Special Issue "Machine Learning Approaches for Assessing Vegetation Phenology under Climate Change"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: 6 December 2023 | Viewed by 2161

Special Issue Editors

Key Laboratory of Water Sediment Sciences, College of Water Sciences, Beijing Normal University, Beijing 100875, China
Interests: UAV remote sensing; machine learning; deep learning; phenology extraction; yield prediction; data fusion
Special Issues, Collections and Topics in MDPI journals
Department of Earth and Space Sciences, South University of Science and Technology of China, Shenzhen 518055, China
Interests: GNSS data processing and application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are in the era of climate change, and global warming and irregular precipitation have profoundly influenced vegetation phenology and crop growth, subsequently affecting the carbon balance. Identifying to what extent vegetation phenology has changed and responded to the ongoing climate change will help to understand the inner influencing mechanisms and to provide effective adaptive measures. Therefore, it is essential to explore the changes in vegetation phenology under climate change at multiple scales. With the development of remote sensing technology, the monitoring of vegetation phenology has been significantly improved. In particular, high-resolution satellites and unmanned aerial vehicles (UAVs) have provided convenience for the Earth observation of vegetation changes in phenology.

The purpose of this Special Issue is to present new research advances on the applications of remote sensing techniques, such as multi/hyperspectral satellites and UAVs, for monitoring the changes in vegetation phenology under the changing climate. Contributions focusing on new methods and applications in vegetation phenology extraction; the assessment of climate change impacts on vegetation phenology, in particular, new approaches and novel contributions using machine learning; and deep learning methods, specifically studies based on multispectral and hyperspectral from multiple platforms, are welcome. The scope of this Special Issue includes, but is not limited to, the following:

  • Vegetation phenology extraction using multi- and hyperspectral images;
  • Mapping vegetation phenology;
  • Vegetation growth monitoring;
  • Time-series analysis monitoring of agriculture and forest;
  • High-throughput phenomics;
  • Machine learning and deep learning.

Dr. Yahui Guo
Dr. Shunqiang Hu
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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.


  • vegetation phenology
  • machine learning and deep learning
  • high-throughput phenomics
  • climate change
  • data fusion
  • radiometric calibration
  • time-series analysis

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


18 pages, 7508 KiB  
Intelligent Analysis Cloud Platform for Soil Moisture-Nutrients-Salinity Content Based on Quantitative Remote Sensing
Atmosphere 2023, 14(1), 23; https://doi.org/10.3390/atmos14010023 - 23 Dec 2022
Cited by 1 | Viewed by 1668
Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and [...] Read more.
Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and intelligently acquiring good soil quality information. Scientists worldwide have developed many remote sensing quantitative inversion models, which need to be systematized and intelligent for agricultural personnel to enjoy the dividends of information technology such as 3S (remote sensing, geographic information system, and global navigation satellite system) techniques. Accordingly, to meet the need of farmers, agricultural managers, and agricultural researchers to acquire timely information on regional soil quality, in this paper, we designed a cloud platform for inversion analysis of moisture, nutrient, salinity, and other important soil quality indicators. The platform was developed using ArcGIS (The software is produced by the Environmental Systems Research Institute, Inc. of America in Redlands, CL, USA) and GeoScene (The software is produced by GeoScene Information Technology Co.,Ltd., Beijing, China) software, with Java and JavaScript as programing languages and SQL Server as the database management system with a PC client, a web client, and a mobile app. On the basis of the existing quantitative remote sensing models, the platform realizes mapping functions, intelligent inversion of soil moisture–nutrient–salinity (SMNS) content, data analysis mining, soil knowledge base, platform management, and so on. It can help different users acquire, manage, and analyze data and make decisions based on the data. In addition, the platform can customize model parameters according to regional characteristics, improving analysis accuracy and expanding the application area. Overall, the platform employs 3S techniques, Internet technology, and mobile communication technology synthetically and realizes intelligent inversion and decision analysis of significant soil quality information, such as moisture–nutrient–salinity content. This platform has been applied to the analysis of soil indicators in several areas and has produced good operational results and benefits. This study will enable rapid data analysis and provide technical support for regional agriculture production, contributing to the development of smart agriculture. Full article
Show Figures

Figure 1

Back to TopTop