Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images

Abstract

Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles.

Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies

“…The China Center for Resources Satellite Data and Application (CCRSDA) provided the Level-1A GF-4 PMS data without atmospheric correction. The original GF-4 PMS images were transformed from digital number to top-of-atmosphere (TOA) radiance by using the radiometric calibration coefficients published on the official website of the CCRSDA [30]. Then, the TOA radiance images were atmospherically corrected to surface reflectance with a look-up table built by the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model.…”

“…It was necessary to conduct spectral matching because of the differences between sensor parameters, such as the spectral responses of GF-4 PMS and Landsat-8 OLI in the corresponding bands. Table 1 and Figure 2 show the sensor parameters and spectral response functions of GF-4 PMS and Landsat-8 OLI [30,35,36]. The wavelength ranges of PMS are slightly wider than those of OLI in the visible bands.…”

On-Orbit Radiometric Calibration for a Space-Borne Multi-Camera Mosaic Imaging Sensor

“…The information of the collocated imagery is listed in Table 1. Figure 4 shows that the view angles for DRCS in the WFV and MODIS images are quite different, so that the bidirectional reflectance distribution function (BRDF) characteristic of DRCS needs to be taken into account [14,17]. …”

“…This site is officially used to obtain on-orbit absolute radiometric calibration coefficients for the visible and near-infrared sensor of Chinese land observation satellites by using the vicarious calibration method, such as the CBERS (China and Brazil Earth Resource Satellite), HJ (HuanJing), FY (FengYun), ZY (ZiYuan), GF-1, GF-2, GF-4, TH (TianHui), etc. [14][15][16][17] …”

“…(b) The image is uncontaminated by cloud clusters, which will damage the spectral and spatial characteristic of the DRCS in image [18]. (c) The WFV image should cover the center region of the DRCS where there is more spectral and spatial homogeneity [13,17].…”