Model inversion of satellite-measured reflectances for obtaining surface biophysical and bidirectional reflectance characteristics of grassland

Journal Article · Mon Sep 01 04:00:00 EDT 1997 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/S0034-4257(96)00115-0· OSTI ID:516739

Gao, W; Lesht, B M ^[1]

---

1. Argonne National Lab., IL (United States). Environmental Research Div.

An inversion method that can be used to derive information about biophysical and radiative conditions of a grassland surface from remotely sensed surface spectral reflectances is described. A previously developed simple surface bidirectional reflectance model was expanded for use in this inversion process. In the inversion process, a multidimensional optimization scheme is used to obtain a set of model parameters that minimizes the difference between modeled and observed reflectances. The effect of the number of data points used in the inversion on the accuracy of the model inversion was investigated with ground-based hemispheric scan measurements of surface spectral reflectances over a tallgrass prairie in Kansas. The result shows that the total inversion error consists of minimum or systematic error and ambiguity error. The former results from uncertainties in the accuracies of the model and measurements, whereas the latter is caused by the ambiguity of the model inversion when a limited number of data points are used. The systematic error is independent of the number of data points, whereas the ambiguity error decreases sharply with increasing data points when the number of data points is small. An inversion method with appropriate data grouping applied to the reflectance data from the Advanced Very High Resolution Radiometer is shown to improve estimates of surface parameters over the grassland site. Relative differences between measured surface bidirectional reflectances and those calculated with the derived surface parameters range from 6% to 15%.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL
• Sponsoring Organization: USDOE Office of Energy Research, Washington, DC (United States)
• DOE Contract Number: W-31109-ENG-38
• OSTI ID: 516739
• Journal Information: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Vol. 59; ISSN RSEEA7; ISSN 0034-4257
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
DATA ANALYSIS
DATA COVARIANCES
MATHEMATICAL MODELS
RADIOMETERS
RANGELANDS
REMOTE SENSING
RESOURCE ASSESSMENT