Cross-Comparison of Global Surface Albedo Operational Products-MODIS, GLASS, and CGLS

Abstract

Surface albedo, as an important parameter for land surface geo-biophysical and geo-biochemical processes, has been widely used in the research communities involved in surface energy balance, weather forecasting, atmospheric circulation, and land surface process models. In recent years, operational products using satellite-based surface albedo have, from time to time, been rapidly developed, contributing significantly to the estimation of energy balance at regional or global scales. The increasing number of research topics on dynamic monitoring at a decades-long scale requires a combination of albedo products generated from various sensors or programs, while the quantitative assessment of agreement or divergence among different surface albedo products still needs further understanding. In this paper, we investigated the consistency of three classical operational surface albedo products that have been frequently used by researchers globally via the official issued datasets-MODIS, GLASS (Global LAnd Surface Satellite), and CGLS (Copernicus Global Land Service). The cross-comparison was performed on all the identical dates available during 2000–2017 to represent four season-phases. We investigated the pixel-based validity of each product, consistency of global annual mean, spatial distribution and different temporal dynamics among the discussed products in white-sky (WSA) and black-sky (BSA) albedo at visible (VIS), near-infrared (NIR), and shortwave (SW) regimes. Further, varying features along with the change of seasons was also examined. In addition, the variation in accuracy of shortwave albedo magnitude was explored using ground measurements collected by the Baseline Surface Radiation Network (BSRN) and the Surface Radiation Budget Network (SUFRAD). Results show that: (1) All three products can provide valid long-term albedo for dominant land surface, while GLASS can provide additional estimation over sea surfaces, with the highest percentage of valid land surface pixels, at up to 93% in 24 October. The invalid pixels mainly existed in the 50°N–60°N latitude belt in December for GLASS, Central Africa in April and August for MODIS, and northern high latitudes for CGLS. (2) The global mean albedo of CGLS at the investigated bands has significantly higher values than those of MODIS and GLASS, with a relative difference of ~20% among the three products. The global mean albedo of MODIS and GLASS show a generally increasing trend from April to December, with an abrupt rise at NIR and SW of CGLS in June of 2014. Compared with SW and VIS bands, the linear temporal trend of the NIR global albedo mean in three products continues to increase, but the slope of CGLS is 10–100 times greater than that of the other two products. (3) The differences in albedo, which are higher in April, October, and December than in August, exhibit a small variation over the main global land surface regions, except for Central Eurasia, North Africa, and middle North America. The magnitude of global absolute difference among the three products usually varies within 0.02–0.06, but with the largest value occasionally exceeding 0.1. The relative difference is mainly within 10–20%, and can deviate more than 40% away from the baseline. In addition, CGLS has a greater opportunity to achieve the largest difference compared with MODIS and GLASS. (4) The comparison with ground measurements indicates that MODIS generally performs better than GLASS and CGLS at the sites discussed. This study demonstrates that apparent differences exist among the three investigated albedo products due to the ingested source data, algorithm, atmosphere correction etc., and also points at caution regarding data fusion when multiple albedo products were organized to serve the following applications.

Arctic Sea Ice Albedo Estimation from Fengyun-3C/Visible and Infra-Red Radiometer

“…To realize a much faster calculation, a direct estimation method based on MODIS is developed [16], which is first applied for the land albedo with high resolution and relatively maturation [17,18]. The algorithm considers a pixel as a mixture of sea ice, ocean, and snow, building a linear empirical lookup table between the top of atmosphere reflectance and surface broadband albedo based on a bidirectional reflectance distribution function (BRDF) database.…”