Abstract
In studies involving the extraction of surface physical parameters using optical remote sensing satellite imagery, sun-sensor geometry must be known, especially for sensor viewing angles. However, while pixel-by-pixel acquisitions of sensor viewing angles are of critical importance to many studies, currently available algorithms for calculating sensor-viewing angles focus only on the center-point pixel or are complicated and are not well known. Thus, this study aims to provide a simple and general method to estimate the sensor viewing angles pixel by pixel. The Rational Function Model (RFM) is already widely used in high-resolution satellite imagery, and, thus, a method is proposed for calculating the sensor viewing angles based on the space-vector information for the observed light implied in the RFM. This method can calculate independently the sensor-viewing angles in a pixel-by-pixel fashion, regardless of the specific form of the geometric model, even for geometrically corrected imageries. The experiments reveal that the calculated values differ by approximately 10−40 for the Gaofen-1 (GF-1) Wide-Field-View-1 (WFV-1) sensor, and by ~10−70 for the Ziyuan-3 (ZY3-02) panchromatic nadir (NAD) sensor when compared to the values that are calculated using the Rigorous Sensor Model (RSM), and the discrepancy is analyzed. Generally, the viewing angles for each pixel in imagery are calculated accurately with the proposed method.
Highlights
With the development of remote sensing theory and techniques, and the advances in remote sensing applications, the field of remote sensing has evolved from predominantly qualitative interpretations to a field that enables quantitative analysis
When considering that rational function model (RFM) are widely used in high-resolution satellite imagery and together with satellite imagery as basic products that are distributed to users, this study proposes a method for calculating the viewing angles that is based on the space-vector information of observed light implied by the Rational Function Model (RFM)
The second dataset is from ZY3-02’s NAD Level-1 imagery, the RFM file taken on 9 March 2017 in Fuzhou, Fujian Province, China
Summary
With the development of remote sensing theory and techniques, and the advances in remote sensing applications, the field of remote sensing has evolved from predominantly qualitative interpretations to a field that enables quantitative analysis. In terms of quantitative analysis, most surface parameters in quantitative remote-sensing models, such as the leaf-area index, are based on the surface albedo. The albedo depends on the Bidirectional Reflectance Distribution Function (BRDF), and directly characterizes the surface-energy balance, making it critical for monitoring of global climate change [1,2]. The albedo of most real-world surfaces is anisotropic, and, the viewing angles can have significant effects on the measured albedo [4]. It is increasingly important to measure viewing angles precisely, given the development of quantitative remote sensing techniques
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