Bidirectional Reflectance Sensitivity to Hemispherical Samplings: Implications for Snow Surface BRDF and Albedo Retrieval.
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| Title: | Bidirectional Reflectance Sensitivity to Hemispherical Samplings: Implications for Snow Surface BRDF and Albedo Retrieval. |
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| Authors: | Guo, Jing1,2 (AUTHOR), Jiao, Ziti1,2 (AUTHOR) jiaozt@bnu.edu.cn, Ding, Anxin3 (AUTHOR), Li, Zhilong1,2 (AUTHOR), Wang, Chenxia1,2 (AUTHOR), Yang, Fangwen1,2,3 (AUTHOR), Gao, Ge1,2 (AUTHOR), Tan, Zheyou1,2 (AUTHOR), Chen, Sizhe1,2 (AUTHOR), Dong, Xin1,2 (AUTHOR) |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 21, p3614. 25p. |
| Subjects: | Albedo, Remote sensing, Reflectance measurement, Reflectance, Snow cover, Sampling methods, Statistical sampling |
| Abstract: | Highlights: What are the main findings? Different illuminating–observing sampling distribution schemes can influence BRDF and albedo more for longer wavelengths. An angular information index (AII) was proposed based on a kernel-driven model, in cooperation with a weighting mechanism and information effectiveness to evaluate the information content of various sampling distribution schemes. What are the implications of the main findings? The angular sampling configuration is demonstrated to be a key factor influencing the accuracy of BRDF and albedo derived from the kernel-driven model. The AII provides a practical metric for guiding future sensor parameterization and for evaluating the quality of multi-angular data before operational application. Multi-angular remote sensing plays a critical role in the study domains of ecological monitoring, climate change, and energy balance. The successful retrieval of the surface Bidirectional Reflectance Distribution Function (BRDF) and albedo from multi-angular remote sensing observations for various applications relies on the sensitivity of an appropriate BRDF model to both the number and the sampling distribution of multi-angular observations. In this study, based on selected high-quality multi-angular datasets, we designed three representative angular sampling schemes to systematically analyze different illuminating–viewing configurations of the retrieval results in a kernel-driven BRDF model framework. We first proposed an angular information index (AII) by incorporating a weighting mechanism and information effectiveness to quantify the angular information content for the angular sampling distribution schemes. In accordance with the principle that observations on the principal plane (PP) provide the most representative anisotropic scattering features, the assigned weight gradually decreases from the PP towards the cross-principal plane (CPP). The information effectiveness is determined based on the cosine similarity between the observations, effectively reducing the information redundancy. With such a method, we assess the AII of the different sampling schemes and further analyze the impact of angular distribution on both BRDF inversion and the estimation of snow surface albedo, including White-Sky Albedo (WSA) and Black-Sky Albedo (BSA) based on the RossThick-LiSparseReciprocal-Snow (RTLSRS) BRDF model. The main conclusions are as follows: (1) The AII approach can serve as a robust indicator of the efficiency of different sampling schemes in BRDF retrieval, which indicates that the RTLSRS model can provide a robust inversion when the AII value exceeds a threshold of −2. (2) When the AII value reaches such a reliable level, different sampling schemes can reproduce the BRDF shapes of snow across different bands to somehow varying degrees. Specifically, observations with smaller view zenith angle (VZA) ranges can reconstruct a BRDF shape that amplifies the anisotropic effect of snow; in addition, the forward scattering tends to be more pronounced at larger solar zenith angles (SZAs), while the variations in BRDF shape reconstructed from off-PP observations depend on both wavelength and SZAs. (3) The relative differences in both BSA and WSA grow with increasing wavelength for all these sampling schemes, mostly within 5% for short bands but up to 30% for longer wavelengths. With this novel AII method to quantify the information contribution of multi-angular sampling distributions, this study offers valuable insights into several main multi-angular BRDF sampling strategies in satellite sensor missions, which relate to most of the fields of multi-angular remote sensing applications in engineering. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Different illuminating–observing sampling distribution schemes can influence BRDF and albedo more for longer wavelengths. An angular information index (AII) was proposed based on a kernel-driven model, in cooperation with a weighting mechanism and information effectiveness to evaluate the information content of various sampling distribution schemes. What are the implications of the main findings? The angular sampling configuration is demonstrated to be a key factor influencing the accuracy of BRDF and albedo derived from the kernel-driven model. The AII provides a practical metric for guiding future sensor parameterization and for evaluating the quality of multi-angular data before operational application. Multi-angular remote sensing plays a critical role in the study domains of ecological monitoring, climate change, and energy balance. The successful retrieval of the surface Bidirectional Reflectance Distribution Function (BRDF) and albedo from multi-angular remote sensing observations for various applications relies on the sensitivity of an appropriate BRDF model to both the number and the sampling distribution of multi-angular observations. In this study, based on selected high-quality multi-angular datasets, we designed three representative angular sampling schemes to systematically analyze different illuminating–viewing configurations of the retrieval results in a kernel-driven BRDF model framework. We first proposed an angular information index (AII) by incorporating a weighting mechanism and information effectiveness to quantify the angular information content for the angular sampling distribution schemes. In accordance with the principle that observations on the principal plane (PP) provide the most representative anisotropic scattering features, the assigned weight gradually decreases from the PP towards the cross-principal plane (CPP). The information effectiveness is determined based on the cosine similarity between the observations, effectively reducing the information redundancy. With such a method, we assess the AII of the different sampling schemes and further analyze the impact of angular distribution on both BRDF inversion and the estimation of snow surface albedo, including White-Sky Albedo (WSA) and Black-Sky Albedo (BSA) based on the RossThick-LiSparseReciprocal-Snow (RTLSRS) BRDF model. The main conclusions are as follows: (1) The AII approach can serve as a robust indicator of the efficiency of different sampling schemes in BRDF retrieval, which indicates that the RTLSRS model can provide a robust inversion when the AII value exceeds a threshold of −2. (2) When the AII value reaches such a reliable level, different sampling schemes can reproduce the BRDF shapes of snow across different bands to somehow varying degrees. Specifically, observations with smaller view zenith angle (VZA) ranges can reconstruct a BRDF shape that amplifies the anisotropic effect of snow; in addition, the forward scattering tends to be more pronounced at larger solar zenith angles (SZAs), while the variations in BRDF shape reconstructed from off-PP observations depend on both wavelength and SZAs. (3) The relative differences in both BSA and WSA grow with increasing wavelength for all these sampling schemes, mostly within 5% for short bands but up to 30% for longer wavelengths. With this novel AII method to quantify the information contribution of multi-angular sampling distributions, this study offers valuable insights into several main multi-angular BRDF sampling strategies in satellite sensor missions, which relate to most of the fields of multi-angular remote sensing applications in engineering. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17213614 |
