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  Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework

Peng, J., Kharbouche, S., Muller, J.-P., Danne, O., Blessing, S., Giering, R., et al. (2020). Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework. Journal of Hydrology, 580: 124245. doi:10.1016/j.jhydrol.2019.124245.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-5157-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-6C04-E
Genre: Journal Article

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 Creators:
Peng, Jian1, 2, Author              
Kharbouche, S.1, Author
Muller, J.-P.1, Author
Danne, O.1, Author
Blessing, S.1, Author
Giering, R.1, Author
Gobron, N.1, Author
Ludwig, R.1, Author
Müller, B.1, Author
Leng, G.1, Author
Lees, T.1, Author
Dadson, S.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Terrestrial Remote Sensing / HOAPS, The Land in the Earth System, MPI for Meteorology, Max Planck Society, ou_913559              

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Free keywords: Latent heat; Parameter estimation; Quality assurance; Radiometers; Sensitivity analysis; Solar radiation; Surface measurement; Surface waters; Uncertainty analysis, Albedo; FluxNet; GlobAlbedo; GLOBMAP; HOLAPS; Leaf Area Index; MODIS; QA4ECV, Heat flux
 Abstract: The High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) programme provides a modeling system to maximize the use of satellite-based products and ensure internally consistent estimation of surface water and energy fluxes. Leaf area index (LAI) and land surface albedo are two key parameters for estimation of latent and sensible heat fluxes with HOLAPS. Thus, to facilitate the generation of long-term high accuracy latent and sensible heat fluxes, high quality global long-term LAI and land surface albedo datasets are essential. The Quality Assurance for Essential Climate Variables (QA4ECV) project released quality-assured long-term LAI and albedo datasets with traceable and reliable uncertainty information provided in the dataset. Taking MODIS-BNU-LAI and MODIS albedo as reference, different global long-term LAI and albedo datasets including GlobAlbedo, QA4ECV and GLOBMAP were investigated for estimation of latent/sensible heat fluxes with HOLAPS in this study. The results show that all albedo datasets show similar accuracy for estimation of latent and sensible heat fluxes when validated against FLUXNET observations. The QA4ECV LAI leads to worse latent heat flux estimation due to its use of effective LAI rather than green leaf LAI. Sensitivity analysis also shows that the HOLAPS estimated latent heat flux (LE) is more sensitive to uncertainty in LAI than land surface albedo. Overall, the combined use of QA4ECV albedo and GLOBMAP LAI is suggested for estimation of latent/sensible heat fluxes with HOLAPS. The root mean square differences (RMSD) between estimations and FLUXNET measurements are 54 (30) W/m2 for hourly (monthly) latent heat flux, and 80.5 (24.5) W/m2 for sensible heat flux, which are comparable to estimates with MODIS and other reported studies. © 2019 Elsevier B.V.

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Language(s): eng - English
 Dates: 2019-102020-012020-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.jhydrol.2019.124245
BibTex Citekey: Peng2019
 Degree: -

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Title: Journal of Hydrology
Source Genre: Journal
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Publ. Info: Elsevier B.V.
Pages: - Volume / Issue: 580 Sequence Number: 124245 Start / End Page: - Identifier: ISSN: 00221694