Abstract
Quantitative uncertainty analysis is generally taken as an indispensable step in the calibration of a remote sensor. A full uncertainty propagation chain has not been established to set up the metrological traceability for surface reflectance inversed from remotely sensed images. As a step toward this goal, we proposed an uncertainty analysis method for the two typical semi-empirical topographic correction models, i.e., C and Minnaert, according to the ‘Guide to the Expression of Uncertainty in Measurement (GUM)’. We studied the data link and analyzed the uncertainty propagation chain from the digital elevation model (DEM) and at-sensor radiance data to the topographic corrected radiance. We obtained spectral uncertainty characteristics of the topographic corrected radiance as well as its uncertainty components associated with all of the input quantities by using a set of Earth Observation-1 (EO-1) Hyperion data acquired over a rugged soil surface partly covered with snow. Firstly, the relative uncertainty of cover types with lower radiance values was larger for both C and Minnaert corrections. Secondly, the trend of at-sensor radiance contributed to a spectral feature, where the uncertainty of the topographic corrected radiance was poor in bands below 1400 nm. Thirdly, the uncertainty components associated with at-sensor radiance, slope, and aspect dominated the total combined uncertainty of corrected radiance. It was meaningful to reduce the uncertainties of at-sensor radiance, slope, and aspect for reducing the uncertainty of corrected radiance and improving the data quality. We also gave some suggestions to reduce the uncertainty of slope and aspect data.
Highlights
The development of imaging spectroscopy leads optical remote sensing to hyperspectral remote sensing
We summed up the data links of the C and Minnaert models and traced the uncertainty sources back to the uncertainty of at-sensor radiance after radiometric calibration, the vertical accuracy of digital elevation model (DEM), the horizontal resolution of DEM, the approximation errors of solar illumination angles, and the uncertainty of linear fitting
We proposed the uncertainty analysis method for semi-empirical topographic correction models and preliminarily established the uncertainty propagation chains
Summary
The development of imaging spectroscopy leads optical remote sensing to hyperspectral remote sensing. For atmospheric correction of remote sensing data over a flat terrain, a few studies on uncertainty analysis were conducted [9,10]. For remote sensing data over rugged terrains, topographic correction is the step that follows radiometric calibration. The SCS correction considers vegetation gravitropism for mountainous forest, based on the geometric relationship of "Sun–canopy–sensor" These two physical models only consider direct solar irradiance onto the rugged terrain, and often bring obvious overcorrection. We selected two classical semi-empirical topographic correction models, i.e., C correction and Minnaert correction, to propose an uncertainty analysis method for a typical semi-empirical topographic correction process. We stressed on the uncertainty propagation analysis, more in general cases than for specific surface types, because topographic correction is just one of the pre-processing steps to get better remotely sensed applications (i.e., classification). The metrological traceability of the C correction and Minnaert correction processes was preliminarily realized
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