Remote Sensing of Complex Permittivity and Penetration Depth of Soils Using P-Band SAR Polarimetry

A P-band SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm)

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

BIOSAR 2010 - A SAR campaign in support to the BIOMASS mission

The ESA funded campaign BioSAR 2010 was carried out at the forestry test site Remningstorp in southern Sweden, in support to the BIOMASS satellite mission under study. Fully polarimetric SAR data were successfully acquired at L- and P-band using ONERA's multi-frequency system SETHI. In addition with other data types gathered, e.g. LiDAR and in-situ measurements, the compiled data set will be used for analyses and comparisons with biomass estimation results obtained at the same test site in the campaign BioSAR 2007, in which DLR's E-SAR made the SAR imaging. Detection of forest changes, robustness of biomass retrieval algorithms and long-term P-band coherence will be in focus as well as cross-validations between the two SAR sensors

Modelling PolSAR Scattering Signatures at Long Wavelengths of Glacier Ice Volumes

The crucial role of cryosphere for understanding the global climate change has been widely recognized in recent decades [1]. Glaciers and ice sheets are the main components of the cryosphere and constitute the basic reservoir of fresh water for high-latitudes and many densely populated areas at mid and low latitudes. The need of information on large scale and the inaccessibility of polar regions qualify synthetic aperture radar (SAR) sensors for glaciological applications. At long wavelengths (e.g. P- and L- band), SAR systems are capable to penetrate several tens of meters deep into the ice body. Consequently, they are sensitive to the glacier surface as well as to sub-surface ice structures. However, the complexity of the scattering mechanisms, occurring within the glacier ice volume, turns the interpretation of SAR scattering signatures into a challenge and large uncertainties remain in estimating reliably glacier accumulation rates, ice thickness, subsurface structures and discharge rates. In literature great attention has been given to model-based decomposition techniques of polarimetric SAR (PolSAR) data. The first model-based decomposition for glacier ice was proposed in [2] as an adaptation and extension of the well-known Freeman-Durden model [3]. Despite this approach was able to interpret many effects in the experimental data, it could not explain, for instance, co-polarization phase differences. The objective of this study is to develop a novel polarimetric model that attempts to explain PolSAR signatures of glacier ice. A new volume scattering component from a cloud of oriented particles will be presented. In particular, air and atmospheric gases inclusions, typically present in ice volumes [4], are modeled as oblate spheroidal particles, mainly horizontally oriented and embedded in a glacier ice background. Since the model has to account for an oriented ice volume, the anisotropic nature of the ice medium has to be incorporated. This phenomenon, neglected in [2], leads to different refraction indices, i.e. differential propagation velocities (phase differences) and losses of the electromagnetic wave along different polarizations [5]. Furthermore, the introduction of additional scattering components (e.g. from the glacier surface) will extend and complete the polarimetric model. For a first quality assessment, modeled polarimetric signatures are compared to airborne fully polarimetric SAR data at L- and P-band, collected over the Austfonna ice-cap, in Svalbard, Norway, by DLR’s E-SAR system within the ICESAR 2007 campaign

Correction of Ionosphere for InSAR by the Combination of Differential TEC Estimators

Low frequency spaceborne SAR configurations are favoured for global forest mapping applications and D-InSAR applications over natural terrain. Several missions have been scheduled to be launched / or proposed to be implemented in the next years: JAXA’s ALOS-II (L-band), NASA’s Destyni (L-band), DLR’s Tandem-L (L-band) and ESA’s BIOMASS (P-band) are some of them. A common challenge for all these missions is to control / compensate the disturbances induced by the ionosphere. At these lower frequencies the ionosphere effects several components of the SAR measurements performed: It delays the group velocity of the transmitting / receiving pulses, advances their phase(s) and rotates their polarisation state. Accordingly, it distorts not only intensity but also polarimetric, interferometric and polarimetric interferometric observation spaces. The total electron content (TEC) is the most decisive parameter in the characterisation of the ionosphere. It is defined as the integrated electron number density per unit volume along the direction of propagation. Most of the free electrons are distributed within a relatively narrow altitude range allowing modelling the ionosphere as a thin layer at a fixed altitude. In this case the ionosphere can be characterised by a 2-D scalar field of TEC [1], [2]. Depending now on the SAR configuration and its observation space different correction approaches are possible leading to a wide range of calibration algorithms. In this paper we propose a concept towards the generalisation of ionospheric calibration methodology by integrating a number of individual approaches / algorithms. In this sense, a novel generic correction schema based on a combined (and improved) estimation of the 2-D TEC field (or the associated differential TEC field in the interferometric case) from a set of individual data based TEC and/or TEC gradient estimates is introduced and discussed. As a special case a combined 2-D (differential) TEC field estimator based on (differential) TEC estimated from Faraday rotation measurements and (differential) TEC gradients obtained from the estimation of azimuth/range (differential) shifts is presented. Both observations are independent, allowing establishing an inverse problem for the (differential) TEC estimation. Geophysical knowledge as the anisotropic nature of the TEC distribution can be incorporated as a priori information in the “combined” (differential) TEC estimator. The performance of the proposed approach is tested using ALOS quad-pol interferometric data sets over several test sites in Alaska. The achieved estimates are characterised by a significantly improved performance: While the FR based estimator suffers from the random granular deviation pattern of TEC after conversion, the proposed combined estimator effectively is free of such artefacts. Emphasis is given in the role of polarisation in the TEC estimation procedure [3] and on the calibration of Pol-InSAR data. References [1] Franz J. Meyer and Jeremy Nicoll, “Prediction, detection, and correction of Faraday rotation in full-polarimetric L-band SAR data”, IEEE Trans. Geosci. And Remote Sensing, 46(10), Oct., 3076-3086, 2008 [2] Xiaoqing Pi, Anthony Freeman, Bruce Champman, Paul Rosen, and Zhenhong Li, “Imaging ionospheric inhomogeneities using spaceborne synthetic aperture radar”, Jour. of Geophysical Research, 116, A04303, 2011 [3] Jun Su Kim, Konstantinos Papathanassiou, Shaun Quegan and Neil Rogers, “Estimation and correction of scintillation effects on spaceborne P-band SAR images”, in Proceedings of IGARSS2012, 23-27. Jul., 201

Temporal Characteristics of P-band Tomographic Radar Backscatter of a Boreal Forest

Temporal variations in synthetic aperture radar (SAR) backscatter over forests are of concern for any SAR mission with the goal of estimating forest parameters from SAR data. In this article, a densely sampled, two-year long time series of P-band (420 to 450 MHz) boreal forest backscatter, acquired by a tower-based radar, is analyzed. The experimental setup provides time series data at multiple polarizations. Tomographic capabilities allow the separation of backscatter at different heights within the forest. Temporal variations of these multi-polarimetric, tomographic radar observations are characterized and quantified. The mechanisms studied are seasonal variations, effects of freezing conditions, diurnal variations, effects of wind and the effects of rainfall on backscatter. An emphasis is placed on upper-canopy backscatter, which has been shown to be a robust proxy for forest biomass. The canopy backscatter was most sensitive to freezing conditions but was more stable than ground-level backscatter and full-forest backscatter during non-frozen conditions. The analysis connects tree water transport mechanisms and P-band radar backscatter for the first time. The presented results are useful for designing boreal forest parameter estimation algorithms, using data from P-band SARs, that are robust to temporal variations in backscatter. The results also present new forest remote sensing opportunities using P-band radars

Parametric and non-parametric forest biomass estimation from PolInSAR data

Biomass estimation performance from model-based polarimetric SAR interferometry (PolInSAR) using generic parametric and non-parametric regression methods is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, estimating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estimation methods include multiple linear regression, support vector machine and random forest. The biomass estimation performance is evaluated on DLR's airborne SAR data at L- and P-bands over Krycklan Catchment, a boreal forest test site in Northern Sweden. The combination of polarimetric indicators and estimated structure information has improved the root mean square error (RMSE) of biomass estimation up to 28% at L-band and up to 46% at P-band. The cross-validated biomass RMSE was reduced to 20 tons/ha

Measurements of forest change using P-band SAR backscatter

Using data from the recent BioSAR 2007 and BioSAR 2010 campaigns, it has for the first time been possible to measure forest biomass change using P-band SAR data. Regression models based on backscatter change have been developed using reference data derived from high density laser scanning data. The models were evaluated for six areas with detailed in-situ measurements, for which the maximum biomass loss and growth was 30% and 20%, respectively. For the best model the coefficients of determination was 55-89%. This result suggests that not only clear cuts but also forest growth and thinning can be measured using P-band SAR backscatter

Spaceborne P-Band MIMO SAR for Planetary Applications

The Space Exploration Synthetic Aperture Radar (SESAR) is an advanced P-band beamforming radar instrument concept to enable a new class of observations suitable to meet Decadal Survey science goals for planetary exploration. The radar operates at full polarimetry and fine (meter scale) resolution, and achieves beam agility through programmable waveform generation and digital beamforming. The radar architecture employs a novel low power, lightweight design approach to meet stringent planetary instrument requirements. This instrument concept has the potential to provide unprecedented surface and near-subsurface measurements applicable to multiple Decadal Survey Science Goals