Functional relation of land surface albedo with climatological variables: a review on remote sensing techniques and recent research developments

Abstract

Surface albedo has been documented as one of the Essential Climate Variables (ECV) of the Global Climate Observing System (GCOS) that governs the Earth's Radiation Budget. The availability of surface albedo data is necessary for a comprehensive environmental modelling study. Thus, both temporal and spatial scale issues need to be rectified. This study reports about the availability of surface albedo data through in-situ and remote sensing satellite observations. In this paper, we reviewed the existing models for surface albedo derivation and various initiatives taken by related environmental agencies in order to understand the issues of climate with respect to surface albedo. This investigation evaluated the major activities on albedo-related research specifically for the retrieval methods used to derive the albedo values. Two main existing albedo measurement methods are derived through in-situ measurement and remotely sensed observations. In-situ measurement supported with number of instruments and techniques such aspyrheliometers, pyranometers and Baseline Surface Radiation Network (BSRN) and remotely sensed observations using angularly integrated Bidirectional Reflectance Distribution Function (BRDF) by both geostationary and polar orbit satellites. The investigation results reveals that the temporal and spatial scaling is the major issues when the albedo values are needed for microclimatic study, i.e. high-resolution timeseries analyses and at heterogeneity and impervious surface. Thus, an improved technique of albedo retrieval at better spatial and temporal scale is required to fulfil the need for such kind of studies. Amongst many others, there are two downscaling methods that have been identified to be used in resolving the spatial scaling biased issues: Smoothing Filter-based Intensity Modulation (SFIM) and Pixel Block Intensity Modulation (PBIM). The temporal issues can be resolved using the multiple regression techniques of land surface temperature, selected air quality parameters, aerosol and daily skylight.

Greenery as a mitigation strategy to urban heat and air pollution: a comparative simulation-based study in a densely built environment

“…Different combinations of H/W ratios, albedo coefficients, and orientations have been tested, providing information on the incoming solar radiation and surface temperatures, useful to determine the most critical canyon configuration to try the MSs later. The relevant radiative properties of surface materials have been selected from the approximate values and ranges presented in BS 8206-2 [27] and by Salleh et al (2014) [28]. The simulation settings for this preliminary study have been summarized in Table 1.…”

Conceptualizing Spatial Heterogeneity of Urban Composition Impacts onPrecipitation Within Tropics

“…Changes in surface characteristics cause accelerated climate warming and amplified heat waves because of this UHI phenomenon which later affect the precipitation intensity and frequency of pattern in different ways (Carter, Marshall Shepherd, Burian, & Jeyachandran, 2012;Hand & Shepherd, 2009;Jin, Kessomkiat, & Pereira, 2011;Marshall Shepherd, Carter, Manyin, Messen, & Burian, 2010;Mitra, Shepherd, & Jordan, 2012;Niyogi et al, 2011;Paul et al, 2018;Shepherd, 2013;Zhong et al, 2008) . The phenomenon of urban heat islands (UHI) is a distinct climate condition in Malaysia because it influences air quality and societal comfort (Salleh, Latif, Pradhan, Wan Mohd, & Chan, 2014). Different surfaces in urban areas act as a heat source and ele-vated temperature producing urban heat islands that exacerbate precipitation com-pared to non-urban regions.…”

Land use land cover analysis with pixel-based classification approach

“…SVM algorithm is a non-parametric that determines the boundaries in feature space by divide and differentiates the classes [18]. SVM has been proven a good classifier for complex man made infrastructures that have similar spectral signatures [19][20][21]. This classification algorithm able to provide higher accuracy in comparison to Maximum Likelihood classification for certain application [22][23][24][25].…”