# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 1 Introduction
This effect can also be applied to other situations where the objective is heat reduction, as in train railways, where the reduction in heat absorption leads to the reduction of railway deformation [25]. However, at a global scale, high albedo values can have an influence on global warming and climate change. An example is the case of desert areas, where the increase in reflected radiation induces an increase in latent and sensible heat fluxes and temperature and a consequent decrease in air density [26]. The multidisciplinary studies mentioned are applied in large areas, in such a way that the use of a methodology with extensive results is essential. Results at big scales can be obtained with remote sensing and satellite imagery instead of using traditional albedometers. There is a high number of space missions, satellites and Earth observation instruments that provide digital images with high quality and open access, able to provide information about the solar energy reflected by a surface, and consequently enabling the computation of albedo. Both polar [27] and geostationary [28] satellites provide products with albedo values, all with the same limitation: low spatial resolution, lower than 250 m. Other acquisition methods can provide an improvement in the spatial resolution, such as the use of Unmanned Aerial Vehicles (UAVs) [29], but these present the drawback of implying high economic cost due to the need for a specific inspection for data acquisition, in addition to the limited extension that can be covered by these platforms compared to satellites. Furthermore, the estimation of albedo from UAVs requires an accurate estimation of the Bidirectional Reflectance Distribution Function (BRDF), due to the large variation in observation angles in images acquired from UAVs. Regarding satellite imagery that can be used for the computation of surface albedo, some of the methodologies are based on multi-angle and multi-temporal observations to take into account surface anisotropy, such as the MODIS BRDF/Albedo product [30]. The problem with this type of imagery is the poor spatial resolution, so over the years, new methodologies have been developed to obtain surface albedo data with better resolution, such as those obtained from Landsat 8 images. However, surface albedo cannot be retrieveddirectly from Landsat data due to the narrow field of view of the sensor, which does not allow observing the intrinsic reflectance anisotropy of most land surfaces [31]. As a solution, different methodologies have been developed to generate surface albedo at Landsat 8 resolution (30 m) and using anisotropy information from the coarser resolution (500 m) BRDF MODIS product. There are many albedo models based on Landsat data [32; 33; 34; 35; 36; 37; 38; 39], but none of them have been validated for their application to heterogeneous environments with vegetation and urban areas. Taking these aspects into account, this work aims to perform a deep study of the different methodologies developed within the scientific community for the estimation of surface albedo from Landsat 8 satellite imagery (30 m spatial resolution). As a result, a new methodology has been developed for estimating surface albedo, taking into account the key aspects of all of them. In addition, this methodology can also be applied to Landsat 9 satellite imagery, provided the characteristics of the imagery sensor in this platform. The objective of the proposed methodology is to obtain a direct albedo product, increasing the spatial resolution with respect to existing products, by applying optimal calculation methodologies in heterogeneous areas. To compare the results of the new method, direct measurements of albedo from SURFRAD (SURFace RADiation budget monitoring) stations and indirect measurements of albedo from the ABOVE (Arctic-Boreal Vulnerability Experiment) space mission are used.