Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Surface soil moisture quantification and validation based on hyperspectral data and field measurements

Urheber*innen
/persons/resource/soerenh

Haubrock,  Sören
5.4 Hydrology, 5.0 Earth Surface Processes, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/chabri

Chabrillat,  Sabine
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Kuhnert,  M.
External Organizations;

Hostert,  P.
External Organizations;

/persons/resource/charly

Kaufmann,  Hermann
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in GFZpublic verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Haubrock, S., Chabrillat, S., Kuhnert, M., Hostert, P., Kaufmann, H. (2008): Surface soil moisture quantification and validation based on hyperspectral data and field measurements. - Journal of Applied Remote Sensing, 2 (023552), 1.
https://doi.org/10.1117/1.3059191


https://gfzpublic.gfz-potsdam.de/pubman/item/item_238296
Zusammenfassung
Surface soil moisture information is needed for monitoring and modeling surface processes at various spatial scales. While many reflectance based soil moisture quantification models have been developed and validated in laboratories, only few were applied from remote sensing platforms and thoroughly validated in the field. This paper addresses the issues of a) quantifying surface soil moisture with very high resolution spectral measurements from remote sensors in a landscape with sandy substrates and low vegetation cover as well as b) comprehensively validating these results in the field. For this purpose, the recently developed Normalized Soil Moisture Index (NSMI) has been analyzed for its applicability to airborne hyperspectral remote sensing data. Three HyMap scenes from 2004 and 2005 were collected from a lignite mining area in southern Brandenburg, Germany. An NSMI model was calibrated (R2=0.92) and surface soil moisture maps were calculated based on this model. An in-situ surface soil moisture map based on a combination of Frequency Domain Reflectometry (FDR) and gravimetric data allowed for validating each image pixel (R2=0.82). In addition, a qualitative multitemporal comparison between two consecutive NSMI datasets from 2004 was performed and validated, showing an increase in estimated surface soil moisture corresponding with field measurements and precipitation data. The study shows that the NSMI is appropriate for modeling surface soil moisture from high spectral-resolution remote sensing data. The index leads to valid estimations of soil moisture values below field capacity in an area with sandy substrates and low vegetation cover (NDVI < 0.3). Further studies will analyze the validity of the NSMI for surface soil moisture estimation from spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMap) in different landscapes.