013","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Myers, L., and Sirois, M.J. (2004). Spearman correlation coefficients, differences between. Encycl. Stat. Sci., 12.","DOI":"10.1002\/0471667196.ess5050"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1287\/opre.2016.1550","article-title":"Percentage and relative error measures in forecast evaluation","volume":"65","author":"Jose","year":"2017","journal-title":"Oper. Res."},{"key":"ref_66","first-page":"125","article-title":"Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques","volume":"589","author":"Yanlai","year":"2020","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:27:18Z","timestamp":1760138838000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":66,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122780"],"URL":"https:\/\/doi.org\/10.3390\/rs14122780","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,6,9]]}}}