Ryu Sugimoto

Interferometric synthetic aperture radar (SAR) measurements are affected by systematic bias, referred to as “fading signal,” when explicitly using temporally short-term and spatially multilooked interferograms. Investigations using extensive time-series data from the Sentinel-1 C-band SAR have identified soil moisture and biomass changes as potential causes of the observed biases. Although phase biases are supposed to increase with increasing wavelengths, detailed studies on the biases observed in ALOS-2 L-band SAR have been limited owing to restrictive data distribution policies. We therefore investigated the systematic bias in ALOS-2 multilooked interferograms across various interferometric pairs and land-cover types over 5 years of its observation. With regard to the behavior of the bias for positive and negative variations in soil moisture, our results demonstrated consistency with simulations performed using an interferometric model for soil moisture. The observed bias was >4 mm/yr for an average temporal baseline of 72 days. Our research suggested by using L-band SAR that the observed bias can be attributed to changes in both soil moisture and biomass. Furthermore, we developed a methodology to mitigate the systematic bias in ALOS-2 multilooked interferograms. This methodology utilizes our findings that a noise-filtering technique proposed by Pepe et al. (2015) can overcome these biases through the utilization of medium-term interferograms and redundant triplets, regardless of the land-cover type.

This letter proposes a novel synthetic aperture radar (SAR) time series analysis method based on the scattering power decomposition algorithm with a reasonable ensemble average in both temporal and spatial domains. We reveal that the ensemble average is effective not only in the spatial domain but also in the temporal–spatial domains in the scattering power decomposition. That is, if we extend the ensemble average window in the temporal domain, the proposed method can accurately achieve volume scattering power with a higher spatial resolution than conventional approaches. The precise volume scattering power serves accurate forest monitoring. As an application, we performed forest disturbance detection in the Amazon rainforest using Sentinel-1 time series data. The proposed method detected the disturbances earlier, in less than 2 months, compared to other methods that take about 3 months.

In this research, we conduct scattering analysis for a simplified manmade objects model and investigate the polarimetric scattering characteristics, as a fundamental study for accurately detecting oriented manmade area in polarimetric SAR (PolSAR) image. The Finite-Difference Time-Domain (FDTD) method is utilized here. The model used in the analysis is made of buildings parts (four dielectric rectangular parallelepipeds) and a ground part (a circular plate), to include multiple scattering contribution between buildings in this analysis. As the ground part, three different models showing three different surface conditions are considered: 1) dielectric ground model with flat surface, 2) conductor ground model with flat surface, and 3) dielectric ground model with rough surface. Characteristics of polarimetric correlation coefficient in HV and LR bases for the models are in detail investigated for squint angle variation.

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized.

Accurate attitude information from a satellite image sensor is essential for accurate map projection and reducing computational cost for post-processing of image registration, which enhance image usability, such as change detection. We propose a robust attitude-determination method for pushbroom sensors onboard spacecraft by matching land features in well registered base-map images and in observed images, which extends the current method that derives satellite attitude using an image taken with 2-D image sensors. Unlike 2-D image sensors, a pushbroom sensor observes the ground by changing its position and attitude according to the trajectory of a satellite. To address pushbroom-sensor observation, the proposed method can trace the temporal variation in the sensor attitude by combining the robust matching technique for a 2-D image sensor and a non-linear least squares approach, which can express gradual time evolution of the sensor attitude. Experimental results using images taken from a visible and near infrared pushbroom sensor of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra as test image and Landsat-8/OLI images as a base map show that the proposed method can determine satellite attitude with an accuracy of 0.003° (corresponding to the 2-pixel scale of ASTER) in roll and pitch angles even for a scene in which there are many cloud patches, whereas the determination accuracy remains 0.05° in the yaw angle that does not affect accuracy of image registration compared with the other two axes. In addition to the achieved attitude accuracy that was better than that using star trackers (0.01°) regarding roll and pitch angles, the proposed method does not require any attitude information from onboard sensors. Therefore, the proposed method may contribute to validating and calibrating attitude sensors in space, at the same time better accuracy will contribute to reducing computational cost in post-processing for image registration.