Surface albedo plays a controlling role in the surface energy budget, and albedo-induced radiative forcing has a significant impact on climate and environmental change (e.g., global warming, snow and ice melt, soil and vegetation degradation, and urban heat islands (UHIs)). Several existing review papers have summarized the algorithms and products of surface albedo as well as climate feedback at certain surfaces, while an overall understanding of various land types remains insufficient, especially with increasing studies on albedo management methods regarding mitigating global warming in recent years. In this paper, we present a comprehensive literature review on the variance pattern of surface albedo, the subsequent climate impact, and albedo management strategies. The results show that using the more specific term “surface albedo” is recommended instead of “albedo” to avoid confusion with similar terms (e.g., planetary albedo), and spatiotemporal changes in surface albedo can indicate subtle changes in the energy budget, land cover, and even the specific surface structure. In addition, the close relationships between surface albedo change and climate feedback emphasize the important role of albedo in climate simulation and forecasting, and many albedo management strategies (e.g., the use of retroreflective materials (RRMs)) have been demonstrated to be effective for climate mitigation by offsetting CO2 emissions. In future work, climate effects and management strategies regarding surface albedo at a multitude of spatiotemporal resolutions need to be systematically evaluated to promote its application in climate mitigation, where a life cycle assessment (LCA) method considering both climate benefits and side effects (e.g., thermal comfort) should be followed.
Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation.
Recently, much attention has been given to using geostationary Earth orbit (GEO) meteorological satellite data for retrieving land surface parameters due to their high observation frequencies. However, their bidirectional reflectance distribution function (BRDF) information content with a single viewing angle has not been sufficiently investigated, which lays a foundation for subsequent quantitative estimation. In this study, we aim to comprehensively evaluate BRDF information from time-series observations from the Advanced Himawari Imager (AHI) onboard the GEO satellite Himawari-8. First, ~6.2 km monthly multiangle surface reflectances from POLDER onboard a low-Earth-orbiting (LEO) satellite with good angle distributions over various land types during 2008 were used as reference data, and corresponding 0.05° high-quality MODIS (i.e., onboard LEO satellites) and AHI datasets during four months in 2020 were obtained using cloud and aerosol property products. Then, indicators of angle distribution, BRDF change, and albedos were retrieved by the kernel-driven Ross-Li BRDF model from the three datasets, which were used for comparisons over different time spans. Generally, the quality of sun-viewing geometries varies dramatically for accumulated AHI observations according to the weight-of-determination, and wide-ranging anisotropic flat indices are obtained. The root-mean-square-errors of white sky albedos between AHI and MODIS half-month data are 0.018 and 0.033 in the red and near-infrared bands, respectively, achieving smaller values of 0.004 and 0.007 between the half-month and daily AHI data, respectively, due to small variances in sun-viewing geometries. The generally wide AHI BRDF variances and good consistency in albedo with MODIS show their potential for retrieving anisotropy information and albedo, while angle accumulation quality of AHI time-series observations must be considered.
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