The Impact on Triple/N-Way Collocation-Based Validation of Remote Sensing Products Due to Non-Ideal Error Statistics.
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| Title: | The Impact on Triple/N-Way Collocation-Based Validation of Remote Sensing Products Due to Non-Ideal Error Statistics. |
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| Authors: | Balasubramaniam, Rajeswari1 (AUTHOR) rajibala@umich.edu, Ruf, Christopher1 (AUTHOR) |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 22, p3751. 21p. |
| Subjects: | Remote sensing, Collocation methods, Analysis of variance, Statistical errors, Computer simulation, Calibration |
| Abstract: | Highlights: This study evaluates the impact of some of the real-world scenarios on the triple/N-way collocation (TC/NWC) method using the proposed numerical simulator. By perturbing statistical parameters and input distributions, it identifies which assumptions critically affect performance and provides empirical corrections to improve real-world applicability in satellite data validation. What are the main findings? TC/NWC remains robust to changes in true signal distribution (e.g., Rayleigh, Weibull, or empirical) and to moderate levels of bias or reference noise, with estimated error variances typically within a few percent of true values. Introducing correlated errors between systems and scaling deviations in the reference—cause large biases or algorithmic instability, which the simulator quantitatively characterizes. What are the implications of the main findings? The developed simulator enables users to derive empirical correction factors for bias, scaling, and correlated-error effects, improving accuracy of TC/NWC-based error variance estimation for their remote sensing systems. The ideas proposed here enhance the realism and reliability of collocation analysis for operational remote sensing applications. Triple/N-way collocation is a statistical analysis tool used to estimate the individual error variances of simultaneous observations of a physical quantity by three or more distinct systems. The tool is widely used to validate remote sensing data products such as ocean surface winds and soil moisture retrieved by satellite sensors, where simultaneous observations by different systems are common. However, the method relies on several assumptions about the statistical properties of the observations that are not always valid in a real-world scenario. We test the validity of these assumptions using a numerical simulator and assess their impact on error variance estimates. Some of these assumptions, that the errors are uncorrelated between observing systems or the reference system having a non-unity scaling factor, etc., are found to have a large impact on estimates of error variance when violated. The violation of some other assumptions is found to be less impactful. The simulator also provides corrections to the erroneous estimates of error variances that result when the underlying assumptions are violated. Additionally, we present a new, more general version of the collocation analysis tool that accommodates cases where the error variance in an observing system has a dependence on the true signal. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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