研究目的
To address the challenges posed by the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF).
研究成果
The proposed cross-calibration method based on data assimilation effectively addresses the challenges posed by large view angles of the GF-4 PMS sensor. The method simplifies the calculation of BRDF by introducing an adjustment factor and employs the SCE-UA algorithm for optimization. Validation results indicate a surface reflectance error of <5% and an uncertainty of less than 7%, demonstrating the method's accuracy and reliability for future quantitative applications and research. The method is also applicable to other sensors with large view angles.
研究不足
The study acknowledges the lack of in situ measurements for validation, relying instead on Landsat-8 images. The uncertainty of the SBAF caused by the lack of ground measured spectrum and the uncertainty caused by atmospheric parameters are identified as potential sources of error.
1：Experimental Design and Method Selection:
The study 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. An adjustment factor for the BRDF was established to correct unequal bidirectional reflection effects. 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.
2：Sample Selection and Data Sources:
The Dunhuang calibration site was used as the test site, with additional samples collected from highly reflective gypsum and lowly reflective water targets. Two sets of data obtained on different dates were employed in the experiment, consisting of GF-4 PMS and Landsat-8 OLI image pairs with minimal cloud coverage acquired on the same day.
3：List of Experimental Equipment and Materials:
The study utilized the GF-4 PMS and Landsat-8 OLI sensors, MODIS aerosol products for atmospheric parameters, and the USGS spectral library for spectral band adjustment factors (SBAF).
4：Experimental Procedures and Operational Workflow:
The cross-calibration process involved geometric registration of images, calculation of TOA reflectance, atmospheric correction, spectral matching, and iterative optimization of calibration coefficients and BRDF adjustment factors using the SCE-UA algorithm.
5：Data Analysis Methods:
The validation results were analyzed by comparing the calibrated radiance and reflectance retrieved by the proposed method with ground truths simulated by Landsat-8 OLI images.
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