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Remote Sensing for Inland Waters and Their Aquatic Vegetation
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Remote Sensing for Inland Waters and Their Aquatic Vegetation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 10469

Special Issue Editors

Dr. Sijia Li

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: satellite sensors; water quality monitoring; mapping; remote sensing
Dr. Zui Tao

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: alidation of remote sensing product; ecological; environmental remote sensing
Dr. Shun Bi

E-Mail Website
Guest Editor
Helmholtz-Zentrum Hereon GmbH, 21502 Geesthacht, Germany
Interests: ocean color; phytoplankton; satellite image analysis; optical water classification

Special Issue Information

Dear Colleagues,

Remote sensing provides multitude tools that can be used to monitor water qualities and ecosystem management inland waters. Recently, many new satellite sensors on satellite platforms were developed. By a combination of in situ measurements, satellite data and numerical simulations, they can provide key contributions on (bio) geophysical parameters of inland waters, as well as the inventories of surface water for current and future sensors.

This Special Issue is dedicated to highlighting the new advancement application for new satellite sensors (including ocean satellite sensors) research on inland waters with visible and thermal bands, etc. Remote sensed hydrological and biogeochemical cycles of inland waters are welcomed.

Dr. Sijia Li
Dr. Zui Tao
Dr. Shun Bi
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. Sensors 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 2600 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.

Keywords

  • satellite sensors applications for water color
  • satellite sensors evaluation for water color
  • remote sensed algorithms of water qualities
  • inland aquatic vegetation mapping

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Published Papers (1 paper)

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Review

26 pages, 7005 KiB  
Review
Land Use and Land Cover Classification Meets Deep Learning: A Review
by Shengyu Zhao, Kaiwen Tu, Shutong Ye, Hao Tang, Yaocong Hu and Chao Xie
Sensors 2023, 23(21), 8966; https://doi.org/10.3390/s23218966 - 3 Nov 2023
Cited by 29 | Viewed by 10039
Abstract
As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural [...] Read more.
As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth’s surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification. Full article
(This article belongs to the Special Issue Remote Sensing for Inland Waters and Their Aquatic Vegetation)
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