Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model

Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to...

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Vydáno v:Remote sensing of environment Ročník 121; s. 415 - 425
Hlavní autoři: Si, Yali, Schlerf, Martin, Zurita-Milla, Raul, Skidmore, Andrew, Wang, Tiejun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY Elsevier Inc 01.06.2012
Elsevier
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ISSN:0034-4257, 1879-0704
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Abstract Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R2=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R2=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R2=0.36, RMSE=1.77, NRMSE=28%; CCC: R2=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (<2) and saturated at higher LAI values (≥4), and the multi-biome CCC was consistently overestimated through the whole data range. Similar temporal trajectories of grassland LAI and CCC estimates were observed using these two approaches, but the multi-biome trajectory consistently produced larger values than the single-biome trajectory. The spatio-temporal variation of grassland LAI and CCC estimated by the single-biome approach was shown to be closely associated with agricultural practices. Our results underline the potential of mapping grassland LAI and CCC using the PROSAIL model and MERIS satellite data. ► A single-biome approach was proposed to estimate grassland properties from MERIS. ► The proposed approach yields higher accuracy than the MERIS global land products. ► Retrieved temporal variation is consistent with phenology and agricultural practices.
AbstractList Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R2=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R2=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R2=0.36, RMSE=1.77, NRMSE=28%; CCC: R2=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (<2) and saturated at higher LAI values (≥4), and the multi-biome CCC was consistently overestimated through the whole data range. Similar temporal trajectories of grassland LAI and CCC estimates were observed using these two approaches, but the multi-biome trajectory consistently produced larger values than the single-biome trajectory. The spatio-temporal variation of grassland LAI and CCC estimated by the single-biome approach was shown to be closely associated with agricultural practices. Our results underline the potential of mapping grassland LAI and CCC using the PROSAIL model and MERIS satellite data. ► A single-biome approach was proposed to estimate grassland properties from MERIS. ► The proposed approach yields higher accuracy than the MERIS global land products. ► Retrieved temporal variation is consistent with phenology and agricultural practices.
Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R 2 =0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R 2 =0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R 2 =0.36, RMSE=1.77, NRMSE=28%; CCC: R 2 =0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (<2) and saturated at higher LAI values ( greater than or equal to 4), and the multi-biome CCC was consistently overestimated through the whole data range. Similar temporal trajectories of grassland LAI and CCC estimates were observed using these two approaches, but the multi-biome trajectory consistently produced larger values than the single-biome trajectory. The spatio-temporal variation of grassland LAI and CCC estimated by the single-biome approach was shown to be closely associated with agricultural practices. Our results underline the potential of mapping grassland LAI and CCC using the PROSAIL model and MERIS satellite data.
Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R²=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R²=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R²=0.36, RMSE=1.77, NRMSE=28%; CCC: R²=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (<2) and saturated at higher LAI values (≥4), and the multi-biome CCC was consistently overestimated through the whole data range. Similar temporal trajectories of grassland LAI and CCC estimates were observed using these two approaches, but the multi-biome trajectory consistently produced larger values than the single-biome trajectory. The spatio-temporal variation of grassland LAI and CCC estimated by the single-biome approach was shown to be closely associated with agricultural practices. Our results underline the potential of mapping grassland LAI and CCC using the PROSAIL model and MERIS satellite data.
Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of grassland ecosystems. The Medium Resolution Imaging Spectrometer (MERIS) is a promising sensor for measuring and monitoring grasslands due to its high spectral resolution, medium spatial resolution and a two- to three-day repeat cycle. However, thus far the multi-biome MERIS land products have limited consistency with in-situ measurements of leaf area index (LAI), while the multi-biome canopy chlorophyll content (CCC) has not been validated yet with in-situ data. This study proposes a single-biome approach to estimate grassland LAI (a surrogate of grass quantity) and leaf chlorophyll content (LCC) and CCC (surrogates of grass quality) using the inversion of the PROSAIL model and MERIS reflectance. Both multi-biome and single-biome approaches were validated using two-season in-situ data sets and the temporal consistency was analyzed using time-series of MERIS data. The single-biome approach showed a consistently better performance for estimating LAI (R 2=0.70, root mean square error (RMSE)=1.02, normalized RMSE (NRMSE)=16%) and CCC (R 2=0.61, RMSE=0.36, NRMSE=23%) compared with the multi-biome approach (LAI: R 2=0.36, RMSE=1.77, NRMSE=28%; CCC: R 2=0.47, RMSE=1.33, NRMSE=84%). However, both single-biome and multi-biome approaches failed to retrieve LCC. The multi-biome LAI was overestimated at lower LAI values (
Author Schlerf, Martin
Zurita-Milla, Raul
Skidmore, Andrew
Wang, Tiejun
Si, Yali
Author_xml – sequence: 1
  givenname: Yali
  surname: Si
  fullname: Si, Yali
  email: yali@itc.nl
  organization: Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing, 100084, China
– sequence: 2
  givenname: Martin
  surname: Schlerf
  fullname: Schlerf, Martin
  email: schlerf@crpgl.lu
  organization: Centre de Recherche Public, Gabriel Lippmann, L-4422 Belvaux, Luxembourg
– sequence: 3
  givenname: Raul
  surname: Zurita-Milla
  fullname: Zurita-Milla, Raul
  email: zurita-milla@itc.nl
  organization: Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, 7500AA Enschede, The Netherlands
– sequence: 4
  givenname: Andrew
  surname: Skidmore
  fullname: Skidmore, Andrew
  email: skidmore@itc.nl
  organization: Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, 7500AA Enschede, The Netherlands
– sequence: 5
  givenname: Tiejun
  surname: Wang
  fullname: Wang, Tiejun
  email: tiejun@itc.nl
  organization: Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, 7500AA Enschede, The Netherlands
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IsPeerReviewed true
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Keywords Grassland
Chlorophyll
PROSAIL
LAI
Quality
Quantity
MERIS
LUT
time variations
data
cycles
global
measurement sensor
Modeling
Biome
grasslands
Gramineae
spatial resolution
models
inverse problem
Chlormequat
Plantae
chlorophyll
Plant leaf
cartography
monitoring
in situ
quality
Measurement in situ
angiosperms
Content
Monocotyledoneae
ecosystems
Spermatophyta
Canopy(vegetation)
Leaf area index
Language English
License CC BY 4.0
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c477t-a74d571773e60233da3962968bc283ead7ca3bdeb2d51d712b27e141d54875323
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ObjectType-Feature-2
content type line 23
PQID 1710233928
PQPubID 24069
PageCount 11
ParticipantIDs wageningen_narcis_oai_library_wur_nl_wurpubs_432247
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proquest_miscellaneous_1735918189
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pascalfrancis_primary_25892998
crossref_citationtrail_10_1016_j_rse_2012_02_011
crossref_primary_10_1016_j_rse_2012_02_011
elsevier_sciencedirect_doi_10_1016_j_rse_2012_02_011
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PublicationCentury 2000
PublicationDate 2012-06-01
PublicationDateYYYYMMDD 2012-06-01
PublicationDate_xml – month: 06
  year: 2012
  text: 2012-06-01
  day: 01
PublicationDecade 2010
PublicationPlace New York, NY
PublicationPlace_xml – name: New York, NY
PublicationTitle Remote sensing of environment
PublicationYear 2012
Publisher Elsevier Inc
Elsevier
Publisher_xml – name: Elsevier Inc
– name: Elsevier
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Snippet Accurate estimates of the quantity and quality of grasslands, as they vary in space and time and from regional to global scales, furthers our understanding of...
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SubjectTerms Animal, plant and microbial ecology
Applied geophysics
Biological and medical sciences
canopy
canopy reflectance
Chlorophyll
chlorophyll content
Chlorophylls
Consistency
data collection
Earth sciences
Earth, ocean, space
ecosystems
Estimates
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Grasses
Grassland
Grasslands
heterogeneous grassland
hyperspectral measurements
image analysis
Internal geophysics
LAI
leaf
Leaf area index
leaves
LUT
MERIS
monitoring
PROSAIL
Quality
Quantity
radiative-transfer models
reflectance
reflectance data
remote sensing
remote-sensing data
spatial variation
Teledetection and vegetation maps
Temporal logic
temporal variation
Trajectories
vegetation indexes
Title Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model
URI https://dx.doi.org/10.1016/j.rse.2012.02.011
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