Ordered Test Site Method for Onboard Measurement Results Validation of Medium Resolution Spectroradiometers
DOI:
https://doi.org/10.20535/RADAP.2022.88.35-41Keywords:
optimization, cost function, validation, remote sensing, test sitesAbstract
The reduction or elimination of uncertainties is one of the main tasks in the analysis of remote sensing data. For this purpose, the upscaling and downscaling of spectroradiometric data operations are widely used. The downscaling operation is particularly used to validate medium resolution spectroradiometer data. The onboard measurement validation data is a complex task and it includes the solution of such important subtasks as (a) selection of the test site type; (b) determining the site size; (c) determining the sampling order. At the same time, after carrying out selective measurements, the question of scaling up (upscaling or generalization) of the obtained ground data arises, its purpose is to carry out validation of satellite data with low spatial resolution. Solving the problem of remote sensing data validation, terrestrial test sites are often used, their heterogeneity must always be taken into account. This problem is usually solved by applying special weighting coefficients and performing temporary periodic measurements, then using the regularization procedure for the averaged results of iterative calculations. In the absence of temporary changes, the need for regularization is eliminated. In this case, as an alternative, the method of the ordered test section can be proposed, it allows to determine the weight coefficients of the ground validation measurement results, providing a minimum of the newly proposed quadrature cost function. To solve the problem of achieving the proposed cost function minimum, the ordered subsections method as part of a single heterogeneous test section is proposed, the measurements are carried out by a sensor mounted on a low-flying carrier. The optimization problem is formulated to calculate the correction coefficients for the measurement results, where the sum of the squares of difference between the corrected data and the known representative estimate is minimized. The optimization problem is solved using a certain restrictive condition imposed on the sum of the correction factors.
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