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Impacts of Climate Change and Weather Variability on Agricultural Production...
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Impacts of Climate Change and Weather Variability on Agricultural Production Observed by Remote-Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7035

Special Issue Editors

Prof. Dr. Jonghan Ko

E-Mail Website
Guest Editor
Applied Plant Science, Chonnam National University, Gwangju 61186, Korea
Interests: crop modelling; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jong-min Yeom

E-Mail Website
Guest Editor
Satellite Application Division, Korea Aerospace Research Institute, 169-84 Gwahak-ro, Daejeon 34133, Korea
Interests: application of remote sensing; integration of satellite images into deep-learning models; remote sensing of ecological resources in land surface
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jaeil Cho

E-Mail Website
Guest Editor
Department of Applied Plant Science, Chonnam National University, 77 Yongbong-ro, Gwangju 61186, Korea
Interests: remote sensing of vegetation; applications in agriculture, hydrology, and micrometeorology; interactions between atmosphere and biosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agroecosystems are vulnerable to rapidly changing climate conditions. However, local survey and statistical data regarding agriculture are hard to identify for evaluating climate change’s and extreme weather variability’s impacts on crop growth and productivity. Remote-sensing techniques allow the prompt monitoring of spatiotemporal shifts in crop land uses and crop growth and development conditions. Remote sensing with various sensors on diverse platforms also generates big data, which poses sizable challenges in data processing, analysis, and assimilation for the practical application of such data in agricultural production. This Special Issue aims to assemble the latest research on scientific and practical approaches for exploring the impacts of climate change and weather variability using remote-sensing techniques. We welcome original research contributions, exhaustive reviews, remote-sensing methodologies, and relevant applications in diverse agricultural environments with the latest developments in agricultural technology. We particularly invite papers on the following research topics:

  • Advancements in scientific methodologies for analyses of the impacts of climate change and weather variability in relation to remote-sensing observation;
  • Innovative remote-sensing and image-analysis tools or methods for the enhanced quantification of the biophysical and biochemical variables of crops and soils;
  • The application of a holistic system for these approaches.

We designated this Special Issue to foster the United Nations Food and Agricultural Organization (FAO)’s initiatives to resolve food security issues for both developing and developed nations. This issue is of interest to stakeholders in the agricultural policy area, including those in climate change adaptation, digital agriculture, and modern farming techniques.

Dr. Jonghan Ko
Dr. Jong-min Yeom
Prof. Dr. Jaeil Cho
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 2700 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

  • agriculture
  • climate change
  • crop
  • monitoring
  • observation
  • production
  • remote sensing
  • weather

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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19 pages, 9271 KiB  
Article
Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities
by Taehwan Shin, Jonghan Ko, Seungtaek Jeong, Jiwoo Kang, Kyungdo Lee and Sangin Shim
Remote Sens. 2022, 14(21), 5443; https://doi.org/10.3390/rs14215443 - 29 Oct 2022
Cited by 1 | Viewed by 2059
Abstract
Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced [...] Read more.
Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring. Full article
(This article belongs to the Special Issue Impacts of Climate Change and Weather Variability on Agricultural Production Observed by Remote-Sensing Techniques)
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17 pages, 26709 KiB  
Article
Simulation of Spatiotemporal Variations in Cotton Lint Yield in the Texas High Plains
by Seungtaek Jeong, Taehwan Shin, Jong-Oh Ban and Jonghan Ko
Remote Sens. 2022, 14(6), 1421; https://doi.org/10.3390/rs14061421 - 15 Mar 2022
Cited by 4 | Viewed by 2244
Abstract
This study aimed to simulate the spatiotemporal variation in cotton (Gossypium hirsutum L.) growth and lint yield using a remote sensing-integrated crop model (RSCM) for cotton. The developed modeling scheme incorporated proximal sensing data and satellite imagery. We formulated this model and [...] Read more.
This study aimed to simulate the spatiotemporal variation in cotton (Gossypium hirsutum L.) growth and lint yield using a remote sensing-integrated crop model (RSCM) for cotton. The developed modeling scheme incorporated proximal sensing data and satellite imagery. We formulated this model and evaluated its accuracy using field datasets obtained in Lamesa in 1999, Halfway in 2002 and 2004, and Lubbock in 2003–2005 in the Texas High Plains in the USA. We found that RSCM cotton could reproduce the cotton leaf area index and lint yield across different locations and irrigation systems with a statistically significant degree of accuracy. RSCM cotton was also used to simulate cotton lint yield for the field circles in Halfway. The RSCM system could accurately reproduce the spatiotemporal variations in cotton lint yield when integrated with satellite images. From the results of this study, we predict that the proposed crop-modeling approach will be applicable for the practical monitoring of cotton growth and productivity by farmers. Furthermore, a user can operate the modeling system with minimal input data, owing to the integration of proximal and remote sensing information. Full article
(This article belongs to the Special Issue Impacts of Climate Change and Weather Variability on Agricultural Production Observed by Remote-Sensing Techniques)
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Other

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14 pages, 7215 KiB  
Technical Note
Development of a Radiometric Calibration Method for Multispectral Images of Croplands Obtained with a Remote-Controlled Aerial System
by Taehwan Shin, Seungtaek Jeong and Jonghan Ko
Remote Sens. 2023, 15(5), 1408; https://doi.org/10.3390/rs15051408 - 2 Mar 2023
Cited by 6 | Viewed by 1925
Abstract
A remote sensing (RS) platform consisting of a remote-controlled aerial vehicle (RAV) can be used to monitor crop, environmental conditions, and productivity in agricultural areas. However, the current methods for the calibration of RAV-acquired images are cumbersome. Thus, a calibration method must be [...] Read more.
A remote sensing (RS) platform consisting of a remote-controlled aerial vehicle (RAV) can be used to monitor crop, environmental conditions, and productivity in agricultural areas. However, the current methods for the calibration of RAV-acquired images are cumbersome. Thus, a calibration method must be incorporated into RAV RS systems for practical and advanced applications. Here, we aimed to develop a standalone RAV RS-based calibration system without the need for calibration tarpaulins (tarps) by quantifying the sensor responses of a multispectral camera, which varies with light intensities. To develop the standalone RAV-based RS calibration system, we used a quadcopter with four propellers, with a rotor-to-rotor length of 46 cm and height of 25 cm. The quadcopter equipped with a multispectral camera with green, red, and near-infrared filters was used to acquire spectral images for formulating the RAV RS-based standardization system. To perform the calibration study process, libraries of sensor responses were constructed using pseudo-invariant tarps according to the light intensities to determine the relationship equations between the two factors. The calibrated images were then validated using the reflectance measured in crop fields. Finally, we evaluated the outcomes of the formulated RAV RS-based calibration system. The results of this study suggest that the standalone RAV RS system would be helpful in the processing of RAV RS-acquired images. Full article
(This article belongs to the Special Issue Impacts of Climate Change and Weather Variability on Agricultural Production Observed by Remote-Sensing Techniques)
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