Temporal decorrelation model for the bistatic SAR interferometry

This paper develops a temporal decorrelation model for the bistatic synthetic aperture radar（BSAR） interferometry. The temporal baseline is one of the important decorrelation sources for the repeat-pass synthetic aperture radar（SAR） interferometry. The study of temporal decorrelation is challenging, especially for the bistatic configuration, since temporal decorrelation is related to the data acquisition geometry. To develop an appropriate theoretical model for BSAR interferometry, the existing models for monostatic SAR cases are extended, and the general BSAR geometry configuration is involved in the derivation. Therefore, the developed temporal decorrelation model can be seen as a general model.The validity of the theoretical model is supported by Monte Carlo simulations. Furthermore, the impacts of the system parameters and BSAR geometry configurations on the temporal decorrelation model are discussed briefly.

Monitoring mine collapse by D-InSAR

For harmful ground collapse and its special deformation characteristics,which causes SAR images to lose coherence,InSAR technology cannot be applied in monitoring surface collapse in mining areas.We took the Shenmu mining area in northern Shaanxi province as an example to study subsidence in mining areas and proposed an interpolated multi-view processing method.The results show that this method can improve the detectable deformation gradient to a certain extent and can become a good reference value for monitoring large scale gradient deformation.We also analyzed the rules for temporal decorrelation in mining.

Progresses on SAR Remote Sensing of Tropical Forests:Forest Biomass Retrieval and Analysis of Changing Weather Conditions

This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation(ID:32278),sub-project Multi-baseline SAR Processing for 3 D/4 D Reconstruction(ID:322782).The work here reported focuses on two important aspects of SAR remote sensing of tropical forests,namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions.Recent studies have shown that by using SAR tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest Above Ground Biomass(AGB).However,the two parameters that determine this linear relationship might vary for different tropical forest sites.For purpose of solving this problem,we investigate the possibility of using Li DAR derived AGB to help training the two parameters.Experimental results obtained by processing data from the Tropi SAR campaign support the feasibility of the proposed concept.This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging,for which we simulate BIOMASS repeat pass tomography using ground-based Tropi SCAT data with a revisit time of 3 days and rainy days included.The resulting backscattered power variation at 30 m is within 1.5 d B.For this forest site,this error is translated into an AGB error of about 50~80 t/hm^(2),which is 20%or less of forest AGB.

Forest-height inversion using repeat-pass spaceborne polInSAR data

Forest-height inversion using airborne double-antenna synthetic aperture radar(SAR)systems has been widely researched,leading to increasing accuracy.Polarimetric SAR Interferometry(PolInSAR)data from spaceborne single-antenna SAR systems,which are influenced by temporal decorrelation,have difficulty inverting forest height.Given the temporal decorrelation effect,the classical random volume over ground(RVoG)model has been proven to invert forest height with significant errors,using repeat-pass PolInSAR data.In consideration of this problem,the temporal decorrelation RVoG(TD-RVoG;based on the RVoG)model was proposed.In this study,an improved TD-RVoG model is presented,with a new temporal decorrelation function.Compared with TD-RVoG,the new model has fewer unknown parameters and can be applied in a three-stage inversion procedure.Validity of the new model is demonstrated by Advanced Land Observing Satellite/Phased Array type L-band SAR(ALOS/PALSAR)data.Results show that the improved TD-RVoG has better accuracy,with inversion error less than 1.5m.