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Estimating latent heat flux of subtropical forests using machine learning algorithms

文献类型：期刊论文

作者Sahu, Harekrushna7,8; Burman, Pramit Kumar Deb5,6; Gnanamoorthy, Palingamoorthy4; Song, Qinghai4; Zhang, Yiping4; Wang, Huimin1; Chen, Yaoliang3; Wang, Shusen2
刊名METEOROLOGICAL APPLICATIONS
出版日期2025
卷号32期号:1页码:20
关键词AdaBoost evapotranspiration gradient boosting latent heat flux random forest regression subtropical forest support vector regression
ISSN号1350-4827
DOI10.1002/met.70023
通讯作者Burman, Pramit Kumar Deb(pramit.cat@tropmet.res.in)
英文摘要Latent heat flux (LE) is a measure of the water exchange between Earth's surface and atmosphere, also known as evapotranspiration. It is a fundamental component in the Earth's energy budget and hydrological cycle and plays an important role in regulating the weather and climate. Moderate Resolution Imaging Spectroradiometer (MODIS) offers a gap-filled biophysical product for LE at 8-day temporal and 500-meter spatial resolutions. Nonetheless, validation against the in situ eddy covariance measurement reveals significant errors in MODIS LE estimation. Our study integrates ground-measured, reanalysis and satellite data to predict LE by leveraging the advantage of the data-driven method. The study draws upon flux data derived from the AsiaFlux database, alongside reanalysis datasets from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and the European Centre for Medium-Range Weather Forecasts (ERA5) products, as well as biophysical measurements from the MODIS satellite. An analysis of the annual water budget, based on ERA5 precipitation data, highlights net positive water balances across the study sites. By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. This study highlights the robustness of support vector regression and accentuates the impact of climatic and environmental conditions on model performance, ultimately contributing to more precise predictions of latent heat flux.
WOS关键词TROPICAL RAIN-FOREST ; EDDY COVARIANCE MEASUREMENTS ; TERRESTRIAL EVAPOTRANSPIRATION ; REANALYSIS PRODUCTS ; CARBON-DIOXIDE ; LAND-COVER ; WATER ; MODIS ; BALANCE ; CLIMATE
资助项目Ministry of Earth Sciences (MoES) ; Government of India
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001390826700001
出版者WILEY
资助机构Ministry of Earth Sciences (MoES) ; Government of India
源URL[http://ir.igsnrr.ac.cn/handle/311030/212447]  
专题中国科学院地理科学与资源研究所
通讯作者Burman, Pramit Kumar Deb
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
2.Nat Resources Canada, Canada Ctr Remote Sensing, Ottawa, ON, Canada
3.Fujian Normal Univ, State Key Lab Subtrop Mt Ecol Minist Sci & Technol, Fuzhou, Peoples R China
4.Chinese Acad Sci, CAS Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Menglun, Peoples R China
5.Savitribai Phule Pune Univ, Dept Atmospher & Space Sci, Pune, India
6.Indian Inst Trop Meteorol, Ctr Climate Change Res, Pune, India
7.Indian Inst Technol, Ctr Machine Intelligence & Data Sci, Mumbai, India
8.Def Inst Adv Technol, Dept Math, Pune, India
推荐引用方式
GB/T 7714		 Sahu, Harekrushna,Burman, Pramit Kumar Deb,Gnanamoorthy, Palingamoorthy,et al. Estimating latent heat flux of subtropical forests using machine learning algorithms[J]. METEOROLOGICAL APPLICATIONS,2025,32(1):20.
APA Sahu, Harekrushna.,Burman, Pramit Kumar Deb.,Gnanamoorthy, Palingamoorthy.,Song, Qinghai.,Zhang, Yiping.,...&Wang, Shusen.(2025).Estimating latent heat flux of subtropical forests using machine learning algorithms.METEOROLOGICAL APPLICATIONS,32(1),20.
MLA Sahu, Harekrushna,et al."Estimating latent heat flux of subtropical forests using machine learning algorithms".METEOROLOGICAL APPLICATIONS 32.1(2025):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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