Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors

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Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, background): “Statistically, this boils down to a regression problem (Fernandes and Leblanc, 2005).”
Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup (2006, background): “Recent work on regression error models for transfer functions [ 76 ], [77] may help quantify the uncertainty in the LAI maps derived this way and the implications of comparing the high-resolution and moderate-resolution LAI surfaces.”
Use and misuse of the reduced major axis for line-fitting. (2009, background): “In some literature, the term ‘‘equation error’’ is used to represent what is called here natural variation—the lack of fit that would exist if technique error and sampling variation were eliminated (e.g., Carroll and Ruppert, 1996; McArdle, 2003; Fernandes and Leblanc, 2005; Warton et al., 2006).”
Consistency and asymptotic distribution of the Theil–Sen estimator (2008, methodology): “Akritas et al. (1995) applied it to astronomy and Fernandes and Leblanc (2005) to remote sensing.”