A transformative approach to enhance the parameter information from microwave and infrared remote sensing measurements

Abstract

In observational science, data is the foundation of a scientific model; satellite-derived parameters serve as data for earth sciences models. The building of science is imprecise if data is ambiguous. Remote sensing 'big data' provides a wealth of information for unlocking the mysteries of earth sciences. The parameter estimation from remote sensing measurements is extremely ill-posed and the inverse method plays a significant role in extracting parameter information. In this paper, predominant stochastic inverse methods in satellite retrieval applications are critically investigated from different schools of thought and several basic flaws are revealed, e.g. error being treated as definite information. The major drawbacks of these methods include a high reliance on a priori information and binding the satellite retrievals to in situ measurements. A fundamentally different and transformative approach is explored as an alternative. A rational, reliable, and repeatable determination of geophysical parameter values from remote sensing measurements is possible using the total least squares based deterministic inverse method. It is a physical model-based data-driven optimization, where the error quantity is extracted from the problem itself for regularization on a case-by-case basis using singular vector decomposition of the augmented function of the Jacobian and the residual. By moving from the prevalent to the proposed inverse method, a paradigm shift in results from "information loss" to 'information gain' is achieved.

Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation

“…This is due to the fact that the PDSST suite can use the MWIR channels for daytime SST retrieval and there is little scope to use MWIR channels in regression-based daytime SST retrievals. Even though a physical-based estimation method, namely, OEM, has the capability to include MWIR channels in daytime SST retrieval, the comparative information content with respect to the error in retrieval of PDSST and OEM shows similar conclusions to the results reported in earlier publications [10], [25], [31]- [34]. For example, retrieval error is higher than the a priori error and the OEM error is comparable with the error in PDSST retrieval when the exact a priori error is supplied as an input.…”

Enhancing Information Content in the Satellite-Derived Daytime Infrared Sea Surface Temperature Dataset Using a Transformative Approach

“…This study confirms that the PDSST retrieval scheme can produce unambiguous SST retrieval from satellite measurements to open up a new frontier in oceanic science by increasing the coverage and resolution in space and time of high-quality observations. Additionally, the physical deterministic algorithm is generic and is found to be superior to other stochastic retrieval methods for profile retrieval using remote sensing empirical data (Koner and Drummond, 2008a,b;Koner et al, 2010Koner and Dash, 2018;Koner, 2020a). This work endorses that the transformative inverse approach of PDSST can maximize unambiguous quantitative information from realistic satellite remote sensing measurements.…”