A Practical Approach to Improve the MODIS MCD43A Products in Snow-Covered Areas

Abstract

The MODerate Resolution Imaging Spectroradiometer (MODIS) MCD43A products have been extensively applied in the remote sensing field, but recent researchers have demonstrated that these products still had the potential to be further improved by using the latest development of the kernel-driven model [RossThick-LiSparseReciprocal-Snow (RTLSRS)] in snow-covered areas, since the MCD43A product algorithm [RossThick-LiSparseReciprocal (RTLSR)] needed to be improved for the accurate simulation of snow bidirectional reflectance distribution function (BRDF) signatures. In this paper, we proposed a practical approach to improve the MCD43A products, which used the Polarization and Directionality of the Earth's Reflectance (POLDER) observations and random forest algorithm to establish the relationship between the BRDF parameters (MCD43A1) estimated by the RTLSR and RTLSRS models. We applied this relationship to correct the MCD43A1 product and retrieve the corresponding albedo (MCD43A3) and nadir reflectance (MCD43A4). The results obtained highlight several aspects: (a) The proposed approach can perform well in correcting BRDF parameters [root mean square error (RMSE) = ~0.04]. (b) The corrected BRDF parameters were then used to retrieve snow albedo, which matched up quite well with the results of the RTLSRS model. (c) Finally, the snow albedo retrieved by the proposed approach was assessed using ground-based albedo observations. Results indicated that the retrieved snow albedo showed a higher accuracy as compared to the station measurements (RMSE = 0.055, bias = 0.005), which was better than the results of the MODIS albedo product (RMSE = 0.064, bias = −0.018), especially at large angles. These results demonstrated that this proposed approach presented the potential to further improve the MCD43A products in snow-covered areas.

Evaluating the Performance of the Enhanced Ross-Li Models in Characterizing BRDF/Albedo/NBAR Characteristics for Various Land Cover Types in the POLDER Database

“…However, the consistency of NBAR results between these two models is relatively poor for SI. In general, the RTLSRC model tends to be overestimated compared with the results of the NBAR retrieved by the RTLSRCS model, which is consistent with our previous research findings [41]. To better compare the differences in the inversion of NBAR results across various land cover types between these two models, we further conducted statistical analysis and T-test on both other 15 land cover types (i.e., LC15) and SI.…”

“…Furthermore, analyzing the differences in the retrieval of BRDF parameters by these two models is crucial for establishing the relationship between their kernel parameters and improving the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43 products. These results also demonstrate the validity and reliability of our previous approach for calibrating MODIS BRDF parameter products (i.e., MCD43A1) [41].…”

“…The BRDF modeling of land surfaces plays a pivotal role in understanding the spatial variability of surface the PROSAIL model [38], correct the hotspot effect by geometric conditions (RossThick-LiSparseReciprocalChen, RTLSRC) [39], and characterize complex mountainous BRDF features and albedo inversion [40]. Nevertheless, these enhanced Ross-Li BRDF models primarily focused on simulating the anisotropic reflection of soil-vegetation systems with an emphasis on strong backscattering effects [34,41]. To enhance the portrayal of the anisotropic features of snow, Jiao et al introduced a snow kernel within the RTLSR model framework, enhancing the representation of snow reflectance properties, termed the RTL-SRS model [34].…”

“…To enhance the portrayal of the anisotropic features of snow, Jiao et al introduced a snow kernel within the RTLSR model framework, enhancing the representation of snow reflectance properties, termed the RTL-SRS model [34]. Ding et al conducted a comprehensive assessment of the capability of the snow kernel to represent snow reflective characteristics [5,6,41]. Their findings indicated that the RTLSRS model exhibits strong performance in describing snow BRDF features and retrieving albedo.…”

“…The latest versions of the Ross-Li model include kernels that incorporate isotropic reflection of the land surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically capable of effectively describing the BRDF/Albedo/Nadir BRDF Adjusted Reflectance (NBAR) characteristics of various surface types [41]. However, there remains a paucity of systematic and comprehensive evaluation of the enhanced Ross-Li models across diverse land cover types.…”