Abstract
Accurate estimation of polarized reflectance (Rp) of land surfaces is critical for remote sensing of aerosol optical properties. In the last two decades, many data-driven bidirectional polarization distribution function (BPDF) models have been proposed for accurate estimation of Rp, among which the generalized regression neural network (GRNN) based BPDF model has been reported to perform the best. GRNN is just a simple machine learning (ML) technique that can solve non-linear problems. Many ML techniques were reported to work well in solving non-linear problems and consequently may provide better performance in BPDF modeling. However, incorporating various ML techniques with BPDF modeling and comparing their performances have never been well documented. In this study, three widely used ML algorithms—i.e., support vector regression (SVR), K-nearest-neighbor (KNN), and random forest (RF)—were applied for BPDF modeling. Using measurements collected by the Polarization and Directionality of the Earth’s Reflectance onboard PARASOL satellite (POLDER/PARASOL), non-linear relationships between Rp and the input variables, i.e., Fresnel factor (Fp), scattering angle (SA), reflectance at 670 nm (R670) and 865 nm (R865), were built using these ML algorithms. Results showed that taking Fp, SA, R670, and R865 as input variables, the performance of the four ML-based BPDF models was quite similar. The KNN-based BPDF model provided slightly better results, and improved the accuracy of the semi-empirical BPDF models by 9.55% in terms of the overall root mean square error (RMSE). Experiments of different configuration of input variables suggested that using multi-band reflectance as input variables provided better results than using vegetation indices. The RF-based BPDF model using all reflectances at six bands as input variables produced the best results, improving the overall accuracy by 6.62% compared with the GRNN-based BPDF model. Among all the input variables, reflectance at absorbing spectral bands—e.g., 490 nm and 670 nm—played more significant roles in RF-based BPDF modeling due to the domination of polarized partition in total reflectance. Fresnel factor and scattering angle were also important for BPDF modeling. This study confirmed the feasibility of applying ML techniques to more accurate BPDF modeling, and the RF-based BPDF model proposed in this study can be used to increase the accuracy of remote sensing of the complete aerosol properties.
Highlights
Radiation scattered by the Earth’s surface is partially polarized [1,2,3]
The relatively worst results among the four machine learning (ML)-based models were obtained by the random forest (RF)-based model, which only gives the best results for International Geosphere Biosphere Program (IGBP) 01
The largest improvement of more than 18% was seen in IGBP 06 and 07, whereas the smallest improvement of 3.57% was found in IGBP 13, which is the surface type that Xie-Cheng model proposed for
Summary
Radiation scattered by the Earth’s surface is partially polarized [1,2,3]. Polarized reflectance (Rp) of the Earth’s surface reveals essential information as, on one hand, it characterizes the optical properties of land surface [4,5]; and on the other hand, it serves as the boundary condition for retrieval of aerosol optical properties [6,7,8]. Physical models reveal good physical interpretations given that they were built based on radiative transfer process They are not as accurate as expected, especially in forward scattering directions when viewing angle is large [10,19]. Monte-Carlo ray tracing based vector radiative transfer models for both leaf [25] and canopy [26,27] have been recently proposed by simulating the propagation of a large number of rays within hundreds of voxels. They give more accurate simulation of reflected polarized reflectance, but are rather complicated and require large computational costs. The data-driven BPDF models have been preferred for modeling polarized reflectance of land surfaces
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