Special Issue "Remote Sensing Data Sets"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Jorge Vazquez
Guest Editor
Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA 91109, USA
Interests: validation of remote sensing data; application of remote sensing to coastal regions; development of new remote sensing for high resolution; Validation of remote sensing data sets in challenging areas, including the Arctic and coastal regions
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Special Issue Information

Dear Colleagues,

Currently, one of the critical questions in the application of remote sensing data sets is “what data set do I use?” The answer to this question can depend on many factors, including temporal resolution, spatial resolution, data uncertainty, temporal coverage and spatial coverage. These types of questions are inherent to the user community and have a commonality to the broad range of remote sensing data sets, inclusive of sea level (altimetry), sea surface winds (scatterometry), and sea surface temperature (infrared  sensors) and ocean color (visible).  Users struggle with finding, in one place, information that can allow them to make a knowledgeable decision about the data set for their application.

We are looking for  articles that address details and characteristics of remote sensing products, that could provide the user community with necessary information for making decisions on the appropriateness of products for specific applications and research problems.  Articles that address the general characteristics of the data sets and specific examples of applications are highly encouraged.  Additionally, articles that focus on data quality issues and/or uncertainties are encouraged. Comparison papers that can help users make decisions on the suitability of remote sensing data sets for their applications/research needs are highly encouraged.

Dr. Jorge Vazquez
Dr. Alexander Kokhanovsky
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 papers will be 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 2200 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.

Published Papers (1 paper)

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Open AccessLetter
Manual-Based Improvement Method for the ASTER Global Water Body Data Base
Remote Sens. 2020, 12(20), 3373; https://doi.org/10.3390/rs12203373 - 15 Oct 2020
A water body detection technique is an essential part of digital elevation model (DEM) generation to delineate land–water boundaries and to set flattened elevations. The initial tile-based water body data that are created during production of the Advanced Spaceborne Thermal Emission and Reflection [...] Read more.
A water body detection technique is an essential part of digital elevation model (DEM) generation to delineate land–water boundaries and to set flattened elevations. The initial tile-based water body data that are created during production of the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) GDEM, as a by-product, are incorporated into ASTER GDEM V3 to improve the quality. At the same time as ASTER GDEM V3, the Global Water Body Data Base (ASTWBD) Version 1 is also released to the public. The ASTWBD generation consists of two parts: separation from land area, and classification into three categories: sea, lake, and river. Sea water bodies have zero elevation. Lake water bodies have flattened elevations. River water bodies have a gradual step-down from upstream to downstream with a step of one meter. The separation process from land area is carried out automatically using an algorithm, except for sea-ice removal, to delineate the real seashore lines in the high latitude areas; almost all of the water bodies are created through this process. The classification process into three categories, i.e., sea, river, and lake, is carried out, and incorporated into ASTER GDEM V3. For inland water bodies, it is not possible to perfectly detect all water bodies using reflectance and spectral index, which are the only available parameters for optical sensors. The only way available to identify the undetected inland water bodies is to manually copy them with visual inspection from the earth’s surface images, like Landsat images. GeoCover2000 images are the main part of the object images. Color–Land ASTER MosaicS (CLAMS) images are used to cover the deficiency of the GeoCover2000 images. This kind of time-consuming, unsophisticated way is inevitable as it is a manual-based method to improve the quality of the ASTWBD. This paper describes the manual-based improvement method; specifically, how deficient water body images are efficiently copied as rasterized images from the earth’s surface images to obtain a more complete global water body data set. Full article
(This article belongs to the Special Issue Remote Sensing Data Sets)
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