View in EDS

Early detection of biomass production deficit hot-spots in semi-arid environment using FAPAR time series and a probabilistic approach.

Saved in:
Bibliographic Details
Title: Early detection of biomass production deficit hot-spots in semi-arid environment using FAPAR time series and a probabilistic approach.
Authors: Meroni, M.1 michele.meroni@jrc.ec.europa.eu, Fasbender, D.1, Kayitakire, F.1, Pini, G.2, Rembold, F.1, Urbano, F.1, Verstraete, M.M.3
Source: Remote Sensing of Environment. Feb2014, Vol. 142, p57-68. 12p.
Subjects: Biomass production, Time series analysis, Probability theory, Flood warning systems, Photosynthesis, Climatology
Abstract: Abstract: Early warning monitoring systems in food-insecure countries aim to detect unfavourable crop and pasture conditions as early as possible during the growing season. This manuscript describes a procedure to estimate the probability of experiencing an end-of-season biomass production deficit during the on-going season based on a statistical analysis of Earth Observation data. A 15-year time series of the Fraction of Absorbed Photosynthetically Active Radiation from the SPOT-VEGETATION instrument is used to characterize the climatological development of vegetation, its variability and its current status. Forecasts of overall seasonal performances, expressed in terms of the probability of experiencing a critical deficit at the end of the growing season, are updated regularly whenever a new satellite observation is made available. Results and performances of the method are discussed for croplands and pastures in the Sahel. [Copyright &y& Elsevier]
Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 94051453
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Early detection of biomass production deficit hot-spots in semi-arid environment using FAPAR time series and a probabilistic approach.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Meroni%2C+M%2E%22">Meroni, M.</searchLink><relatesTo>1</relatesTo><i> michele.meroni@jrc.ec.europa.eu</i><br /><searchLink fieldCode="AR" term="%22Fasbender%2C+D%2E%22">Fasbender, D.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kayitakire%2C+F%2E%22">Kayitakire, F.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pini%2C+G%2E%22">Pini, G.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Rembold%2C+F%2E%22">Rembold, F.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Urbano%2C+F%2E%22">Urbano, F.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Verstraete%2C+M%2EM%2E%22">Verstraete, M.M.</searchLink><relatesTo>3</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing+of+Environment%22">Remote Sensing of Environment</searchLink>. Feb2014, Vol. 142, p57-68. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Biomass+production%22">Biomass production</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Flood+warning+systems%22">Flood warning systems</searchLink><br /><searchLink fieldCode="DE" term="%22Photosynthesis%22">Photosynthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Climatology%22">Climatology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract: Early warning monitoring systems in food-insecure countries aim to detect unfavourable crop and pasture conditions as early as possible during the growing season. This manuscript describes a procedure to estimate the probability of experiencing an end-of-season biomass production deficit during the on-going season based on a statistical analysis of Earth Observation data. A 15-year time series of the Fraction of Absorbed Photosynthetically Active Radiation from the SPOT-VEGETATION instrument is used to characterize the climatological development of vegetation, its variability and its current status. Forecasts of overall seasonal performances, expressed in terms of the probability of experiencing a critical deficit at the end of the growing season, are updated regularly whenever a new satellite observation is made available. Results and performances of the method are discussed for croplands and pastures in the Sahel. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=94051453
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.rse.2013.11.012
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 57
    Subjects:
      – SubjectFull: Biomass production
        Type: general
      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Flood warning systems
        Type: general
      – SubjectFull: Photosynthesis
        Type: general
      – SubjectFull: Climatology
        Type: general
    Titles:
      – TitleFull: Early detection of biomass production deficit hot-spots in semi-arid environment using FAPAR time series and a probabilistic approach.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Meroni, M.
      – PersonEntity:
          Name:
            NameFull: Fasbender, D.
      – PersonEntity:
          Name:
            NameFull: Kayitakire, F.
      – PersonEntity:
          Name:
            NameFull: Pini, G.
      – PersonEntity:
          Name:
            NameFull: Rembold, F.
      – PersonEntity:
          Name:
            NameFull: Urbano, F.
      – PersonEntity:
          Name:
            NameFull: Verstraete, M.M.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 25
              M: 02
              Text: Feb2014
              Type: published
              Y: 2014
          Identifiers:
            – Type: issn-print
              Value: 00344257
          Numbering:
            – Type: volume
              Value: 142
          Titles:
            – TitleFull: Remote Sensing of Environment
              Type: main
ResultId 1