{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:11:36Z","timestamp":1760238696522,"version":"build-2065373602"},"reference-count":8,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"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>This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. 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(2020). Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data. Remote Sens., 12.","DOI":"10.3390\/rs12081242"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ort\u00edz-Barrios, M.A., Cleland, I., Nugent, C., Pancardo, P., J\u00e4rpe, E., and Synnott, J. (2020). Simulated Data to Estimate Real Sensor Events\u2014A Poisson-Regression-Based Modelling. Remote Sens., 12.","DOI":"10.3390\/rs12050771"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"He, J., and Eastman, J.R. (2020). A Sequential Autoencoder for Teleconnection Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12050851"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bai, T., Pang, Y., Wang, J., Han, K., Luo, J., Wang, H., Lin, J., Wu, J., and Zhang, H. (2020). An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images. 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Atmospheric Correction Thresholds for Ground-Based Radar Interferometry Deformation Monitoring Estimated Using Time Series Analyses. Remote Sens., 12.","DOI":"10.3390\/rs12142236"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2815\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:04:54Z","timestamp":1760177094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,31]]},"references-count":8,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12172815"],"URL":"https:\/\/doi.org\/10.3390\/rs12172815","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,8,31]]}}}