Matches in SemOpenAlex for { <https://semopenalex.org/work/W2516594577> ?p ?o ?g. }
- W2516594577 endingPage "742" @default.
- W2516594577 startingPage "742" @default.
- W2516594577 abstract "Combining flux tower measurements with remote sensing or land surface models is generally regarded as an efficient method to scale up flux data from site to region. However, due to the heterogeneous nature of the vegetated land surface, the changing flux source areas and the mismatching between ground source areas and remote sensing grids, direct use of in-situ flux measurements can lead to major scaling bias if their spatial representativeness is unknown. Here, we calculate and assess the spatial representativeness of 15 flux sites across northern China in two aspects: first, examine how well a tower represents fluxes from the specific targeted vegetation type, which is called vegetation-type level; and, second, examine how representative is the flux tower footprint of the broader landscape or regional extents, which is called spatial-scale level. We select fraction of target vegetation type (FTVT) and Normalized Difference Vegetation Index (NDVI) as key indicators to calculate the spatial representativeness of 15 EC sites. Then, these sites were ranked into four grades based on FTVT or cluster analysis from high to low in order: (1) homogeneous; (2) representative; (3) acceptable; and (4) disturbed measurements. The results indicate that: (1) Footprint climatology for each site was mainly distributed in an irregular shape, had similar spatial pattern as spatial distribution of prevailing wind direction; (2) At vegetation-type level, the number of homogeneous, representative, acceptable and disturbed measurements is 8, 4, 1 and 2, respectively. The average FTVT was 0.83, grass and crop sites had greater representativeness than forest sites; (3) At spatial-scale level, flux sites with zonal vegetation had greater representativeness than non-zonal vegetation sites, and the scales were further divided into three sub-scales: (a) in flux site scale, the average of absolute NDVI bias was 4.34%, the number of the above four grades is 9, 4, 1 and 1, respectively; (b) in remote sensing pixel scale, the average of absolute NDVI bias was 8.27%, the number is 7, 2, 2 and 4, respectively; (c) in land model grid scale, the average of absolute NDVI bias was 12.13%, the number is 5, 4, 3 and 3. These results demonstrate the variation of spatial representativeness of flux measurements among different application levels and scales and highlighted the importance of proper interpretation of EC flux measurements. These results also suggest that source area of EC flux should be involved in model validation and/or calibration with EC flux measurements." @default.
- W2516594577 created "2016-09-16" @default.
- W2516594577 creator A5019532048 @default.
- W2516594577 creator A5033514563 @default.
- W2516594577 creator A5040296391 @default.
- W2516594577 creator A5059510144 @default.
- W2516594577 date "2016-09-08" @default.
- W2516594577 modified "2023-09-25" @default.
- W2516594577 title "Assessment of Spatial Representativeness of Eddy Covariance Flux Data from Flux Tower to Regional Grid" @default.
- W2516594577 cites W1487859523 @default.
- W2516594577 cites W1563680739 @default.
- W2516594577 cites W1598502789 @default.
- W2516594577 cites W1598572863 @default.
- W2516594577 cites W1604999394 @default.
- W2516594577 cites W1969112274 @default.
- W2516594577 cites W1971331431 @default.
- W2516594577 cites W1974734806 @default.
- W2516594577 cites W1975158286 @default.
- W2516594577 cites W1979743603 @default.
- W2516594577 cites W1998069659 @default.
- W2516594577 cites W2013084637 @default.
- W2516594577 cites W2015202358 @default.
- W2516594577 cites W2017221919 @default.
- W2516594577 cites W2017499476 @default.
- W2516594577 cites W2017533211 @default.
- W2516594577 cites W2018318189 @default.
- W2516594577 cites W2025424387 @default.
- W2516594577 cites W2042621636 @default.
- W2516594577 cites W2054566120 @default.
- W2516594577 cites W2056811372 @default.
- W2516594577 cites W2057498579 @default.
- W2516594577 cites W2057802193 @default.
- W2516594577 cites W2058364716 @default.
- W2516594577 cites W2060629327 @default.
- W2516594577 cites W2062266193 @default.
- W2516594577 cites W2063623478 @default.
- W2516594577 cites W2065454207 @default.
- W2516594577 cites W2092722122 @default.
- W2516594577 cites W2103170539 @default.
- W2516594577 cites W2105536220 @default.
- W2516594577 cites W2107146364 @default.
- W2516594577 cites W2108977706 @default.
- W2516594577 cites W2109422134 @default.
- W2516594577 cites W2111979722 @default.
- W2516594577 cites W2114091458 @default.
- W2516594577 cites W2114344299 @default.
- W2516594577 cites W2127132651 @default.
- W2516594577 cites W2130063902 @default.
- W2516594577 cites W2130797201 @default.
- W2516594577 cites W2134289299 @default.
- W2516594577 cites W2139248557 @default.
- W2516594577 cites W2147423506 @default.
- W2516594577 cites W2152425839 @default.
- W2516594577 cites W2156402689 @default.
- W2516594577 cites W2158897782 @default.
- W2516594577 cites W2165628866 @default.
- W2516594577 cites W2171757568 @default.
- W2516594577 cites W2338049369 @default.
- W2516594577 cites W2342095398 @default.
- W2516594577 doi "https://doi.org/10.3390/rs8090742" @default.
- W2516594577 hasPublicationYear "2016" @default.
- W2516594577 type Work @default.
- W2516594577 sameAs 2516594577 @default.
- W2516594577 citedByCount "15" @default.
- W2516594577 countsByYear W25165945772017 @default.
- W2516594577 countsByYear W25165945772019 @default.
- W2516594577 countsByYear W25165945772020 @default.
- W2516594577 countsByYear W25165945772021 @default.
- W2516594577 countsByYear W25165945772022 @default.
- W2516594577 countsByYear W25165945772023 @default.
- W2516594577 crossrefType "journal-article" @default.
- W2516594577 hasAuthorship W2516594577A5019532048 @default.
- W2516594577 hasAuthorship W2516594577A5033514563 @default.
- W2516594577 hasAuthorship W2516594577A5040296391 @default.
- W2516594577 hasAuthorship W2516594577A5059510144 @default.
- W2516594577 hasBestOaLocation W25165945771 @default.
- W2516594577 hasConcept C105795698 @default.
- W2516594577 hasConcept C110872660 @default.
- W2516594577 hasConcept C132943942 @default.
- W2516594577 hasConcept C142724271 @default.
- W2516594577 hasConcept C158709400 @default.
- W2516594577 hasConcept C159620131 @default.
- W2516594577 hasConcept C166957645 @default.
- W2516594577 hasConcept C18903297 @default.
- W2516594577 hasConcept C191897082 @default.
- W2516594577 hasConcept C192562407 @default.
- W2516594577 hasConcept C205649164 @default.
- W2516594577 hasConcept C2776133958 @default.
- W2516594577 hasConcept C2777016058 @default.
- W2516594577 hasConcept C33923547 @default.
- W2516594577 hasConcept C35187779 @default.
- W2516594577 hasConcept C37381756 @default.
- W2516594577 hasConcept C39432304 @default.
- W2516594577 hasConcept C62649853 @default.
- W2516594577 hasConcept C68709404 @default.
- W2516594577 hasConcept C71924100 @default.
- W2516594577 hasConcept C86803240 @default.
- W2516594577 hasConcept C94747663 @default.