Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides.

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm³/cm³ and 0.032 cm³/cm³ at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.

This study presents an efficient super-resolution (SR) method for targets observed by satellite synthetic aperture radar (SAR). First, a small target image is extracted from a large-scale SAR image and undergoes proper preprocessing. The preprocessing step is adaptively designed depending on the types of movements of targets. Next, the principal scattering centers of targets are extracted using the compressive sensing technique. Subsequently, an impulse response function (IRF) of the satellite SAR system (IRF-S) is generated using a SAR image of a corner reflector located at the calibration site. Then, the spatial resolution of the IRF-S is improved by the spectral estimation technique. Finally, according to the SAR signal model, the super-resolved IRF-S is combined with the extracted scattering centers to generate a super-resolved target image. In our experiments, the SR capabilities for various targets were investigated using quantitative and qualitative analysis. Compared with conventional SAR SR methods, the proposed scheme exhibits greater robustness towards improvement of the spatial resolution of the target image when the degrees of SR are high. Additionally, the proposed scheme has faster computation time (CT) than other SR algorithms, irrespective of the degree of SR. The novelties of this study can be summarized as follows: (1) the practical design of an efficient SAR SR scheme that has robustness at a high SR degree; (2) the application of proper preprocessing considering the types of movements of targets (i.e., stationary, moderate motion, and complex motion) in SAR SR processing; (3) the effective evaluation of SAR SR capability using various metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), focus quality parameters, and CT, as well as qualitative analysis.

Recently, target analysis using satellite SAR images has received much attention in the area of satellite SAR remote sensing. Because the spatial resolution of the target response in the satellite SAR image is a main factor that has a large effect on target analysis performances, the improvement of the spatial resolution of target response is required to enhance the target analysis capability. However, the spatial resolution is already determined in the satellite SAR system design process. To solve the above problem, the super-resolution techniques that have been applied to radar images can be utilized. However, the application of the super-resolution techniques to the target response in the satellite SAR image is not simple due to the following reasons. First, the target’s motion induces severe blurring of the target response, which impedes the successful improvement of spatial resolution. Next, the zero-region in the frequency spectrum of the target image containing the target response also hinders the generation of the super-resolved image. To successfully improve spatial resolution of the satellite SAR image, the super-resolution techniques should be combined with proper preprocessing steps that can cope with the above two issues. In this paper, the whole super-resolution procedure for target responses in KOMPSAT-5 images is described. To the best of the authors’ knowledge, the description of the whole super-resolution procedure for target responses is the first ever attempt in the area of satellite SAR. First, a target image containing the target response is extracted from a large-scale KOMPSAT-5 image. Subsequently, the target image is transformed to be appropriate for the utilization of super-resolution techniques by proper preprocessing steps, considering the direction of super resolution and the motion of the target. Then, some super-resolution techniques are utilized to improve the spatial resolutions and qualities of the target images. The super-resolution performances of the proposed scheme are validated using various target images for point static, extended static, and extended moving targets. The novelties of this paper can be summarized as follows: (1) the practical design of whole super-resolution processing for real satellite SAR images; (2) the performance evaluation of super-resolution techniques on real satellite SAR images. The results show that the proposed scheme can led to noticeable improvements of spatial resolution of the target images for various types of targets with reliable computation times. In addition, the proposed scheme also enhanced PSLR, ISLR, and IC, leading to clearer scattering information of the principal scatterers. Consequently, the proposed method can assist in extracting more precise and meaningful information for targets represented in KOMPSAT-5 images, which means great potential for target recognition.

In this study, Cross calibration was conducted at the Libya 4 PICS site on 2015 using Landsat-8 and KOMPSAT-3A. Ideally a cross calibration should be calculated for each individual scene pair because on any given date the TOA spectral profile is influenced by sun and satellite view geometry and the atmospheric conditions. However, using the near-simultaneous images minimizes this effect because the sensors are viewing the same atmosphere. For the cross calibration, the calibration coefficient was calculated by comparing the at sensor spectral radiance for the same location calculated using the Landsat-8 calibration parameters in metadata and the DN of KOMPSAT-3A for the regions of interest (ROI). Cross calibration can be conducted because the satellite sensors used for overpass have a similar bandwidth. However, not all satellites have the same color filter transmittance and sensor reactivity, even though the purpose is to observe the visible bands. Therefore, the differences in the RSR should be corrected. For the cross-calibration, a calibration coefficient was calculated using the TOA radiance and KOMPSAT-3 DN of the Landsat-8 OLI overpassed at the Libya 4 Site, As a result, the accuracy of the calibration coefficient at the site was assumed to be ± 1.0%. In terms of the results, the radiometric calibration coefficients suggested here are thought to be useful for maintaining the optical quality of the KOMPSAT-3A.