Estimation of Forest Biomass and Faraday Rotation using Ultra High Frequency Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) data in the Ultra High Frequency (UHF; 300 MHz – 3 GHz)) band have been shown to be strongly dependent of forest biomass, which is a poorly estimated variable in the global carbon cycle. In this thesis UHF-band SAR data from the fairly flat hemiboreal test site Remningstorp in southern Sweden were analysed. The data were collected on several occasions with different moisture conditions during the spring of 2007. Regression models for biomass estimation on stand level (0.5-9 ha) were developed for each date on which SAR data were acquired. For L-band (centre frequency 1.3 GHz) the best estimation model was based on HV-polarized backscatter, giving a root mean squared error (rmse) between 31% and 46% of the mean biomass. For P-band (centre frequency 340 MHz), regression models including HH, HV or HH and HV backscatter gave an rmse between 18% and 27%. Little or no saturation effects were observed up to 290 t/ha for P-band. A model based on physical-optics has been developed and was used to predict HH-polarized SAR data with frequencies from 20 MHz to 500 MHz from a set of vertical trunks standing on an undulating ground surface. The model shows that ground topography is a critical issue in SAR imaging for these frequencies. A regression model for biomass estimation which includes a correction for ground slope was developed using multi-polarized P-band SAR data from Remningstorp as well as from the boreal test site Krycklan in northern Sweden. The latter test site has pronounced topographic variability. It was shown that the model was able to partly compensate for moisture variability, and that the model gave an rmse of 22-33% when trained using data from Krycklan and evaluated using data from Remningstorp. Regression modelling based on P-band backscatter was also used to estimate biomass change using data acquired in Remningstorp during the spring 2007 and during the fall 2010. The results show that biomass change can be measured with an rmse of about 15% or 20 tons/ha. This suggests that not only deforestation, but also forest growth and degradation (e.g. thinning) can be measured using P-band SAR data. The thesis also includes result on Faraday rotation, which is an ionospheric effect which can have a significant impact on spaceborne UHF-band SAR images. Faraday rotation angles are estimated in spaceborne L-band SAR data. Estimates based on distributed targets and calibration targets with high signal to clutter ratios are found to be in very good agreement. Moreover, a strong correlation with independent measurements of Total Electron Content is found, further validating the estimates

The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

International audienceIn 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space

The primary objective of the European Space Agency's 7th Earth Explorer mission, BIOMASS, is to determine the worldwide distribution of forest above-ground biomass (AGB) in order to reduce the major uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere, including carbon fluxes associated with Land Use Change, forest degradation and forest regrowth. To meet this objective it will carry, for the first time in space, a fully polarimetric P-band synthetic aperture radar (SAR). Three main products will be provided: global maps of both AGB and forest height, with a spatial resolution of 200 m, and maps of severe forest disturbance at 50 m resolution (where “global” is to be understood as subject to Space Object tracking radar restrictions). After launch in 2022, there will be a 3-month commissioning phase, followed by a 14-month phase during which there will be global coverage by SAR tomography. In the succeeding interferometric phase, global polarimetric interferometry Pol-InSAR coverage will be achieved every 7 months up to the end of the 5-year mission. Both Pol-InSAR and TomoSAR will be used to eliminate scattering from the ground (both direct and double bounce backscatter) in forests. In dense tropical forests AGB can then be estimated from the remaining volume scattering using non-linear inversion of a backscattering model. Airborne campaigns in the tropics also indicate that AGB is highly correlated with the backscatter from around 30 m above the ground, as measured by tomography. In contrast, double bounce scattering appears to carry important information about the AGB of boreal forests, so ground cancellation may not be appropriate and the best approach for such forests remains to be finalized. Several methods to exploit these new data in carbon cycle calculations have already been demonstrated. In addition, major mutual gains will be made by combining BIOMASS data with data from other missions that will measure forest biomass, structure, height and change, including the NASA Global Ecosystem Dynamics Investigation lidar deployed on the International Space Station after its launch in December 2018, and the NASA-ISRO NISAR L- and S-band SAR, due for launch in 2022. More generally, space-based measurements of biomass are a core component of a carbon cycle observation and modelling strategy developed by the Group on Earth Observations. Secondary objectives of the mission include imaging of sub-surface geological structures in arid environments, generation of a true Digital Terrain Model without biases caused by forest cover, and measurement of glacier and icesheet velocities. In addition, the operations needed for ionospheric correction of the data will allow very sensitive estimates of ionospheric Total Electron Content and its changes along the dawn-dusk orbit of the mission

Regression-Based Retrieval of Boreal Forest Biomass in Sloping Terrain using P-band SAR Backscatter Intensity Data

A new biomass retrieval model for boreal forest using polarimetric P-band synthetic aperture radar (SAR) backscatter is presented. The model is based on two main SAR quantities: the HV backscatter and the HH/VV backscatter ratio. It also includes a topographic correction based on the ground slope. The model is developed from analysis of stand-wise data from two airborne P-band SAR campaigns: BioSAR 2007 (test site: Remningstorp, southern Sweden, biomass range: 10-287 tons/ha, slope range: 0-4 degrees) and BioSAR 2008 (test site: Krycklan, northern Sweden, biomass range: 8-257 tons/ha, slope range: 0-19 degrees). The new model is compared to five other models in a set of tests to evaluate its performance in different conditions. All models are first tested on data sets from Remningstorp with different moisture conditions, acquired during three periods in the spring of 2007. Thereafter, the models are tested in topographic terrain using SAR data acquired for different flight headings in Krycklan. The models are also evaluated across sites, i.e., training on one site followed by validation on the other site. Using the new model with parameters estimated on Krycklan data, biomass in Remningstorp is retrieved with RMSE of 40-59 tons/ha, or 22-33% of the mean biomass, which is lower compared to the other models. In the inverse scenario, the examined site is not well represented in the training data set, and the results are therefore not conclusive

The BIOMASS level 2 prototype processor : design and experimental results of above-ground biomass estimation

BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements

The Interaction Between Faraday Rotation and System Effects in Synthetic Aperture Radar Measurements of Backscatter and Biomass

For long-wavelength space-based radars, such as the P-band radar on the recently selected European Space Agency BIOMASS mission, system distortions (crosstalk and channel imbalance), Faraday rotation, and system noise all combine to degrade the measurements. A first-order analysis of these effects on the measurements of the polarimetric scattering matrix is used to derive differentiable expressions for the errors in the polarimetric backscattering coefficients in the presence of Faraday rotation. Both the amplitudes and phases of the distortion terms are shown to be important in determining the errors and their maximum values. Exact simulations confirm the accuracy and predictions of the first-order analysis. Under an assumed power-law relation between σhv and the biomass, the system distortions and noise are converted into biomass estimation errors, and it is shown that the magnitude of the deviation of the channel imbalance from unity must be 4-5 dB less than the crosstalk, or it will dominate the error in the biomass. For uncalibrated data and midrange values of biomass, the crosstalk must be less than -24 dB if the maximum possible error in the biomass is to be within 20% of its true value. A less stringent condition applies if the amplitudes and phases of the distortion terms are considered random since errors near the maximum possible are very unlikely. For lower values of the biomass, the noise becomes increasingly important because the σhv signal-to-noise ratio is smaller

Airborne S-Band SAR for forest biophysical retrieval in temperate mixed forests of the UK

Radar backscatter from forest canopies is related to forest cover, canopy structure and aboveground biomass (AGB). The S-band frequency (3.1–3.3 GHz) lies between the longer L-band (1–2 GHz) and the shorter C-band (5–6 GHz) and has been insufficiently studied for forest applications due to limited data availability. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest biophysical properties. To understand the scattering mechanisms in forest canopies at S-band the Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model was used. S-band backscatter was found to have high sensitivity to the forest canopy characteristics across all polarisations and incidence angles. This sensitivity originates from ground/trunk interaction as the dominant scattering mechanism related to broadleaved species for co-polarised mode and specific incidence angles. The study was carried out in the temperate mixed forest at Savernake Forest and Wytham Woods in southern England, where airborne S-band SAR imagery and field data are available from the recent AirSAR campaign. Field data from the test sites revealed wide ranges of forest parameters, including average canopy height (6–23 m), diameter at breast-height (7–42 cm), basal area (0.2–56 m2/ha), stem density (20–350 trees/ha) and woody biomass density (31–520 t/ha). S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest AGB with least error between 90.63 and 99.39 t/ha and coefficient of determination (r2) between 0.42 and 0.47 for the co-polarised channel at 0.25 ha resolution. The conclusion is that S-band SAR data such as from NovaSAR-S is suitable for monitoring forest aboveground biomass less than 100 t/ha at 25 m resolution in low to medium incidence angle rang

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R2CV = 0.67 and RMSECV = 0.88 m2 m−2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments.EEA Hilario AscasubiFil: Caballero, Gabriel. Technological University of Uruguay (UTEC). Agri-Environmental Engineering; UruguayFil: Caballero, Gabriel. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; EspañaFil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Berger, Katja. Mantle Labs GmbH; AustriaFil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); Españ