Development of land surface albedo parameterization based on Moderate Resolution Imaging Spectroradiometer (MODIS) data

Abstract

[1] A new dynamic-statistical parameterization of snow-free land surface albedo is developed using the Moderate Resolution Imaging Spectroradiometer (MODIS) products of broadband black-sky and white-sky reflectance and vegetation and the North American and Global Land Data Assimilation System (LDAS) outputs of soil moisture during [2000][2001][2002][2003]. The dynamic component represents the predictable albedo dependences on solar zenith angle, surface soil moisture, fractional vegetation cover, leaf plus stem area index, and greenness, while the statistical part represents the correction for static effects that are specific to local surface characteristics. All parameters of the dynamic and statistical components are determined by solving nonlinear constrained optimization problems of a physically based conceptual model for the minimization of the bulk variances between simulations and observations. They all depend on direct beam or diffuse radiation and visible or near-infrared band. The dynamic parameters are also functions of land cover category, while the statistical factors are specific to geographic location. The new parameterization realistically represents surface albedo variations, including the mean, shape, and distribution, around each dependent parameter. For composites of all temporal and spatial samples of the same land cover category over North America, correlation coefficients between the dynamic component of the new parameterization and the MODIS data range from 0.39 to 0.88, while relative errors vary within 8-42%. The gross (i.e., integrated over all categories) correlations and errors are 0.57-0.71 and 17-26%, changing with direct beam or diffuse radiation and visible or near-infrared band. The static local correction results in a further reduction in relative errors, producing gross values of 11-21%. The new parameterization is a marked improvement over the existing albedo scheme of the state-of-the-art Common Land Model (CLM), which has correlation coefficients from À0.57 to 0.71 and relative errors of 18-140% for individual land cover categories, and gross values of 0.03-0.32 and 37-71%, respectively.

Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

“…However, their simulations were on the site scale and over a very short time frame (less than 2 months) and may not be easily applicable to regional or global simulations over longer time frames. Liang et al (2005) developed a "dynamic-statistical" parameterisation of snow-free albedo using MODIS albedo and soil moisture from a land data assimilation system over North America. While the dynamical part of the model represents the physical dependencies of surface albedo on solar zenith angle and surface soil moisture etc., the statistical model provides parameter estimates specific to geographic location.…”

“…In the morning just after sunrise and late afternoon before sunset, albedo is generally higher as compared to midday when the sun is directly overhead. The inclusion of soil and vegetation albedo dependence on solar zenith angle during clear-sky conditions has improved albedo simulations in some LSMs (Liang et al, 2005).…”

“…Wei et al, 2001;Oleson et al, 2003;Zhou et al, 2003;Wang et al, 2004) and the development of vegetation and soil albedo parameterisations (e.g. Liang et al, 2005;Yang et al, 2008). These remotely sensed products have also allowed the mapping of land surface parameters, such as the spatial and temporal distribution of PFTs, LAI, and soil colour, for use in LSMs (Lawrence and Chase, 2007).…”

Human-Induced Landcover Changes Drive a Diminution of Land Surface Albedo in the Loess Plateau (China)

“…However, these studies used the static LULC and ignored the time dynamic information on land cover change in inter-annual scales [24], such as urban sprawl, which can directly affect the land surface characteristics [25][26][27]. These knowledge gaps in the most important drivers of albedo change prevent the accurate estimation of albedo change at the regional scale.…”

Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe

“…It directly determines the amount of solar radiation absorbed by the land surface, and is widely used in the study of surface energy budget. Surface albedo can be obtained through land surface models [1], remote sensing retrieval [2][3][4][5], or land data assimilation [6], with different levels of accuracy and reliability. However, all these estimates are indirect measurements of albedo that must be validated through accurate ground measurements.…”