Conference Agenda

The AGRIROSE-L campaign coordinated and conducted by the German Aerospace Center (DLR) was conducted in cooperation with the LMU, GFZ and CzechGlobe over an agricultural area in southern Germany called Puch. The campaign's primary goal is to provide calibration and validation data to support future Earth observation missions, specifically ROSE-L and CHIME, with a focus on improving the monitoring of soil moisture and health, crop growth, and other agricultural parameters from space. The data collected is crucial for developing and testing algorithms for sustainable agriculture.

For this the DLR’s F-SAR system recorded a globally unique dataset across four different frequency ranges (the X, C, S and L bands). In total, the radar team carried out 23 measurement flights between April and July covering the whole agricultural vegetation season. On selected days, the flights took place in the morning, at midday and in the evening to record any daily changes in the soil and vegetation.

The data was collected using innovative imaging techniques such as polarimetry, interferometry and tomography. Experienced DLR test pilots flew specified paths with metre-level precision, supported by the satellite-based navigation system integrated into the F-SAR. Parallel to each flight, a team from LMU collected ground measurements of soil and vegetation parameters, such as soil moisture, surface roughness, plant water content and plant biomass. In addition, four times also the DLR’s hyperspectral sensor HySpecs was flown over the same area and one-time CzechGlobes hyperspectral sensor acquired data.

In this research work the campaign, its collected data and the first performance analysis are presented.

Provided that there is enough penetration in the medium, multibaseline interferometric acquisitions can be used to reconstruct the vertical profile of the scene via its power spectral density [1]. This technique, known as radar tomographic imaging, is particularly advantageous because it does not alter the volume of interest and provides information from the whole scene, contrasting with other methods that only retrieve a profile for a single datapoint. [2,3]

We have conducted our tomographic experiment with KAPRI, a Ku-band ground-based interferometer with full-polarimetric capabilities [4]. In order to create a tomographic array, it was necessary to use two KAPRI devices (G21 and G22) operating in bistatic mode. The devices were placed in different locations, resulting in a separation of a few-meters in the horizontal and vertical directions that together with the local geometry determined the effective baseline. The KAPRI units were used sequentially. First, the G21 acted as both transmitter and receiver (master) while G22 was used as passive receiver only (slave). In the next step, the devices exchanged roles. The temporal baseline between transmissions was kept below 3 minutes, which allowed to consider consecutive acquisitions as simultaneous, hence, increasing the density of the tomographic array.

The tomographic imaging was performed in two locations. The first campaign took place in mid-February, a five-hour long time series were retrieved from the Jungfraufirn region (Aletschglacier, Switzerland), in a flat and homogeneous snow-covered region of the glacier. Additionally to the radar measurements, in-situ glacier investigations were done as a support for later data processing and interpretation. The complementary fieldwork consisted of installing two corner reflectors on the glacier surface for radar measurements, snow-depth investigations, recording the temperature and density of snow, taking images of snow grains and using a metallic scatterer inside the snowpack for depth penetration estimation. A second campaign was done in ETH Hönggerberg on a meadow area with the purpose of helping with the processing of the previous dataset and investigating decorrelation phenomena of unclear source on the Jungfraujoch campaign.

The data pre-processing is proving to be particularly challenging given the fact that Ku-Band has such a small wavelength (1.74cm) and makes the system very sensitive to small inaccuracies of the horizontal baseline. Thus, making the coregistration step very time consuming. Furthermore, the temporal baseline, despite being so small, is enough to cause decorrelation at this wavelength, in turn, resulting into noisy interferograms. Such problem had to eventually be solved by using very strong adaptive filtering (Goldstein filter).

Even though preprocessing is still on-going, there are results that are valuable due to the lack of investigations of different media using Ku-Band. Our specific radar configuration leads to a narrow “usable” region of the scene due to horizontal decorrelation. However, this limitation allows us to observe that the snow and vegetation datasets exhibit different decorrelation behaviors. We interpret this as the meadows behaving closer to a surface scatterer while the snowpack to a volume scatterer. Further analysis of the dataset will determine if the volume information contained in the radar signal can be exploited to reconstruct the vertical profile.

References

[1] Stoica, P., & Moses, R. L. (2005). Spectral Analysis of Signals. Prentice Hall.

[2] Tebaldini, S., et al. (2013). High-resolution 3D imaging of a snowpack from ground-based SAR at X and Ku band. IGARSS.

[3] Frey, O., et al. (2023). Time-series analysis of snow vertical profiles by SAR tomography at L/S/C, Ku, and Ka bands vs. snow characterization. IGARSS, 754–757.

[4] Werner, C., et al. (2012). The GPRI multi-mode differential interferometric radar for ground-based observations. EUSAR, 304–307.

NASA/JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) has been deployed to collect multi-baseline interferometric SAR observations across a diverse range of forest biomes, including tropical forests in Africa and Central America, temperate forests in California and Maine, and boreal forests in Alaska and Saskatchewan, Canada. For each site, the multi-baseline data acquisition was designed and optimized based on expected canopy height, radar frequency (L- or P-band), and the number of interferometric tracks achievable within a single flight mission. The acquired multi-baseline data are processed, polarimetrically calibrated, and co-registered into stacks of single-look complex (SLC) images. To remove residual phase screens among SLCs, we apply the phase center double localization (PCDL) method for inter-track phase calibration. The resulting calibrated SLC stacks serve as input for subsequent polarimetric interferometric SAR (PolInSAR) and tomographic SAR (TomoSAR) processing. PolInSAR processing is performed using Kapok, an open-source software developed at JPL, to compute the full PolInSAR covariance matrix from the calibrated SLC stack. For TomoSAR processing, we use the Capon beamforming algorithm to reconstruct 3-D radar backscatter voxels (tomographic cubes). To retrieve canopy height and ground elevation beneath vegetation, we developed a SAR-Lidar data fusion workflow capable of ingesting a variety of PolInSAR and/or TomoSAR input, or a combination of both. In this presentation, we summarize the data processing and machine learning framework used to generate PolInSAR/TomoSAR-based canopy height and bare surface topography retrievals, and evaluate the performance and generalizability of data fusion models trained with different number of baselines, radar frequencies, and forest types.

Tropical forests are particularly important. Although they only cover about 6% of Earth’s surface, they are home to approx. 50% of the world’s animal and plant species. Their trees store 50% more carbon than trees outside the tropics. At the same time, they are one of the most endangered ecosystems on Earth: about 6 million of hectares per year are felled for timber or cleared for farming. Compared to the other components of the carbon cycle (i.e. the ocean as a sink and the burning of fossil fuels as a source), the uncertainty in the land local carbon stocks and the carbon fluxes are particularly large. This is especially true for tropical forests, which remain poorly characterized compared to other ecosystems on the planet. More than 98% of the land use change flux should be due to tropical deforestation, which converts carbon stored as woody biomass (of which around 50% is biomass) into emissions.

In the frame of the ESAs BIOMASS mission, selected in May 2013 as the 7th Earth Explorer mission to meet the pressing need for information on tropical carbon sinks and sources through estimates of forest height and biomass a first airborne campaign over tropical forest in Gabon was conducted. The campaign, called AFRISAR took place over four forest sites in Gabon where two acquisitions at different season where made, the first one was conducted by ONERA (SETHI system, July 2015) and the second one by DLR (F-SAR system, February 2016).

After 7 years a second campaign, called AFRISAR-2/GABONX, was conducted in 2023 exactly over the same test site and three further one selected by the Gabonese partners (the space agency AGEOS, CENAREST and the Ministry of Forest) with the same configuration. This time the campaign served also the mission objectives of ROSE-L, with its six days repeat-pass cycles acquiring fully polarimetric and multi-baseline data sets in L- and P-band. The main objective of this campaign was to observe short term changes in terms of decorrelation on the interferometric coherence and long term changes over 7 years.

A third campaign was conducted in late 2025 underflying the BIOMASS satellite in P-band for signal calibration and validation. First results of the campaign will be presented and a change analysis will be provided for the 7 and 9 years difference.

Terrestrial laser scanning (TLS) is being recognized as a key technology in forest monitoring by providing highly detailed 3D point clouds of the ecosystem. Recent algorithmic and computational advances now allow for the near-automated processing of the raw point clouds into 3D reconstructions of real forests. Here, we show how these 3D ‘virtual forest’ replicas, combined with the parameterization of its components (e.g. leaves, stems, soil), can serve as input for microwave interaction models (MIM) to study the interaction of electromagnetic waves with forests scenes in a realistic simulation environment.

First, we present ongoing work on in-vivo stem dielectric permittivity estimation with wood penetrating radar (WPR). Novel experimental WPR sensors are currently being tested, which non-destructively measure the forward and back scatter of multi-frequency microwaves emitted through the tree trunk by placing two antennas on opposite sides of the stem. Two such sensors have been installed on a sycamore tree in the Ghent University forest experimental site (Belgium) and have been measuring at a 20-minute time interval since February 2024. Concurrently, weather and microclimate variables are recorded and monthly TLS scans of the tree are made to capture the 3D dynamics (e.g. seasonality, growth, branch loss) of the tree. From the WPR measurements, the (dynamic) dielectric permittivity can be estimated, which holds potential to parametrize the woody components of the virtual forest for microwave MIM. We show preliminary results of how the dielectric permittivity relates to environmental and phenological variability.

Secondly, we demonstrate the use of microwave MIM using data from the Caxiuanã research site in the eastern Amazonia (Para, Brazil), the longest running drought experiment in the tropics. Both the 1-ha control plot and 1-ha rain throughfall exclusion (TFE) plot have been reconstructed into a 3D virtual forest from TLS acquired in November 2024. Additionally, for both plots, a tower radar system is installed centrally in the plot and 21 trees in the field of view of the radar are equipped with FDR sensors to estimate the stem water content. By combining these data sources, we aim to parameterize the MIPERS-4D microwave MIM and will show preliminary results of how simulations compare to actual measurements.

With these two use cases, we aim to demonstrate that the combination of structurally accurate 3D virtual forests with a parameterized microwave RTM would allow for a powerful instrument to facilitate the calibration and validation of remote sensing signals and derived biophysical products such as forest water status or biomass.