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Evaluation of three BRDF models’ performance using spaceborne POLDER snow data

  • role： First author第一作者
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

  • Email：guojing0404@mail.bnu.edu.cn
  • Introduction：郭静，研究方向为定量遥感、雪的反射率模型等研究工作。E-mail：guojing0404@mail.bnu.edu.cn
GUO Jing12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

  • Email：jiaozt@bnu.edu.cn
  • Introduction：焦子锑，研究方向为多角度光学遥感的建模、反演与应用（土壤—植被—冰雪）。E-mail：jiaozt@bnu.edu.cn
JIAO Ziti12*,  
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

DING Anxin12,  
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

DONG Yadong12,  
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    College of Water Sciences, Beijing Normal University, Beijing 100875, China

ZHANG Xiaoning123,  
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

CUI Lei12,  
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

YIN Siyang12,  
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

CHANG Yaxuan12,  
  • Affiliation：

    Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

XIE Rui12

Resümee

Snow and ice scatter solar radiation in a strong anisotropic fashion, especially in shortwave region, which, in turn, causes a significant difference in the study of the global energy balance and water cycles. The remote sensing community has developed a series of reflectance models for various applications in snow surface. Comprehensive comparison and evaluation of these models help in choosing an algorithm to produce satellite multi-angle remote sensing product. In this paper, we use the Polarization and Directionality of Earth Reflectances (POLDER) multi-angle snow data to compare and evaluate the performance of three models to characterize snow scattering. Three models including the kernel-driven linear Ross Thick-Li Sparse Reciprocal (RTLSR) model as the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo operational algorithm, the Asymptotic Radiative Theory model (ART), and the lately developed RTLSR-Snow (RTLSRS) model have been well used in studies. First, the POLDER data are divided into pure snow data and impure snow data by the using the homogeneity index provided by the POLDER database. We then use three BRDF models to fit (1) a single pure snow BRDF dataset; (2) the entire archive of the pure snow BRDF data; (3) a single impure snow BRDF dataset; and (4) the entire achieve of the impure snow BRDF data. We analyze the result on the basis of the R2, RMSE and bias. As the volumetric scattering kernel and geometric optical kernel contribute little to pure snow reflectances, we further simplify the RTLSRS model by keeping only isotropic scattering and snow scattering kernel in the kernel-driven model framework (i.e., isotropic and snow-kernel model, ISM). The performance of the ISM model has further been evaluated using the POLDER pure snow data. The results are as follows: (1) The RTLSRS is the most accurate model among all models being considered. For a single pure snow BRDF dataset, the RTLSRS model has an RMSE value that is 45.45% lower than that of ART models and is only 18.46% of that for RTLSR model.For a single impure snow BRDF dataset, the BRDF curve of RTLSRS model is generally similar with RTLSR model’s, but the RMSE is 67.5% lower than RTLSR. The RMSE of the ART model is the largest in this case, arriving at 0.136. (2) The accuracy of the RTLSRS model in simulating the pure snow data (R2=0.969, RMSE=0.012) is higher than that of the impure snow data (R2=0.926, RMSE=0.013). (3) The simplified ISM model can characterize the pure snow BRDF data well. The R2 and RMSE can reach 0.949 and 0.034 for the entire POLDER pure snow data, which is even better than the ART model. RTLSRS has the highest accuracy in fitting various POLDER BRDF snow data. Although the ISM has low accuracy relative to its original RTLSRS model, it shows higher accuracy than the ART model in fitting the POLDER pure snow data. Results present that the index of the “homogeneity” provided by the entire archive of the POLDER snow database cannot necessarily meet the requirement to identify the pure snow pixels of POLDER snow data. Therefore, a new method must be developed to refine the POLDER snow data and provide more details that can improve the understanding for potential users in relation to snow optical scattering.

Schlüsselwort

snow;ART;RTLSR;RTLSRS;POLDER;kernel-driven BRDF model

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