This paper investigates the positioning accuracy of image pairs achieved by integrating images from multiple satellites. High-resolution satellite images from IKONOS, QuickBird, and KOMPSAT-2 for Daejeon, Korea were combined to produce pairs of stereo images. From single-satellite stereo pairs to multiple-satellite image pairs, all available combinations were analyzed via a rational function model (RFM). The positioning accuracy of multiple-satellite pairs was compared to a typical single-satellite stereo pair. The results show that dual-satellite integration can be an effective alternative to single-satellite stereo imagery for horizontal position mapping, but is less accurate for vertical mapping. The integration of additional higher-resolution images can improve the overall accuracy of the existing two images, but, conversely, may result in lower accuracy when very weak convergence or bisector elevation (BIE) angles occur. This highlights that the use of higher resolution images may not ensure improved accuracy, as it can result in very weak geometry. The findings confirm that multiple-satellite images can replace or enhance typical stereo pairs, but also suggest the need for careful verification, including consideration of various geometric elements and image resolution. This paper reveals the potential, limitations, and important considerations for mapping applications using images from multiple satellites.

Kyunghwa Lee, Dong-Won Lee, Lim-Seok Chang, Jeong-Ah Yu, Won-Jin Lee, Kyoung-Hee Kang, Jaehoon Jeong. Korean J. Remote Sens. -0001;0:. https://doi.org/10.7780/kjrs.2024.40.5.2.5

True-color imagery is essential for an intuitive comprehension of atmospheric data. However, the Geostationary Environment Monitoring Spectrometer (GEMS) of the Geostationary Korea Multi-Purpose Satellite (GK)-2B lacks green and red bands, which limits its ability to monitor atmospheric environments. To mitigate this issue, we suggest an innovative method of generating virtual GEMS green and red bands using conditional generative adversarial networks with data observed in the blue-green-red (RGB) bands of the Advanced Meteorological Imager (AMI) sensor, a payload of the GK-2A satellite. The paired datasets of the AMI blue band and the AMI RGB bands were used to train and test the data-to-data (D2D) translation model. Using the GEMS blue band as input data, the D2D model generated GEMS hypothetical radiance data at the green and red bands. Our results show that the D2D model generated hypothetical GEMS green and red bands with outstanding performance. The averaged values of the correlation coefficient, root-mean-square error, and bias between the observed and D2D-generated GEMS blue band were 0.999, 3.450 W/cm²/cm/sr, and -1.858 W/cm²/cm/sr, respectively. This research is expected to significantly contribute to the monitoring and comprehension of atmospheric environments in Asia and potentially improve the GEMS's global ability to monitor air quality. Additionally, the proposed method has the potential to enhance the capabilities of other satellites with limited spectral bands.

Keywords: Dual-satellite Stereo, Geometric Weakness, High-resolution Satellite Image, Imaging Geometry, Positioning Accuracy Many previous studies have demonstrated the potential applications of stereo pairs obtained from high-resolution satellites for accurate geo-positioning, but it should be noted that they are based on the conventional use of single-satellite stereo images produced by integrating two images taken by the same satellite. Considering the emergence of various types of satellite data and the requirements to combine these data, it is important to investigate dual-satellite stereo images that integrate two images taken by different satellites. This study reviews the image geometry and positioning accuracy of dual-satellite stereo images based on several important studies, including comparisons with conventional single-satellite stereo images. In particular, the geometric limitation of dual-satellite stereo images often leads to very weak geometry, which degrades the positioning accuracy according to theoretical and experimental studies. Optimal ray intersection can improve the positioning accuracy using dual-satellite stereo images. This review improves our understanding of satellite stereo geometry and the handling of the dual-satellite stereo images, which are expected to become more common in real mapping applications.

This study proposes an infrared-to-rain (IR2Rain) model to enhance the accuracy of the geostationary (GEO) weather satellite Geo-Kompsat-2A (GK-2A) Rain Rate (RR) product. The IR2Rain model is built upon a conditional generative adversarial network, taking GK-2A brightness temperatures as inputs and Integrated Multi-satellitE Retrievals for GPM (IMERG) Final RRs as target values. To address the distinct physical characteristics and ranges of the input and target datasets, IR2Rain employs preprocessing for normalization and postprocessing for denormalization. The IR2Rain model is developed and validated using the paired input and output datasets collected between September 2019 and December 2022, encompassing a broad region across Asia and Oceania. This study compares the performance of IR2Rain-derived RRs against IMERG RR, GK-2A RR, and PERSIANN-Dynamic Infrared Rain Rate-Now products. The results demonstrated a probability of detection of 0.607, a critical success index of 0.482, a root mean square error of 0.759 mm/hr, and a correlation coefficient of 0.671. By combining the high temporal resolution of GEO satellite observations with the reliability of IMERG Final data, the IR2Rain model produces a robust near-real-time IMERG-like precipitation product. Despite smoothing effects and the tendency to underestimate intense rainfall, IR2Rain improves the performance relative to RR products based on the same GK-2A IR data, mitigates the latency encountered in IMERG data generation, and provides timely and accurate precipitation information on intensity and distribution. These products are particularly valuable for operational weather forecasting and public end-users in Asia and Oceania, supporting disaster preparedness and hydrological applications.