Biophysical parameter mapping from optical remote sensing images always require an intermediate modelling step to transform spectral observations into useful estimates. This modelling step can be approached with either statistical, physical or hybrid methods. Here emphasis is put on statistical methods. Statistical methods can be categorized into either parametric or nonparametric approaches.
The here presented Spectral Indices (SIs) assessment toolbox provides a suite of parametric techniques in one toolbox to enable semiautomatic mapping of surface biophysical variables.
Conceptual design of the Spectral Indices toolbox to map biophysical variables.
The principle basically is to correlate mathematical combinations of reflectances measured in different wavelength ranges or large spectral bands with biophysical parameters of interest. This procedure can be considered as a parametric spectral index (SI) modelling approach.
The SI assessment toolbox enables the analysis and assessment of the accuracy of an indefinite number of SI models. Basically, the module offers a systematic approach for the assessment of all possible 1, 2, 3 up to 10-band SI formulations. Datasets can be divided into calibration and validation subsets. These datasets may originate from simulations, e.g. as generated by the optical radiative transfer models in ARTMO, or from field campaigns.
ARTMO’s Spectral Indices toolbox v.1.29
In short, the Spectral Indices (SI) toolbox enables:
- To apply and evaluate multiple spectral indices, band combinations and curve fitting functions (e.g., linear, exponential, power or polynomial functions)according to customized calibration strategies, e.g. with different noise and train/validation partitioning.
- Data can either come from radiative transfer models or from field measurements, or can be mixed.
- If a land cover map is provided, then for each land cover class a distinct SI models can be optimized.
- When having validation data available then multiple spectral indices strategies can be analyzed against the validation dataset by using goodness-of-fit statistics. Results are stored in a relational database.
- The best performing SI strategy can be loaded and applied to an imagery, or a model can be directly developed and applied to an imagery, for mapping applications.