Abstract
For satellite remote sensing, radiances received at the sensor are not only affected by the atmosphere but also by the topographic properties of the terrain surface. As a result, atmospheric correction alone does not yield output images that truly reflect terrain surface properties, namely surface reflectance (bidirectional reflectance factor, BRF) of objects on the earth surface. Following the concept of the radiometric control area (RCA)-based path radiance estimation method, we herein propose a statistical approach for surface reflectance estimation utilizing DEM data and surface reflectance of selected radiometric control areas. An algorithm for identification of shaded samples and a shape factor model were also developed in this study. The proposed RCA-based surface reflectance estimation method is capable of achieving good reflectance estimates in a region where elevation varies from 0 to approximately 600 m above the mean sea level. However, further study is recommended in order to extend the application of the proposed method to areas with substantial terrain variation.
Highlights
Remote sensing images have been widely used for applications of earth surface monitoring such as landslide sites identification, land use/land cover (LULC) classification and change detection, crop yieldRemote Sensing 2012, 4 estimation, reservoir and coastal water quality monitoring, etc
Following the concept of radiometric control area (RCA)-based path radiance estimation method, we propose in this study a statistical approach for surface reflectance estimation utilizing digital elevation model (DEM) data
In previous sections we have demonstrated that values of digital numbers (DNs) pλ, k1λ, k2λ μ1 and k2λ K λ can be considered as constants for all ground samples for applications that do not cover extensively wide areas
Summary
Remote Sensing 2012, 4 estimation, reservoir and coastal water quality monitoring, etc Radiometric corrections such as dark object subtraction (DOS) are often conducted prior to LULC classification and change detection [1,2,3,4]. We need to use features or properties that are reflective of earth surface conditions and free of the topographic and atmospheric effects for remote sensing applications of earth surface monitoring. One such essential feature in the optical spectral range is the BRF which is considered to be the inherent property of any earth surface object
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