|Title||Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE|
|Publication Type||Journal Article|
|Year of Publication||1995|
|Authors||Friedl, MA, Davis, FW, Michaelsen, J, Moritz, MA|
|Journal||Remote Sensing of Environment|
|Keywords||Documentation, General–Field Apparatus) (Mathematical Biology and Statistical Methods) (Ecology Environmental Biology–Bioclimatology and Biometeorology) (Ecology Environmental Biology–Plant) (Biophysics–Biocybernetics (1972- )) (Forestry and Forest Products) Pl, General–Field Methods) (Methods, Materials and Apparatus, Nomenclature and Terminology) (General Biology–Information, Plantae-Unspecified (General Biology–Taxonomy, Retrieval and Computer Applications) (Methods|
Biophysical inversion of remotely sensed data is constrained by the complexity of the remote sensing process. Variations in sensor response associated with solar and sensor geometries, surface directional reflectance, topography, atmospheric absorption and scattering, and sensor electrical-optical engineering interact in complex manners that are difficult to deconvolve and quantify in individual images or in time series of images. We have developed a model of the remote sensing process to allow systematic examination of these factors. The model is composed of three main components, including a ground scene model, an atmospheric model, and a sensor model, and may be used to simulate imagery produced by instruments such as the Landsat Thematic Mapper and the Advanced Very High Resolution Radiometer. Using this model, we examine the effect of subpixel variance in leaf area index (LAI) on relationships among LAI, the fraction of absorbed photosynthetically active radiation (FPAR), and the normalized difference vegetation index (NDVI). To do this, we use data from the first ISLSICP Field Experiment (FIFE) to parameterize ground scene properties within the model. Our results demonstrate interactions between sensor spatial resolution and spatial autocorrelation in ground scenes that produce a variety of effects in the relationship between both LAI and FPAR and NDVI. Specifically, sensor regularization, nonlinearity in the relationship between LAI and NDVI, and scaling the NDVI all influence the range, variance, and uncertainty associated with estimates of LAI and FPAR inverted from simulated NDVI data. These results have important implications for parameterization of land surface process models using biophysical variables such as LAI and FPAR estimated from remotely sensed data.