While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use of L- and P-band airborne SAR and in situ data measured in the field together with laser scanning data acquired over one hemi-boreal (Remningstorp) and one boreal (Krycklan) forest study area in Sweden. We fit statistical models to different combinations of topographic-corrected SAR backscatter and forest heights estimated from PolInSAR for the biomass estimation, and evaluate retrieval performance in terms of $R^2$ and using 10-fold cross-validation. The study shows that specific combinations of radar observables from L- and P-band lead to biomass predictions that are more accurate in comparison with single-band retrievals. The correlations and accuracies between the combinations of SAR features and aboveground biomass are consistent across the two study areas, whereas the retrieval performance varied for individual bands. P-band-based retrievals were more accurate than L-band for the hemi-boreal Remningstorp site and less accurate than L-band for the boreal Krycklan site. The aboveground biomass levels as well as the ground topography differ between the two sites. The results suggest that P-band is more sensitive to higher biomass and L-band to lower biomass forests. The forest height from PolInSAR improved the results at L-band in the higher biomass substantially, whereas no improvement was observed at P-band in both study areas. These results are relevant in the context of combining information over boreal forests from future low-frequency SAR missions such as the European Space Agency (ESA) BIOMASS mission, which will operate at P-band, and future L-band missions planned by several space agencies.

The study presented here is focused on the assessment of surface elevations derived from CryoSat-2 SARIn level 1b data over the Austfonna ice cap, Svalbard, in 2016. The processing chain that must be applied to the CryoSat-2 waveforms to derive heights is non-trivial, and consists of multiple steps, all requiring subjective choices of methods such as the choice of retracker, geo-relocation, and outlier rejection. Here, we compare six CryoSat-2 level-2 type data sets of surface elevations derived using different SARIn processing chains. These data sets are validated against surface elevation data collected from an airborne laser scanner, during a dedicated CryoSat validation experiment field campaign carried out in April 2016. The flight pattern of the airborne campaign was designed so that elevations were measured in a grid pattern rather than along single lines, as has previously been the standard procedure. The flight grid pattern was chosen to optimize the comparison with the CryoSat-2 SARIn elevation data, the location of which can deviate from nadir by several kilometers due to topography within the satellite footprint. The processing chains behind the six data sets include different outlier/error rejection approaches, and do not produce the same number of data points in our region of interest. To make a consistent analysis, we provide statistics from the validation of both the full data sets from each processing chain, and on only those data that all the six data sets provide a geo-located elevation estimate for. We find that the CryoSat-2 data sets that agree best with the validation data are those derived from dedicated land ice processing schemes. This study may serve as a benchmark for future CryoSat-2 retracker developments, and the evaluation software and data set are made publicly available.

This work shows that additional ScanSAR capabilities, with respect to those achievable through the current Synthetic Aperture Radar (SAR) system of the forthcoming ROSE-L mission, may be easily enabled through a proper shaping of the azimuth pattern of the TX radar antenna. In particular, we show that by properly acting only on the distribution of the input excitations of the elements of the currently designed TX antenna array, we can move from a single azimuth look ScanSAR configuration to a more attractive two-look one. This result is achieved without upsetting the current system architecture and without changing the current mission parameters (such as azimuth resolution, range swath and prf), all tailored to a one-look ScanSAR configuration. On the other hand, the proposed two-look mode requires to double the azimuth beam-width with respect to the one-look mode, thus leading to an unavoidable antenna gain decrease, whose amount is of approximately 3 dB. The presented analysis also shows that, by still acting on the distribution of the input excitations of the TX antenna array, the scalloping effect can be significantly mitigated with respect to that of the current system design, in both scenarios relevant to the original one-look ScanSAR configuration and to the proposed two-look one.

The objective of this study is to estimate the residual surface soil moisture of rice fields after harvesting, in Northeast Thailand, using C‐band SAR images. Such information is useful to determine possibilities of cultivating additional short‐cycle crops after the harvesting of the main rice crop. To accomplish this objective this study exploits three C‐band SAR images acquired over the test area along with simultaneous ground measurements collected in support of analysis of the SAR images. The main parameters measured on the ground were: the volumetric moisture content of the soil using time domain reflectrometry (TDR), the surface roughness, using profile data, and residue cover. Other field environmental factors that might affect radar backscattering, including the topography of rice terraces (elevation, slope, and aspect), bund height between rice fields, soil types, and density of sparse tree stands were also acquired and registered to the image data. Before the analysis, the SAR images were calibrated, registered, and then filtered using multitemporal and spatial filters to reduce variability of the radar backscattering coefficient due to speckle. The measured soil moisture values were then correlated with the radar backscattering coefficients of the experimental plots and the results interpreted in terms of the main environmental factors. Varying residue cover and field aspect angles were found to significantly affect the relationship between radar backscatter and soil moisture content and therefore had to be taken into account before inverting the image data into soil moisture maps. Overall it was found that two soil moisture classes could reliably be separated using the SAR images, and that soil moisture maps could be obtained through a simple inversion technique based on applying a threshold to the SAR images. Analyses of these resulting moisture maps indicate that SAR data is useful to monitor and map surface soil moisture in rice fields after harvest.

In the yearlong MOSAIC expedition (2019-2020) R/V Polarstern drifted with sea ice through the Arctic Ocean, with the goal to continually monitor changes in the coupled ocean-ice-atmosphere system throughout the seasons. A substantial amount of synthetic aperture radar (SAR) satellite images overlapping the campaign was collected. Here, we investigate the change in polarimetric features over sea ice from the freeze up to the advanced melt season using fully polarimetric L-band images from the ALOS-2 PALSAR-2 and fully polarimetric C-band images from the RADARSAT-2 satellite SAR sensors.Three different sea ice types are investigated, young ice, level first year ice and deformed first and second-year ice. Areas of deformed and level sea ice were observed in the vicinity of R/V Polarstern and these areas are included whenever possible in the yearlong time series.Comparing the different sea ice types, we observe that during the freezing season there is a larger difference in the co-polarization channels between smooth and deformed ice in L-band compared to C-band. Similar to earlier findings we observe larger differences between young ice and deformed ice backscatter values in the L-band data compared to the C-band data. Moreover, throughout the year the HV-backscatter values show larger differences between level and deformed sea ice in L-band than C-band. The L-band data variability is significantly smaller for the level sea ice compared to the deformed sea ice, and this variability was also smaller than that observed for the overlapping C-band data. Thus L-band data could be more suitable to reliable separate deformed from level sea ice areas.   Within the L-band images a noticeable shift towards higher backscatter values in early melt season compared to the freezing season for all polarimetric channels is observed, though no such strong trend is found in the C-band data. The change in backscatter values is first noticeable in the C-band images and later followed by a change in the L-band images, probably caused by their different penetration depth and volume scattering sensitivities. This change also results in a smaller backscatter variability.The polarization difference (PD; VV-HH on a linear scale) show a seasonal dependency for the smooth and deformed sea ice within the L-band data, whereas for the C-band data no such trend is observed. For the L-band data were the PD variability for all ice classes reasonably small for the freezing season, with a significant shift towards larger variability during the early melt season, though during this time period the mean PD values remained similar. However, once the temperatures reached above 0°C both the variability and the mean values increased significantly.Overall, our results demonstrate that the C- and L-band data are complementary to one another and that through their slightly different dependencies on season and sea ice types, a combination of the two frequencies can aid improved sea ice classification. The availability of a high spatial and temporal resolution dataset combined with in-situ information ensures that seasonal changes can be fully explored.

The NASA/ESA Joint Program Planning Group (JPPG) subgroup on satellite calibration/validation was created to facilitate coordinated efforts between ESA, NASA, and their respective investigator communities to enhance calibration and/or validation activities for current and/or future satellite missions. The cooperation enabled through this activity includes airborne campaigns, use of surface-based measurements, and satellite-to-satellite intercomparisons. Numerous examples of such activities exist over the ten years of the JPPG. In this talk, examples of calibration/validation focused activities, accomplishments, and future plans will be presented. A particular focus will be on how the COVID-19 pandemic has affected field work planned for 2020 and 2021.  The JPPG subgroup also includes joint European-US studies of satellite results that integrate the results of both parties’ observational capabilities, and the status of those activities will be presented as well.