Open Access Paper
7 November 2023 Front Matter: Volume 12727
Abstract
This PDF file contains the front matter associated with SPIE Proceedings Volume 12727, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. Some conference presentations may not be available for publication. Additional papers and presentation recordings may be available online in the SPIE Digital Library at SPIEDigitalLibrary.org.

The papers reflect the work and thoughts of the authors and are published herein as submitted. The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.

Please use the following format to cite material from these proceedings:

Author(s), “Title of Paper,” in Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, edited by Christopher M. Neale, Antonino Maltese, Proc. of SPIE 12727, Seven-digit Article CID Number (DD/MM/YYYY); (DOI URL).

ISSN: 0277-786X

ISSN: 1996-756X (electronic)

ISBN: 9781510666832

ISBN: 9781510666849 (electronic)

Published by

SPIE

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Copyright © 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).

Copying of material in this book for internal or personal use, or for the internal or personal use of specific clients, beyond the fair use provisions granted by the U.S. Copyright Law is authorized by SPIE subject to payment of fees. To obtain permission to use and share articles in this volume, visit Copyright Clearance Center at copyright.com. Other copying for republication, resale, advertising or promotion, or any form of systematic or multiple reproduction of any material in this book is prohibited except with permission in writing from the publisher.

Printed in the United States of America by Curran Associates, Inc., under license from SPIE.

Publication of record for individual papers is online in the SPIE Digital Library.

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Paper Numbering: A unique citation identifier (CID) number is assigned to each article in the Proceedings of SPIE at the time of publication. Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication. SPIE uses a seven-digit CID article numbering system structured as follows:

  • The first five digits correspond to the SPIE volume number.

  • The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0A, 0B … 0Z, followed by 10-1Z, 20-2Z, etc. The CID Number appears on each page of the manuscript.

Conference Committee

Symposium Chair

  • Lorenzo Bruzzone, Università degli Studi di Trento (Italy)

Conference Chairs

  • Christopher M. U. Neale, University of Nebraska Lincoln (United States)

  • Antonino Maltese, Università degli Studi di Palermo (Italy)

Conference Programme Committee

  • Alessandra Capolupo, Politecnico di Bari (Italy)

  • María Patrocinio González-Dugo, Instituto de Investigación y Formación Agraria y Pesquera (Spain)

  • Antonino Maltese, Università degli Studi di Palermo (Italy)

  • Christopher M. U. Neale, University of Nebraska Lincoln (United States)

Introduction

This proceedings volume contains papers presented during the Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV conference.

This conference was a segment of the SPIE Sensors + Imaging symposium, which is a fusion of two of Europe’s premier photonics conferences: SPIE Remote Sensing and SPIE Security + Defence.

The symposium took place at the RAI Exhibition and Convention Centre in Amsterdam, the Netherlands, from 3-6 September 2023. The conference this year featured approximately 30 oral presentations and 10 poster presentations, encompassing a wide array of topics related to the application of remote sensing in environmental science.

The conference was structured into 12 sessions, each focusing on a specific theme. These themes included, “Machine Learning and Deep Learning I”, “Surface, Coastal, and Ground Water”, “Forest Mapping and Monitoring I”, “Forest Mapping and Monitoring II”, “Agricultural Vegetation”, “Machine Learning and Deep Learning II”, “Wildfire Monitoring”, “Hyperspectral, High Spatial Resolution”, “Energy Balance, Evapotranspiration”, “Modelling, Machine Learning, and Deep Learning”, “UAV, LIDAR, and Smart Solutions for Farming” and “Environmental Remote Sensing”.

The conference’s Best Student Paper Award was given to the paper, “Association of catchment characteristics to Budyko hydrologic model’s uncertainty in humid catchments”, by Lilin Zhang, Michael Marshall, Anton Vrieling, and Andrew Nelson from the Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands.

The poster presentations effectively mirrored the themes of the aforementioned sessions. They encompassed a spectrum of research activities, both fundamental and application-oriented, including modelling, laboratory and field experiments, and operational applications.

We extend our heartfelt thanks to the presenters for sharing their research and to the attendees for their thought-provoking questions and discussions. We also express our gratitude to the SPIE for providing an exceptional venue, and to the SPIE Europe Manager, Program Coordinators and Proceedings Coordinator for their unwavering support before, during, and after the symposium.

We look forward to an even more successful conference in 2024 in Edinburgh, UK.

Christopher M. U. Neale

Antonino Maltese

© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
"Front Matter: Volume 12727", Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 1272701 (7 November 2023); https://doi.org/10.1117/12.3014329
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KEYWORDS
Data modeling

Environmental monitoring

Remote sensing

Optical sensing

Crop monitoring

Machine learning

Agriculture

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