Abstract
Land surface albedo (LSA) is one of the driving factors in the energy balance of surface radiation and the interaction between the earth and atmosphere. LSA is an important parameter that is widely used in surface energy balance, medium- and long-term weather forecasting, and global change studies. GF-1 wide field view (WFV) data provide a spatial resolution of 16 m and temporally intensive land surface observations, but efficient algorithm was still lacking for quantitatively land surface parameters estimation. It is essential to improve the data use ability by generating efficient land surface parameter retrieval algorithms. This study proposed an LSA retrieval algorithm by using GF-1 WFV data. Land surface bidirectional reflectance distribution function characteristic parameters were used to represent the non-Lambertian characteristic of land surface. The top of atmosphere (TOA) reflectance is simulated by the 6S radiative transfer model by considering non-Lambertian land surfaces. Linear regression is applied in the TOA reflectance, and LSA is simulated with the surface bidirectional reflectance characteristic parameters to build a lookup table. The proposed algorithm can estimate LSA with high accuracy according to the TOA reflectance without the complex multistep inversion process. The validation results of ground measurements in Northwest China for different land cover types show that the algorithm is effective, and the overall root mean square error was 0.036 when compared with field observation. The algorithm also shows great consistency with Landsat albedo data. The proposed algorithm is of great significance for improving GF data utilization.
Highlights
Land surface albedo (LSA), which refers to the ratio of reflected solar radiation to incident solar radiation, is an important parameter that characterizes the land surface energy balance [1]
Many operational remote sensing albedo products are routinely derived from various sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) [9], Polarization and Directionality of the Earth’s Reflectance (POLDER) [10,11,12,13], Medium Resolution Imaging Spectrometer (MERIS) [14], Clouds and the Earth’s Radiant Energy System (CERES) [12, 13], Meteosat Second Generation (MSG) [15, 16], Meteosat [17, 18], Visible Infrared Imaging Radiometer Suite (VIIRS) [19, 20], and Airborne Visible Infrared Imaging Spectrometer (AVIRIS) [21]
To improve the efficiency and ability of data use, this paper presents a method to estimate land surface albedo by using high spatial and temporal resolution GF-1 wide field view (WFV) data
Summary
Land surface albedo (LSA), which refers to the ratio of reflected solar radiation to incident solar radiation, is an important parameter that characterizes the land surface energy balance [1]. Many operational remote sensing albedo products are routinely derived from various sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) [9], Polarization and Directionality of the Earth’s Reflectance (POLDER) [10,11,12,13], Medium Resolution Imaging Spectrometer (MERIS) [14], Clouds and the Earth’s Radiant Energy System (CERES) [12, 13], Meteosat Second Generation (MSG) [15, 16], Meteosat [17, 18], Visible Infrared Imaging Radiometer Suite (VIIRS) [19, 20], and Airborne Visible Infrared Imaging Spectrometer (AVIRIS) [21] Their spatial resolutions are mainly of kilometer magnitude, and their temporal resolutions range from daily to monthly. For regional use, for example, when assessing environmental changes induced by urbanization [22] and land cover change, kilometer-scale resolution LSA products [10, 13, 23] have difficulty capturing patch-size changes that are induced by human activities
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