Original Articles
Early Access
https://doi.org/10.4081/jae.2026.2096

Estimating indoor heat stress in broiler and swine facilities using high-resolution Geo-KOMPSAT-2A satellite data

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Published: 2 April 2026
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Heat stress, temperature–humidity index, machine learning, satellite data, livestock microclimate

Authors

Rial Arifin Rajagukguk
https://orcid.org/0000-0002-3258-7307
Department of Rural and Bio-systems Engineering, Chonnam National University, Gwangju, Korea, Republic of.
Jinseon Park
https://orcid.org/0000-0002-6281-4910
AgriBio Institute of Climate Change Management, Chonnam National University, Gwangju, Korea, Republic of.
Heejou Kim
https://orcid.org/0009-0004-2410-5517
Department of Rural and Bio-systems Engineering, Chonnam National University, Gwangju, Korea, Republic of.
Chae-rin Lee
https://orcid.org/0009-0001-2551-1515
Department of Rural and Bio-systems Engineering, Chonnam National University, Gwangjunnam National University, Korea, Republic of.
Ji-yeon Park
https://orcid.org/0009-0004-4940-7116
Department of Rural and Bio-systems Engineering, Chonnam National University, Gwangju, Korea, Republic of.
Se-yeon Lee
https://orcid.org/0009-0006-1131-5878
Department of Rural and Bio-systems Engineering, Chonnam National University, Gwangju, Korea, Republic of.
Taehwan Ha
https://orcid.org/0000-0002-3508-1897
Department of Agricultural and Rural Engineering, Chungnam National University, Daejeon, Korea, Republic of.
Se-woon Hong
hsewoon@jnu.ac.kr
https://orcid.org/0000-0002-9647-5523
Department of Rural and Bio-systems Engineering and AgriBio Institute of Climate Change Management, Chonnam National University, Gwangju, Korea, Republic of.

Heat stress poses a critical challenge to animal welfare and productivity in intensive livestock production systems, particularly for broilers and swine with limited thermoregulatory capacity. The temperature-humidity index (THI) is widely applied to assess thermal stress, yet conventional monitoring methods remain constrained by limited spatial coverage and scalability. This study introduces a novel satellite-based framework for estimating indoor heat stress in livestock facilities using machine learning and GEO-KOMPSAT-2A (GK2A) satellite data. The proposed outside-in approach integrates satellite-derived temperature, humidity, and solar irradiance to infer indoor environmental conditions without reliance on on-site sensors or detailed building specifications. Unlike computational fluid dynamics models, which are resource-intensive and difficult to scale, this data-driven method captures nonlinear relationships between outdoor meteorological variables and indoor microclimates. The XGBoost model demonstrated superior accuracy in estimating indoor temperature and humidity across multiple farms. When converted to THI, relative root mean square errors (rRMSE) ranged from 0.623% to 0.693% in swine farms and 0.827% to 1.332% in broiler farms, demonstrating robust performance in heat stress assessment. By leveraging geostationary satellite observations with high temporal and spatial resolution, this framework enables continuous, large-scale monitoring of thermal conditions in livestock facilities. The approach provides a practical and scalable tool to support ventilation management, cooling strategies, and animal welfare decisions under dynamic weather conditions.

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CRediT authorship contribution

RAR, manuscript original drafting, software, methodology, investigation, formal analysis; JP, contribution to manuscript writing and editing; HK, contribution to manuscript writing and editing; CL, contribution to manuscript writing and editing; JP, contribution to manuscript writing and editing; SL, contribution to manuscript writing and editing; TH, contribution to manuscript writing and editing; SH, contribution to manuscript writing and editing, software, methodology, investigation, formal analysis.

Supporting Agencies

National Institute of Agricultural Sciences, Rural Development Administration, South Korea

Data Availability Statement

The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality agreements with the collaborating farms, but are available upon reasonable request from the corresponding author.

How to Cite



“Estimating indoor heat stress in broiler and swine facilities using high-resolution Geo-KOMPSAT-2A satellite data” (2026) Journal of Agricultural Engineering [Preprint]. doi:10.4081/jae.2026.2096.
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