Conference Agenda

EMPAC addresses different aspects related to the air quality (AQ) over China: aerosols, trace gases and their interaction through different processes, including effects of radiation and meteorological, geographical and topographical influences. Satellite and ground-based remote sensing together with detailed in situ measurements and model simulations provide complimentary information on the contributions from different sources and processes affecting AQ, with scales varying from the whole of China to local studies and from the surface to the top of the boundary layer and above. Different species contributing to air quality are studied, i.e. aerosols, in AQ studies often represented as PM2.5, trace gases such as NO2, NH3, Volatile Organic Compounds (VOCs) and O3. The primary source of information in these studies is the use of a variety of satellite-based instruments providing data on atmospheric composition using different techniques. However, satellite observations provide column-integrated quantities, rather than near-surface concentrations. The relation between column-integrated and near-surface quantities depends on various processes. This relationship and the implications for the application of satellite observations in AQ studies are the focus of the EMPAC project. Initial results of detailed process studies using ground/based in situ measurements, instrumented towers, as well as remote sensing using lidar and Max-DOAS will be presented. A unique source of information on the vertical variation of NO2, O3, PM2.5 and BC is obtained from the use of an instrumented drone.
The results of the last year will be presented, including trend studies of air pollutants, algorithm development for aerosol retrieval, and analyses of NOx emissions from Sentinel 5p over East China.

The aim of this project consists in the development and application of processing methodologies to address two specific Sub-topics relevant for stack-based spaceborne applications. Sub-topic 1 concerns the internal structure of natural media, and it is mapped to Dragon topic Solid Earth - Subsurface target detection. Subtopic 2 concerns joint estimation of deformation and water vapour maps, and it is mapped to Dragon topic Solid Earth - Monitoring of surface deformation of large landslides. The topics above are of fundamental importance in the context of present and future spaceborne missions, which will allow increasingly more systematic use of multiple acquisitions thanks to improved hardware stability and orbital control. Indeed, the proposed activities are intended to support use of multi-pass data stacks from: the upcoming P-Band mission BIOMASS; future L-Band missions, such as the SAOCOM constellation, the upcoming Chinese L-Band bistatic Mission Lu-Tan1, and potentially Tandem-L and Rose-L; the C-Band Sentinel Missions.

The main results are summarized into 4 contributions:

1. We have proposed a technique that exploits the well-known Phase Linking algorithm in combination with a novel calibration scheme used to remove orbital errors. This calibration scheme is data-driven and uses a network of GNSS stations on the ground to estimate and remove a low-frequency phase trend that arises when the orbital knowledge of the satellite is not accurate enough. Combining the optimal estimator and the calibration procedure allows us to estimate the tropospheric component in the interferometric phase accurately. We validated the method's effectiveness using statistical comparisons with the theoretical models and an external set of GNSS measurements. The correlation between our estimate and the independent GNSS measure is close to one, proving the method's effectiveness.
2. We have demonstrated a fast and effective implementation of the split-spectrum algorithm applied to Sentinel-1 data. The novelty is that we jointly exploit a set of SAR images, gaining from all the available interferograms to reduce the noise and partially compensate for the lack of data-driven coregistration. In contrast with the state-of-the-art workflows for ionospheric signal extraction, we start the procedure by co-registering data immediately before sub-band extraction, allowing greater compatibility with standard InSAR processors. Testing was per-formed using data acquired over Chile, a site where the ionosphere is known to vary rapidly. The compensation showed a significant reduction in phase jumps between bursts and an overall reduction in the number of fringes. Only coregistered data was used during estimation and compensation, making the approach appealing for integration into existing interfero-metric tools.
3. We presented an evaluation using theoretical model and experimental results between SAR Tomography and the Phase Histogram technique in the context of remote sensing of forested areas. The observed results were then interpreted on the basis of a simple physical model to characterize phase histograms as a function of the number of scatterers per resolution cell, which led to an analytical assessment of the expected outcome in terms of peak position and height dispersion. Experimental results showed that phase histograms do provide some indication about forest structure but are far from the accurate representation produced by SAR tomography. According to the developed theory, the results are perfectly consistent with the presence of a distribution of elementary scatterers within each SAR resolution cell, in which conditions phase histograms cannot correctly reproduce forest vertical structure. From a physical point of view, we ascribe the observed behavior to a combination of L-Band penetration capabilities with the features of the TomoSense dataset, i.e. medium spatial resolution and hilly topography. Accordingly, one general conclusion from this work is that phase histograms cannot correctly reproduce forest vertical structure, unless Radar returns in each SAR pixel are actually determined by a dominant scatterer and the variation of the position of dominant scatterers in neighboring SAR pixels is large enough to probe the whole vertical structure. This can be in principle the case of very high-resolution data, preferably in the case where scattering from the terrain level is enhanced by a flat topography. On the other hand, results relative to forest height retrieval show that use of phase histograms achieved an RMS error of 4.45 m and 5.46 m in NW and SW data, respectively. Whereas not as accurate as the results from tomography those results advocate for the actual presence of strong, although not fully dominant, scatterers within each SAR pixel. Overall, these results confirm that the PH technique is a powerful method to retrieve forest height, especially considering that it can be implemented using just two acquisitions. In this sense, we reiterate our recommendation that the PH technique should be applied to high-resolution data, preferably paired with fully polarimetric acquisitions to help the detection of scattering from the terrain level using well-known PolInSAR methods.
4. The Common Scene Stacking (CSS) method adopts a simple strategy of mitigating tropospheric delay in time-series SAR interferograms by iteratively estimating the tropospheric delay of the common date in the data stack. However, this method might be less effective due to noises introduced by the point-by-point estimation of the atmospheric delay phase. Aiming at this problem, we proposed an improved CSS method with the incorporation of a spatially low-pass filtering for the estimated tropospheric delay phases subject to the assumption that the atmospheric delay phase is a low-frequency component in the spatial domain. In the improved CSS method, phase unwrapping and CSS atmospheric delay phase estimation are alternatively iterated to improve the deformation estimation results. Experimental results for real Sentinel-1 data show that CSS method can achieve significant reduction of spatial and temporal deviations of differential interferometric phase by 62% and 58%, respectively.