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Atmosphere
Special Issues
Recent Advances in Agrometeorological Techniques and Their Applications
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Recent Advances in Agrometeorological Techniques and Their Applications

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 1014

Editors

Prof. Dr. Márcio Mesquita

E-Mail Website
Guest Editor
Department of Agronomy, Federal University of Goiás (UFG), Goiânia 74690-900, Brazil
Interests: agrometeorology; remote sensing; satellite images; climate change; radiation and energy balance; vegetation and water indices; evapotranspiration; rainfall and drought
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marcos Vinícius Da Silva

E-Mail Website
Guest Editor
Centro de Ciências Agrárias e Ambientais (CCAA), Universidade Federal de Maranhão, BR-222, Chapadinha 65500-000, MA, Brazil
Interests: animal confort; agricultural and biosystems engineering; agricultural engineering; agricultural meteorology; animal thermal comfort; applied meteorology; applied statistics; hydrological modeling; hydrology; irrigation engineering; remote sensing; meteorology
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Charalampopoulos

E-Mail Website
Guest Editor
Faculty of Crop Sciences, Agricultural University of Athens, Athens, Greece
Interests: agroclimatology; bioclimatology; biometeorology; landscape change; computational data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Atmosphere aims to bring together innovative research that explores new approaches, methodologies, and applications relating to agrometeorology in order to address global challenges such as climate change, extreme events, food security, and the optimization of agricultural resources. We welcome studies on the following topics: 

  1. Short- and long-term agrometeorological modeling and forecasting;
  2. Impacts of climate change on agriculture and adaptation strategies;
  3. The use of remote sensing and machine learning in agrometeorological analysis;
  4. Influence of meteorological factors on crop growth and development;
  5. Techniques for mitigating abiotic stresses in agricultural systems;
  6. Applications of agrometeorology in precision agriculture and sustainable management.

Agrometeorology plays an essential role in understanding the interactions between atmospheric systems and agriculture, directly influencing the productivity, sustainability, and resilience of agricultural systems. In recent years, significant advances in climate modeling, remote sensing, artificial intelligence, and big data have revolutionized the way weather and climate impacts are monitored, predicted, and managed in agriculture. 

We invite researchers to submit original articles, reviews, and case studies that contribute to the advancement of agrometeorological knowledge and practice, providing valuable insights for academia, the agricultural sector, and policy makers.

Prof. Dr. Márcio Mesquita
Prof. Dr. Marcos Vinícius Da Silva
Dr. Ioannis Charalampopoulos
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.

Keywords

  • precision agriculture
  • sustainable management
  • agrometeorology
  • remote sensing
  • climate change
  • evapotranspiration

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

Published Papers (1 paper)

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Research

30 pages, 4355 KB  
Article
Identifying Nonlinear Thresholds and Interaction Dominance of Meteorological Drivers on Rice Yield: A SHAP-Based Approach
by Chenshuang Lin, Zhitao Yan and Shujie Miao
Atmosphere 2026, 17(6), 599; https://doi.org/10.3390/atmos17060599 - 11 Jun 2026
Viewed by 258
Abstract
Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading [...] Read more.
Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R2 = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article
(This article belongs to the Special Issue Recent Advances in Agrometeorological Techniques and Their Applications)
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