Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
 
 
DownloadE-Mail
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Geostatistical regularization of inverse models for the retrieval of vegetation biophysical variables

Atzberger, C., Berger [Richter], K. (2009): Geostatistical regularization of inverse models for the retrieval of vegetation biophysical variables - Proceedings Volume 7478, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX, SPIE Remote Sensing (Berlin, Germany 2009), 74781O.
https://doi.org/10.1117/12.830009

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5027979 Versions-Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5027979_1
Genre: Konferenzbeitrag

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Atzberger, C.1, Autor
Berger [Richter], Katja2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
20 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146023              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: The robust and accurate retrieval of vegetation biophysical variables using radiative transfer models (RTM) is seriously hampered by the ill-posedness of the inverse problem. With this research we further develop our previously published (object-based) inversion approach [Atzberger (2004)]. The object-based RTM inversion takes advantage of the geostatistical fact that the biophysical characteristics of nearby pixel are generally more similar than those at a larger distance. A two-step inversion based on PROSPECT+SAIL generated look-up-tables is presented that can be easily implemented and adapted to other radiative transfer models. The approach takes into account the spectral signatures of neighboring pixel and optimizes a common value of the average leaf angle (ALA) for all pixel of a given image object, such as an agricultural field. Using a large set of leaf area index (LAI) measurements (n = 58) acquired over six different crops of the Barrax test site (Spain), we demonstrate that the proposed geostatistical regularization yields in most cases more accurate and spatially consistent results compared to the traditional (pixel-based) inversion. Pros and cons of the approach are discussed and possible future extensions presented.

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2009-10-07
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1117/12.830009
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: SPIE Remote Sensing
Veranstaltungsort: Berlin, Germany
Start-/Enddatum: 2009-10-09 - 2009-10-09

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Proceedings Volume 7478, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 7478 Artikelnummer: 74781O Start- / Endseite: - Identifikator: -