Paper
23 January 2001 Coupling remote sensing observation models and a growth model for improved retrieval of (geo)biophysical information from optical remote sensing data
Heike Bach, Wouter Verhoef, Karl Schneider
Author Affiliations +
Abstract
In the ESA study GeoBIRD (Geo- and Biophysical Information from Remote Sensing Data) the process-based vegetation growth model PROMET-V was coupled to the GeoSAIL canopy reflectance model for the retrieval of spatially distributed biophysical parameters from surface reflectance images derived from Landsat TM data. The raster based PROMET-V model calculates plant growth, water and nitrogen fluxes using meteorological data, a land use map, and a soil map. In this study the total leaf area index (LAI), the fraction brown leaf area and surface soil moisture, as modelled in PROMET-V, were used in conjunction with GeoSAIL, to model the surface reflectance spectra. Besides modelling the canopy reflectance GeoSAIL, also accounts for the soil reflectance using the dry soil spectrum and surface moisture as inputs. By minimising the difference between observed reflectance spectra derived from LANDSAT images and the modelled surface reflectance spectra, the total LAI, fraction of brown leaves and surface soil moisture were estimated. This optimisation process is constrained by physically and plant physiologically meaningful boundaries in order to obtain valid results and accelerate the process. By re-initialising the PROMET-V model, the retrieved and simulated LAI were matched. This leads to improved model results regarding biomass and yield.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Heike Bach, Wouter Verhoef, and Karl Schneider "Coupling remote sensing observation models and a growth model for improved retrieval of (geo)biophysical information from optical remote sensing data", Proc. SPIE 4171, Remote Sensing for Agriculture, Ecosystems, and Hydrology II, (23 January 2001); https://doi.org/10.1117/12.413920
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Cited by 28 scholarly publications.
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KEYWORDS
Remote sensing

Data modeling

Reflectivity

Soil science

Atmospheric modeling

Earth observing sensors

Satellites

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