Sandrine Daniel

The TropiSAR Airborne Campaign in French Guiana: Objectives, Description, and Observed Temporal Behavior of the Backscatter Signal

The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne radar system SETHI. The main objective of this campaign was to collect data to support the Phase A of the 7th Earth Explorer candidate mission, BIOMASS. Several specific questions needed to be addressed to consolidate the mission concept following the Phase 0 studies, and the data collection strategy was constructed accordingly. More specifically, a tropical forest data set was required in order to provide test data for the evaluation of the foreseen inversion algorithms and data products. The paper provides a description of the resulting data set which is now available through the European Space Agency website under the airborne campaign link. First results from the TropiSAR database analysis are presented with two in-depth analyses about both the temporal radiometric variation and temporal coherence at P-band. The temporal variations of the backscatter values are less than 0.5 dB throughout the campaign, and the coherence values are observed to stay high even after 22 days. These results are essential for the BIOMASS mission. The observed temporal stability of the backscatter is a good indicator of the expected robustness of the biomass estimation in tropical forests, from cross-polarized backscatter values as regarding environmental changes such as soil moisture. The high temporal coherence observed after a 22-day period is a prerequisite for SAR Polarimetric Interferometry and Tomographic applications in a single satellite configuration. The conclusion then summarizes the paper and identifies the next steps in the analysis.

TropiSAR: Exploring the temporal behavior of P-Band SAR data

The TropiSAR campaign has been conducted in August 2009 in French Guiana with the ONERA airborne system SETHI. The main objective of this campaign was to collect data to support the Phase A of the Earth Explorer candidate mission, Biomass. Several specific questions need to be addressed to answer the recommendations of the ESAC group and the data collection strategy has been constructed accordingly. The first part of the paper lists these specific questions. We then describe the selected test sites, followed by a summary on the radar instrument and the radar configuration (geometry and waveform). The data acquisition plan is provided and the temporal behaviour of the P-Band data is explored.

Temporal decorrelation analysis at P band over tropical forest

The TropiSAR campaign was conducted in French Guiana in the summer 2009 in the framework of the Phase A studies pertaining to the BIOMASS mission, one of the three for Earth Explorer candidates. Its main objectives were the evaluation of P-Band radar imaging over tropical forests for biomass and forest height estimation. Over two main forest sites, Paracou and Nouragues, a temporal decorrelation analysis is done in order to evaluate the performance of the PolInSAR technique in a single-satellite configuration. The interferometric coherence is observed to stay high after 22 days over the Paracou site, while presenting larger decorrelation over the Nouragues area, a more rugged area.

P-band SAR study of tropical forest in French Guiana

In August 2009, the TropiSAR campaign was conducted in French Guiana with the ONERA airborne system SETHI in order to support the Phase A of the Earth Explorer candidate mission, BIOMASS. Several SAR data acquisitions at P-band are now available for analysis over tropical forest. This paper presents one of the four acquisition sites, Paracou and two related studies performed over this dataset. The first interrogation focuses on the radiometric stability at P-band of the forest backscatter. This stability is an essential point if the backscatter is expected to be used for forest biomass estimation. Moreover, the compatibility of the current BIOMASS mission design, relying on repeat pass interferometry for forest height retrieval, to tropical forest and the related temporal decorrelation is then explored.

Surface Parameter Estimation Over Periodic Surfaces Using a Time-Frequency Approach

The aim of this paper is to analyze the feasibility of soil moisture retrieval over rough periodic surfaces. For this kind of fields, the Bragg phenomenon effect appears and the inversion algorithms are shown to be no more valid. Using a time-frequency approach and a new random periodic surface scattering model, a polarimetric analysis allows to show that the use of alpha1 polarimetric parameter is important for the inversion since it is quasi insensible to this phenomenon. Thus, the alpha1 inversion method is proposed that provides some encouraging results.

Agricultural vegetation classification with SVM and polarimetric SAR data

Polarimetric SAR data at L-band are known to be particularly well adapted for estimating moisture content and roughness. However, many agricultural fields are generally covered by a short vegetation layer that hampers this analysis. In fact, many applications of surface parameter retrieval methods using polarimetric SAR data over agricultural sites revealed that parameters are underestimated over most of the fields covered by short vegetation (e.g. grass, clovers, winter wheat). This bias is due to the electromagnetic contribution of the vegetation which significantly modifies the polarimetric response. An identification of different kind of vegetation is necessary in order to determine the feasibility to estimate soil moisture. The AgriSAR campaign, Agricultural Bio-/Geophysical Retrievals from Frequent Repeat SAR and Optical Imaging, was conducted for ESA in 2006 in order to study the agricultural vegetation. The multi-temporal datasets were acquired with the DLRs E-SAR sensor in Görmin (Germany). From this campaign, many ground measurements were obtained: Leaf Area Index (LAI), wet and dry biomass and soil moisture. Thus, using all information, eight agricultural vegetation classes could be characterized independently of soil moisture. This paper presents this identification necessary to elaborate an original mapping technique allowing localizing agricultural fields having a vegetation layer. A classification based on the support vector machine (SVM) and on the analysis of polarimetric parameter behavior is developed using multi-temporal images over fields covered by vegetation. The obtained vegetation maps allow the analysis of the temporal evolution of plants. This classification has high product and user accuracy which are presented. The technique is shown to perform well over the AgriSAR dataset.

Short vegetation influence on surface parameter estimations

Several inversion models [1] estimate roughness and soil moisture of bare surfaces. In the case of vegetated areas, the soil moisture is underestimated by inversion algorithms due to the sensitivity of the polarimetric scattering to the vegetation. In order to estimate soil moisture under vegetation, this paper analyzes the influence of scattering through short plant layer at L- band for which the scattering is very sensitive to soil parameters. The potential to classify the short vegetation influence using the so-called Single and Double bounce Eigenvalue Relative Difference (SDERD) and the Luneburg randomness parameter pr [2], is addressed so as to observe. Moreover, the short vegetation backscattering is empirically modeled basing on the radiative transfer first order method [3]. The data used are at L-band from the airborne E-SAR system of the German Aerospace Center (DLR) and the testsite is the Ailing city in Germany.