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Remote sensing statistical inference: basic theory and forward simulation of water–air statistical radiative transfer

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Abstract

We proposed a new generic qualitative-quantitative method for remote sensing image analysis: statistical inference, which is different from the traditional qualitative remote sensing method, classification, and the quantitative remote sensing method, inversion. Remote sensing statistical inference is theoretically based on statistical optics, radiative transfer, and probability distribution transfer, and primarily studies how the probability distributions of surface properties affect the spectral probability distributions (SPD) of the sensor-observed optical signals, and further how to infer the probability distributions of surface properties from the SPDs so that we can obtain qualitative and quantitative information about the earth surface object. In this study, we introduced the basic concepts, principles, applicability, and research topics of statistical inference as well as its advantages over remote sensing classification and inversion. Using the Monte-Carlo and Hydrolight simulations, we studied the statistical transfer of probability distribution of ocean color components in complex water, found that the simulated results are basically consistent with the theoretical predictions, and confirmed that the probability distributions of water’s inherent optical properties have significant impacts on the SPDs of its apparent optical properties.

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Acknowledgements

This study is supported by National Natural Science Foundation of China (No. 41971373, 41876031). The author also thanks Zeliang Zhang, Shuna Pang, and Zaiqiao Yang for their work of data and image processing.

Funding

This study was supported by National Natural Science Foundation of China (No. 41971373, 41876031).

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  1. Institute of Marine Sensing and Network, Ocean College, Zhejiang University, Hangzhou, China

    Weining Zhu

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Communicated by: H. Babaie

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Zhu, W. Remote sensing statistical inference: basic theory and forward simulation of water–air statistical radiative transfer. Earth Sci Inform 14, 2145–2159 (2021). https://doi.org/10.1007/s12145-021-00661-y

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