In this paper, the authors propose a novel radio frequency interference (RFI) detection and removal method for a synthetic aperture radar (SAR). The proposed method is exclusively sensitive to a pulse-like signal that is temporally transmitted in wide bandwidth. The bandwidth of the RFI is supposed to be similar or wider than the bandwidth of the SAR, while traditional RFI detectors assume that it is narrower and thus it can be removed in the range-frequency and azimuth-time domain using a bandpass filter. On the other hand, the proposed method employs a local autocorrelation-based detection scheme in the range-time and azimuth-frequency domain. The proposed method assumes the existence of another type of RFI, namely, intermittently transmitted wideband (ITWB) RFI. Once ITWB RFI superimposes on the SAR image, its short-pulse duration/wide bandwidth hinders the detection in the range-frequency and azimuth-time domain. Conversely, in the range-time and azimuth-frequency domain, ITWB RFI can be detected easily because its Doppler frequency profile does not fit with backscattered signals from the ground. In this paper, a basic methodology and the experimental results are presented.

Global carbon cycle research and climate change mitigation measures require a means for large-scale monitoring of forest aboveground biomass (AGB). L-band synthetic aperture radar (SAR) is one of promising means, although its signal is saturated at 75–150 Mg ha−1 of AGB, making it difficult to monitor high biomass forest. The Advanced Land Observing Satellite-2/Phased Array L-band SAR-2 (PALSAR-2) conducts ScanSAR mode observations that cover the whole tropical region about nine times a year. We investigated whether such time-series data is effective in overcoming the signal saturation issue. In Borneo, we adopted a spaceborne Light Detection and Ranging (LiDAR) Ice, Cloud, and Land Elevation/Geoscience Laser Altimeter System (GLAS)-derived AGB data for training and validation data, then developed an AGB estimation model using the Random Forest algorithm. As a result, we improved the saturation issue, and estimated the AGB up to 280 Mg ha−1 with a root mean square error (RMSE) of 62.8 Mg ha−1. Such an AGB range covers 83% of Borneo's forests. The developed model was applied to create an AGB map of Borneo with a 250-m resolution as of 2016. Total AGB of Borneo was calculated at 12.8 Gt with an average of 173.3 Mg ha−1. This article showed the PALSAR-2 time-series data to be highly useful in the AGB mapping of high carbon stock forests. However, we needed to correct the difference in observation years between GLAS and PALSAR-2 using a simple biomass growth model, but the accuracy will be improved by using future LiDAR and SAR sensors.

Cloud contamination is one of the severest problems for the time-series analysis of optical remote sensing data such as vegetation phenology detection. Sub-pixel clouds are especially difficult to identify and remove. It is important for accuracy improvement in various terrestrial remote sensing applications to clarify the influence of these residual clouds on spectral vegetation indices. This study investigated the noises caused by residual sub-pixel clouds on several frequently-used spectral indices (NDVI, EVI, EVI2, NDWI, and NDII) by using in situ spectral data and sky photographs at the satellite overpass time. We conducted in situ continuous observation at a Japanese deciduous forest for over a year and compared the MODIS spectral indices with the cloud-free in situ spectral indices. Our results revealed that residual sub-pixel clouds potentially contaminated about 40% of the MODIS data after cloud screening by the state flag of MOD09 product. These residual clouds significantly decreased NDVI values during the leaf growing season. However, such noises did not appear in the other indices. This result was thought to be caused by the different combination of wavelengths among spectral indices. Our results suggested that the noises by residual sub-pixel clouds can be reduced by using EVI, NDWI, or NDII in place of NDVI.

JAXA’s Advanced Land Observing Satellite-4 (ALOS-4) is the successor to the ALOS-2 satellite. On July 1, 2024, ALOS-4 was successfully launched, and on July 15, it acquired the first observation images by PALSAR-3. The new satellite carries a Phased Array-type L-band Synthetic Aperture Radar-3 (PALSAR-3) and SPace based Automatic Identification System for ships Experiment 3 (SPAISE3). The objectives of ALOS-4 are enhancing the ALOS-2 mission and exploring new applications of L-band SAR. PALSAR-3 has a wider swath width and frequent time-series data acquisition capability, and SPAISE3 improves the ship detection rate in crowded sea areas. The tests of the satellite and ground systems have been completed, and the functions and performance have been confirmed as designed. The PALSAR-3 CAL/VAL and observation plans have also been formulated for consistent data acquisition from ALOS-2. ALOS-4 will monitor land deformation and subsidence, natural disasters, environmental and ecosystem concerns, and maritime applications for social benefit.

PALSAR2 polarimetric distortion matrix is measured using corner reflectors deployed in the Amazonian forest. The Amazonian forest near the geomagnetic equator provides ideal sites for the assessment of L-band PALSAR2 antenna parameters, at free Faraday rotation. Corner reflectors (CRs) deployed at free Faraday rotation provide accurate estimation of antenna cross-talks in contrast to the biased measurements obtained with CRs deployed at significant Faraday rotation. The extended Freeman–Van Zyl calibration method introduced and validated for ALOS-PALSAR calibration is used for the assessment of PALSAR-2 calibration parameters. Six datasets collected over the Amazonian rainforests (with CRs) are used to assess PALSAR-2 distortion matrix for five beams (FP6-3 to FP6-7) with incidence angle varying from 25° to 40°. It is shown that the PALSAR2 antenna is highly isolated with very low cross-talks (lower than −40 dB). Finally, the impact of a significant Faraday rotation on antenna cross-talk measurements using CR is discussed.