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Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM. [ABSTRACT FROM AUTHOR]

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results. [ABSTRACT FROM AUTHOR]

Polarization and infrared imaging technology have unique advantages for various applications ranging from biology to ocean remote sensing. However, conventional combined polarization camera and infrared camera have limitations because they are constrained to single-band imaging systems with rotating polarizers and cascaded optics. Therefore, we propose a common-aperture mode based on multi-band infrared guided polarization imaging system (IGPIS) in this paper, which consists of infrared wide-area sensing and polarization features acquisition for accurate detection of ship targets. The IGPIS can provide images in visible polarization (0.45–0.76 μm), near-infrared polarization (0.76–0.9 μm), and long-wave infrared (8–12 μm) bands. Satellite attitude parameters and camera optical parameters are accurately calculated by establishing a dynamic imaging model for guidance imaging. We illustrate the imaging principle, sensors specifications and imaging performance analysis and the experimental results show that the MTF is 0.24 for visible and near-infrared, and 0.13 for long-wave infrared. The obtained multi-band images have an average gradient of 12.77 after accurate fusion. These results provide theoretical guidance for the design of common-aperture cameras in remote sensing imaging field. [ABSTRACT FROM AUTHOR]

Over the past decade, IECAS (Institute of Electronics, Chinese Academy of Sciences) has developed a set of L-, S-, C-, and X-band active radar calibrators that are deployed during the calibration campaigns for HJ1C synthetic aperture radar (SAR), Gaofen-3 SAR, and so on. In the near future, P-band and Ka-band spaceborne SARs will be launched. We found that it is not convenient to develop special active radar calibrators (ARCs) for a specific SAR or a specific frequency band SAR, and the acquired experience could help in the design and development of a multi-band ARC. This paper describes the design and implementation of a multi-band active radar calibrator which can operate in the L-, C-, X-, and Ka-bands. Moreover, laboratory measurements are performed to characterize the performance of the multi-band ARC, paying particular attention to the gain stability, the system transfer function, the gain flatness, and the linearity of the ARC receiver. Three such ARCs are developed, and to our knowledge, the multi-band ARC is the first of its kind in China or even in the world, and it can be used to implement the calibration campaigns of the Chinese Gaofen-3 SAR, Shenzhen-1 SAR, Luojia-2 SAR, and so on.

Over the past decade, IECAS (Institute of Electronics, Chinese Academy of Sciences) has developed a set of L-, S-, C-, and X-band active radar calibrators that are deployed during the calibration campaigns for HJ1C synthetic aperture radar (SAR), Gaofen-3 SAR, and so on. In the near future, P-band and Ka-band spaceborne SARs will be launched. We found that it is not convenient to develop special active radar calibrators (ARCs) for a specific SAR or a specific frequency band SAR, and the acquired experience could help in the design and development of a multi-band ARC. This paper describes the design and implementation of a multi-band active radar calibrator which can operate in the L-, C-, X-, and Ka-bands. Moreover, laboratory measurements are performed to characterize the performance of the multi-band ARC, paying particular attention to the gain stability, the system transfer function, the gain flatness, and the linearity of the ARC receiver. Three such ARCs are developed, and to our knowledge, the multi-band ARC is the first of its kind in China or even in the world, and it can be used to implement the calibration campaigns of the Chinese Gaofen-3 SAR, Shenzhen-1 SAR, Luojia-2 SAR, and so on. [ABSTRACT FROM AUTHOR]