Conference Agenda

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 until now are summarized into four contributions:

1. The performance of three typical TomoSAR super-resolution algorithms was evaluated (i.e., Capon, MUSIC and CS methods) in reconstructing tropical forest tomographic profile and in obtaining the forest height and underlying topography based on the scattering characteristics of the forest. Furthermore, the effects of different baseline designs and filters on the results were discussed. The experimental results show that: (1) All the algorithms have the ability to reconstruct tomographic profile. Considering the robustness and time-liness of the algorithm, Capon algorithm performs well and is recommended. (2) Under the same conditions, the more baselines, the more uniform distribution baselines, the better recon-struction of the tomographic profile. (3) In order to obtain forest height and underlying topography, it is necessary to select the appropriate filter window size and filters. Smaller Windows fail to suppress side lobes, and larger ones tend to lose detail. With the experimental result, Hamming window filter performs well and is recommended.

2. Single-image polarimetric SAR backscatter coefficient information has great advantages in regional or national scale forest height and biomass extraction, since it is not limited by interferometric geometry. However, in forested areas with large topographic relief, the SAR backscatter echo signal is easily affected by the terrain, which limits its effective association with the vertical structure parameters of the forest. In view of this fact, we established a multi-stage SAR backscatter coefficient correction strategy in complex terrain forest scenes, including:（1）For the influence of the azimuth slope, the polarization orientation angle and correct the polarimetric covariance matrix were calculated. (2) Considering the heteromorphic relationship between the SAR slant range image space and the geographic coordinate space, a hybrid projection angle (HPA) approach was used to correct the SAR effective scattering area; (3) To solve the residual terrain effect caused by SAR observation and target geometry in the forest scene, we adopt a LUT correction method based on SAR look angle and range slope. After applying the above-mentioned SAR terrain radiometric comprehensive correction for forest scene, we further used an RVoG semi-empirical model method to obtain the forest height. Finally, we validate the algorithm with Full- polarimetric UAVSAR data covering a mountainous forest area.

3) A new technique will be presented to estimate a set of Atmospheric Phase Screens (APS) from a stack of C-Band Sentinel-1 SAR data. The algorithm exploits the so-called Phase Linking algorithm, which can optimally estimate the interferometric phase over distributed and permanent targets. The joint exploitation of the two is the critical factor enabling the generation of dense and uniform maps spanning thousands of kilometers, even over highly decorrelating areas such as tropical forests. The procedure is first tested in South Africa by generating delay maps as large as 210,000 square kilometers. The area shows severe decorrelation and steep topography. Still, the proposed algorithm could produce a reliable estimate of the differential atmospheric delay. The orbital correction routine is also employed, and the derived APS are validated using an external NWPM (GACOS). Some spatial statistics are derived from the delay maps, and we show that they follow theoretical models in the literature

4) Different approaches to structural analyses of forested areas are presented based on the bistatic and multi-frequency data-set TomoSense, collected in the context of an ESA study in 2020/21. In the study here reported, the data are processed in two different fashions. The first one consists in using multi-baseline data to form a tomographic reconstruction of the forest vertical profile, using well known methods from SAR tomographic processing. The second one takes advantage of the phase histogram approach, which under some circumstances allows for an estimation of forest structure using single-baseline data. The two approaches are here compared concerning their capability to correctly estimate forest structure and forest height.

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. Climate forecasts are needed so that we can prepare, mitigate and adapt to the changing climate. The forecasts require accurate information about the sources and sinks of natural and anthropogenic GHGs, in particular, carbon dioxide (CO₂) and methane (CH₄). Presently, GHG concentrations are observed using ground-based and satellite observations. While local sources can be observed using accurate in-situ measurements, remote sensing methods from satellites are needed to obtain global and regional coverage, which are important for climate research. A number of studies have indicated that uncertainties in regional CO₂ and CH₄ surface fluxes can be significantly reduced with global, unbiased, precise space-borne measurements which can lead to a more complete understanding of the CO₂ and CH₄ budget. The accuracy requirements of satellite remote sensing of atmospheric composition and, in particular, GHGs are challenging. Validation of measurements and their uncertainties and continuous development of retrieval methods are important for the success of satellite remote sensing systems, especially for GHGs where error requirements are demanding. Furthermore, sophisticated data assimilation methods and atmospheric transport models are needed to link atmospheric concentration to the underlying surface fluxes.

In this project we use a combination of ground-based measurements of CO₂ and CH₄ and data from current satellite observations (TanSat, GOSAT/-2, OCO-2/-3 and TROPOMI) to validate and evaluate satellite retrievals with retrieval intercomparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄. The main geographic focus is China but we will also take advantage of the global view provided by the space-borne data.

We will show validation results from the TCCON network and Chinese ground-based measurements complemented with AirCore profile observations of GHGs at Sodankylä. We will also present the outcome of an intercomparison of two independent retrieval algorithms available at University of Leicester and IAP that have been applied to TanSat. Furthermore, we discuss CO2 and CH4 surface flux results obtained with the GEOS-Chem atmospheric transport model combined with Ensemble Kalman Filter. We will conclude the presentation with an outlook towards future satellite missions for greenhouse gases.

spectrometer onboard the Chinese satellite GaoFen-5 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The spectral quality and greenhouse gas (GHG) inversion ability of the Level 1 products are analyzed, and the results illustrate that the specifications meet the mission’s requirements. An initial evaluation of the resolution, signal-to-noise ratio (SNR), and stability of the radiance spectrum reveals that the overall function and performance are within the design objectives. A comparison between our Level 1 products and the theoretical spectrum shows that the root mean square (rms) of the residual is approximately 0.8%, and the Level 1 products of the GMI captured within five months after observations have good spectral stability characteristics (less than 0.005 cm−1 for Band 1, 0.003 cm−1 for Band 2, 0.002 cm−1 for Band 3, and 0.004 cm−1 for Band 4). These results demonstrate that the GMI payload and the processing algorithm all work well and reliably. Furthermore, based on the Level 1 products, a GHG retrieval experiment is carried out, and the results are compared with data from Total Column Carbon Observing Network (TCCON) stations. The initial comparison of the XCO2 results yields a value of 0.869 for R2 (goodness of fit), 0.51 ppm for bias (mean of absolute error), and 0.53 ppm for standard deviation of error. Similarly, the XCH4 comparison yields values of 0.841 for R2, 4.64 ppb for bias, and 4.66 ppb for standard deviation of error.