File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A framework for consistent estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from MODIS time-series data

TitleA framework for consistent estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from MODIS time-series data
Authors
KeywordsConsistent estimation
data assimilation
ensemble Kalman filter technique (EnKF)
fraction of absorbed photosynthetically active radiation (FAPAR)
leaf area index (LAI)
MODerate Resolution Imaging Spectroradiometer (MODIS)
surface albedo
Issue Date2015
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2015, v. 53, n. 6, p. 3178-3197 How to Cite?
AbstractCurrently available land-surface parameter products are generated using parameter-specific algorithms from various satellite data and contain several inconsistencies. This paper developed a new data assimilation framework for consistent estimation of multiple land-surface parameters from time-series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112° spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively.
Persistent Identifierhttp://hdl.handle.net/10722/321749
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXiao, Zhiqiang-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorWang, Jindi-
dc.contributor.authorXie, Donghui-
dc.contributor.authorSong, Jinling-
dc.contributor.authorFensholt, Rasmus-
dc.date.accessioned2022-11-03T02:21:11Z-
dc.date.available2022-11-03T02:21:11Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2015, v. 53, n. 6, p. 3178-3197-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/321749-
dc.description.abstractCurrently available land-surface parameter products are generated using parameter-specific algorithms from various satellite data and contain several inconsistencies. This paper developed a new data assimilation framework for consistent estimation of multiple land-surface parameters from time-series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112° spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectConsistent estimation-
dc.subjectdata assimilation-
dc.subjectensemble Kalman filter technique (EnKF)-
dc.subjectfraction of absorbed photosynthetically active radiation (FAPAR)-
dc.subjectleaf area index (LAI)-
dc.subjectMODerate Resolution Imaging Spectroradiometer (MODIS)-
dc.subjectsurface albedo-
dc.titleA framework for consistent estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from MODIS time-series data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2014.2370071-
dc.identifier.scopuseid_2-s2.0-85027932635-
dc.identifier.volume53-
dc.identifier.issue6-
dc.identifier.spage3178-
dc.identifier.epage3197-
dc.identifier.isiWOS:000351063800014-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats