Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method
Changes in evapotranspiration can affect water availability and climate, leading to extreme weather and severe impact on ecosystems. In particular, increased water stress in farmland, forests, and mountainous areas with limited water resources can result in detrimental impacts such as droughts and w...
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Elsevier
2024-12-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017224000555 |
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| author | Bu-Yo Kim Joo Wan Cha |
| author_facet | Bu-Yo Kim Joo Wan Cha |
| author_sort | Bu-Yo Kim |
| collection | DOAJ |
| description | Changes in evapotranspiration can affect water availability and climate, leading to extreme weather and severe impact on ecosystems. In particular, increased water stress in farmland, forests, and mountainous areas with limited water resources can result in detrimental impacts such as droughts and wildfires. In this study, we utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK-2A) and employed a tree-based machine learning method to accurately estimate reference evapotranspiration (ETo) in South Korea. The estimated SAT ETo was compared to the ASOS ETo, which was estimated using meteorological variables from the Automated Synoptic Observing System (ASOS) and the Penman–Monteith method. The hourly SAT ETo demonstrated an estimated accuracy with a relative bias (rBias) of −0.26%, a relative root mean square error (rRMSE) of 34.01%, and a coefficient of determination (R2) of 0.94, whereas the daily SAT ETo exhibited an estimated accuracy with an rBias of −0.25%, an rRMSE of 8.30%, and an R2 of 0.97. In this study, various cases were analyzed in detail, including daytime and nighttime, wet and dry conditions, and varying cloud cover. The highly accurate estimation of ETo using data from the GK-2A satellite, which have high temporal and spatial resolution, can be effectively utilized as monitoring data for water resource management and natural disaster prevention. |
| format | Article |
| id | doaj-art-233c4b537d6b45e79d221eb05cd7ca14 |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Science of Remote Sensing |
| spelling | doaj-art-233c4b537d6b45e79d221eb05cd7ca142024-12-12T05:23:09ZengElsevierScience of Remote Sensing2666-01722024-12-0110100171Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning methodBu-Yo Kim0Joo Wan Cha1Corresponding author.; Research Applications Department, National Institute of Meteorological Sciences, Seogwipo, Jeju, 63568, South KoreaResearch Applications Department, National Institute of Meteorological Sciences, Seogwipo, Jeju, 63568, South KoreaChanges in evapotranspiration can affect water availability and climate, leading to extreme weather and severe impact on ecosystems. In particular, increased water stress in farmland, forests, and mountainous areas with limited water resources can result in detrimental impacts such as droughts and wildfires. In this study, we utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK-2A) and employed a tree-based machine learning method to accurately estimate reference evapotranspiration (ETo) in South Korea. The estimated SAT ETo was compared to the ASOS ETo, which was estimated using meteorological variables from the Automated Synoptic Observing System (ASOS) and the Penman–Monteith method. The hourly SAT ETo demonstrated an estimated accuracy with a relative bias (rBias) of −0.26%, a relative root mean square error (rRMSE) of 34.01%, and a coefficient of determination (R2) of 0.94, whereas the daily SAT ETo exhibited an estimated accuracy with an rBias of −0.25%, an rRMSE of 8.30%, and an R2 of 0.97. In this study, various cases were analyzed in detail, including daytime and nighttime, wet and dry conditions, and varying cloud cover. The highly accurate estimation of ETo using data from the GK-2A satellite, which have high temporal and spatial resolution, can be effectively utilized as monitoring data for water resource management and natural disaster prevention.http://www.sciencedirect.com/science/article/pii/S2666017224000555EvapotranspirationEToGK-2APenman–MonteithWater resource management |
| spellingShingle | Bu-Yo Kim Joo Wan Cha Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method Science of Remote Sensing Evapotranspiration ETo GK-2A Penman–Monteith Water resource management |
| title | Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method |
| title_full | Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method |
| title_fullStr | Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method |
| title_full_unstemmed | Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method |
| title_short | Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method |
| title_sort | estimation of reference evapotranspiration in south korea using gk 2a ami channel data and a tree based machine learning method |
| topic | Evapotranspiration ETo GK-2A Penman–Monteith Water resource management |
| url | http://www.sciencedirect.com/science/article/pii/S2666017224000555 |
| work_keys_str_mv | AT buyokim estimationofreferenceevapotranspirationinsouthkoreausinggk2aamichanneldataandatreebasedmachinelearningmethod AT joowancha estimationofreferenceevapotranspirationinsouthkoreausinggk2aamichanneldataandatreebasedmachinelearningmethod |