Cross-Comparison between GF-2 PMS2 and ZY-3 MUX Sensor Data
WU Xiao-ping1, 2, XU Han-qiu1, 2*
1. College of Environment and Resources, Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China
2. Institute of Remote Sensing Information Engineering, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou 350116, China
Abstract：In recent years, China has launched a variety of Earth observation satellites with many newly-developed sensors onboard. Meanwhile, researches on the cross-comparison of these China-made new sensors are in progress. Nevertheless, no study has been published with respect to the comparison between Gaofen-2 (GF-2) PMS2 and Ziyuan-3 (ZY-3) MUX sensor data up to now. The quantitative relationship between the two sensor data is unclear, and it is uncertain whether the two sensor data can be used for the same project directly. To meet this special requirement, this study carried out a cross-comparison between the GF-2 PMS2 and ZY-3 MUX sensor data based on three synchronous image pairs of the two sensors. The cross-comparison was performed using two methods. The first one is making use of image statistics based on large areas in common between the image pairs. A pixel-by-pixel comparison method was used to investigate quantitative relationship between GF-2 PMS2 and ZY-3 MUX sensor data based on the whole test area. The other method is a comparison based on the region of interest (ROI) in common to avoid the problem due to the difference in spatial resolution between the GF-2 PMS2 (4 m) and ZY-3 MUX (6 m). The ROIs had appropriate size and were selected from homogeneous areas that excluded complicated terrain conditions. A linear regression model was adopted for the Top of Atmosphere (TOA) reflectance-based comparison between the ROIs of the GF-2 PMS2 and ZY-3 MUX images. Through the two methods, we obtained the quantitative relationship models between GF-2 PMS2 and ZY-3 MUX sensor data. This comparison study found that the results obtained by two methods, i. e., pixel-by-pixel comparison and ROI-based comparison, are almost consistent. However, the ROI-based comparison achieves a higher accuracy because the spectral information of the corresponding pixels may be offset when using the pixel-by-pixel comparison due largely to the mis-registration of image pixels. This will lower the accuracy of the pixel-by-pixel comparison method. The results showed that the TOA reflectance of GF-2 PMS2 and ZY-3 MUX sensors has a high degree of agreement, with R2 values greater than 0.9 for all the four bands. However, the higher R2 values in blue and green bands indicated that the TOA reflectance between the two sensors in both bands has a better agreement than that of red and near-infrared bands. Scatter plots showed that almost all data points lie under the one-to-one line in the spectral feature space with GF-2 data in x-axis and ZY-3 data in y-axis. This suggested that the GF-2 PMS2 sensor data generally have higher TOA reflectance than ZY-3 MUX, especially in blue and green bands. It should be noted that the difference of TOA reflectance between the two sensor data can be affected by land cover types in red and near infrared bands. In the image pairs dominated by bare soil, the difference between the TOA reflectance of two sensors decreases with increasing wavelength, while for vegetation-dominated image pairs, the difference increases with increasing wavelength. In order to further examine the differences caused by the land cover types, more ROIs of pure vegetation and pure bare soil were extracted separately. The results showed that the signal difference between the two sensors is mainly affected by bare soil in the red band and by vegetation in the near infrared band. The more vigorous the vegetation grows, the greater the difference between the two sensors is. The band-by-band comparison has yielded the conversion equations for each corresponding bands of the two sensors, which were applied to convert the TOA reflectance between each corresponding bands of the two sensors. The validation of the conversion showed that the obtained conversion equations have high accuracy. It can be observed that the GF-2 PMS2-simulated ZY-3 MUX data are almost identical with the actual ZY-3 MUX data with R2 values close to 1 and RMSE less than 0.01. The conversion has resulted in a significance reduction in RMSE by up to 64.79%, as well as a significant decrease in ME. This study showed that such a conversion can significantly improve the agreement between the two sensors data. The converted data are more conducive to the synergy between the GF-2 PMS2 and ZY-3 MUX sensor data. The analysis showed that the differences in TOA reflectance between the two sensor data result probably from the differences in their spectral response function and spatial resolution. We found that the spectral response curve of ZY-3 MUX is smoother with no obvious fluctuations than that of GF-2 PMS2, which is fluctuant in all of four bands. Such a difference in the spectral response functions may have led to the difference in TOA reflectance between the two sensors. In addition, the spatial resolution of GF-2 PMS2 is 4 m, which is higher than ZY-3 MUX’s 6 m. A higher spatial resolution will help GF-2 PMS2 sensor to detect subtle spectral information of small ground objects and thus cause the difference in TOA reflectance between the two sensors.
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