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Special Issue "Selected Papers from the “International Symposium on Remote Sensing 2022”"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 842

Special Issue Editors

Dr. Hirokazu Yamamoto
E-Mail Website
Guest Editor
National Institute of Advanced Industrial Science and Technology, Annex 5th Floor, AIST Tokyo Waterfront, 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
Interests: calibration and validation of optical remote sensing systems; atmospheric correction; validating retrieved surface reflectance
Special Issues, Collections and Topics in MDPI journals
Dr. Sayaka Yoshikawa
E-Mail Website
Guest Editor
Global and Local Environment Co-creation Institute (GLEC), Ibaraki University 2-2-35 Sakuragawa, Mito, Ibaraki 310-0801, Japan
Interests: land use/land cover; deforestation; optical remote sensing; water resource
Dr. Naoyuki Hashimoto
E-Mail Website
Guest Editor
Faculty of Agriculture and Marine Science, Kochi University, 200 Monobeotsu, Nankoku, Kochi 783-8502, Japan
Interests: monitoring of field crop growth; assessment of cultivation environments; information systems with remote sensing
Dr. Wataru Takeuchi
E-Mail Website
Guest Editor
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: atmosphere and high carbon reservoirs; agriculture; urban environment assessment; natural disaster
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The International Symposium on Remote Sensing 2022 (ISRS 2022, https://isrs2022.sciforum.net/) will be a fully virtual meeting to provide all members of our community with the opportunity to participate in the annual ISRS event. This is the premier symposium that provides all participants with invaluable opportunities for catching up on state-of-the art techniques and the latest developments in remote sensing but also serves for sharing new ideas and information with colleagues and young scholars engaged in similar studies, research, or activities. This Special Issue in Remote Sensing is planned in conjunction with ISRS 2022 and will include peer-reviewed feature papers presented at ISRS 2022. In the cover letter, authors should provide the corresponding paper number of ISRS 2022.

Dr. Hirokazu Yamamoto
Dr. Sayaka Yoshikawa
Dr. Naoyuki Hashimoto
Dr. Wataru Takeuchi
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 2500 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

  • international symposium on remote sensing 2022
  • remote sensing
  • geoinformatics
  • Geoscience information system (GIS)
  • Global positioning system (GPS)
  • image processing

Published Papers (1 paper)

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Research

Article
Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
Remote Sens. 2022, 14(16), 4088; https://doi.org/10.3390/rs14164088 - 21 Aug 2022
Viewed by 389
Abstract
Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, [...] Read more.
Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future. Full article
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