GaoFen-3, the first polarimetric SAR satellite of China, carried out polarimetric calibration experiments using C-band polarimetric active radar calibrators (PARCs), trihedral corner reflectors (TCRs), and dihedral corner reflectors (DCRs). The calibration data were firstly processed referring to the classic 2 × 2 receive R and transmit T model for radar polarimeter systems, first proposed by Zebker, Zyl, and Held, and Freeman’s method based on PARCs, but the results were not good enough. After detailed analysis about the GaoFen-3 polarimetric system, we found that the system had some nonlinearity, then a new imbalance parameter was introduced to the classic model, which is equivalent to the γ proposed in Freeman’s paper about a general polarimetric system model. Then, we proposed the calibration data processing algorithm for GaoFen-3 based on the improved model and obtained better results. The algorithm proposed here is verified to be suitable for GaoFen-3 and can be applied to other spaceborne and airborne fully-polarimetric SAR systems.

In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement.

The parameters for polarization distortion of spaceborne polarimetric synthetic aperture radar (SAR) have range-dependence (or look-angle-dependence), which requires a polarimetric calibration to be performed at any look-angle. It is a huge endeavor to rely solely on ground experiments to obtain a polarimetric calibration at all look-angles. For SAR with phased array antennas we deduce, based on the model for the general polarimetric system, the model for fine polarization distortion described by the parameters of the radar device under the condition of high polarization isolation. We point out the mechanism that causes both variable and constant polarization distortions, and we deduce the correction algorithms for the two types of polarization distortion. Then we propose a polarimetric calibration scheme combining internal and external calibrations to calibrate the two types of polarization distortions for SAR with phased array antennas. The scheme uses the internal calibration data of the radar and the model of the antenna pattern established before satellite launch to invert the in-orbit antenna patterns to correct for the variable polarization distortion, and it needs only a small amount of calibration equipment to solve for the parameters for constant polarization distortion. The scheme no longer depends on the distributed target and improves the polarization precision of the data. It is applied to the calibration experiment for the data processing of the GaoFen-3 satellite and has achieved good results in applications.