Dear Colleagues,

Thanks to the variety of missions with a large array of scientific applications, satellite remote sensing data has now become abundant. The low temporal revisit of orbiting satellites makes it possible to acquire data over a given zone with a sampling rate of a few days, allowing to capture quick variations in the scene content. However, making the most of these rich time series data is a promising but still relatively under-explored research direction because of the complexity of processing and analyzing this additional temporal information jointly with the spatial (and potentially spectral if the used sensor provides multichannel images) content of the scene. In this special issue, we aim at collecting papers developing original methods to process and analyze multimodal remote sensing time series, with a strong emphasis on data driven or artifical intelligence based approaches. By multimodal, we mean here that the information within the time series data may not be restricted to a single sensor type. Submissions of deep learning based approaches for remote sensing time series processing are warmly welcomed, but contributions featuring traditional machine learning or signal processing techniques are also encouraged.  Applications can range from land cover and land use changes, environmental monitoring to disaster management and data interpolation or forecasting, among others.

Dr. Guillaume Tochon
Dr. Lucas Drumetz
Dr. Mauro Dalla Mura
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• time series data
• remote sensing
• multimodal data
• artificial intelligence
• machine/deep learning