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.
Highlights
China began implementation of a high-definition earth observation system (HDREOS) in 2010 as a step towards an all-weather, all-day earth observation network with global coverage
The calibration from Landsat-8 Operational Land Imager (OLI) data are regarded as ground truths
The calibration coefficients derived through the spectrum matching (SM) and radiative transfer model (RTM)-bidirectional reflectance distribution function (BRDF) methods participate in the comparison, coefficients derived through the SM and RTM-BRDF methods participate in the comparison, along along with the officially provided coefficients
Summary
China began implementation of a high-definition earth observation system (HDREOS) in 2010 as a step towards an all-weather, all-day earth observation network with global coverage. As an important part of the system, the GF-4, launched on 29 December 2015, is the first Chinese optical remote sensing satellite in geostationary orbit designed for civil use. A panchromatic multispectral sensor (PMS) sensor is onboard the GF-4 satellite, providing images for about one third of the earth at a high spatial resolution (50 m) and wide coverage (500 km). It can adjust to the observation area within a few minutes, and achieves up-to-the-minute high-frequency continuous imaging of the same area. In the first operating year, the GF-4 PMS played an important role in many applications including monitoring a forest fire in Diebu city Gansu province, China and the Nepartak typhoon (http://www.cresda.com/CN/xwzx/xwdt/index.shtml).
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