Malcolm Davidson

On the characterization of agricultural soil roughness for radar remote sensing studies

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation length l. However, soil moisture retrieval studies based on these models have yielded inconsistent results, not so much because of the failure of the models themselves, but because of the complexity of natural surfaces and the difficulty in estimating appropriate input roughness parameters. In this paper, the authors address the issue of soil roughness characterization in the case of agricultural fields having different tillage (roughness) states by making use of an extensive multisite database of surface profiles collected using a novel laser profiler capable of recording profiles up to 25 m long. Using this dataset, the range of RMS height and correlation values associated with each agricultural roughness state is estimated, and the dependence of these estimates on profile length is investigated. The results show that at spatial scales equivalent to those of the SAR resolution cell, agricultural surface roughness characteristics are well described by the superposition of a single scale process related to the tillage state with a multiscale random fractal process related to field topography.

Large-Scale Mapping of Boreal Forest in SIBERIA using ERS Tandem Coherence and JERS Backscatter Data

Siberias boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km2 in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m3/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.

Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops

This paper investigates the potential of multi-temporal C- and L-band SAR data, acquired within a short revisiting time (1-2 weeks), to map temporal changes of surface soil moisture content (mv) underneath agricultural crops. The analysed data consist of a new ground and SAR data set acquired on a weekly basis from late April to early August 2006 over the DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site (Northern Germany) during the European Space Agency 2006 AgriSAR campaign. The paper firstly investigates the main scattering mechanisms characterizing the interaction between the SAR signal and crops, such as winter wheat and rape. Then, the relationship between backscatter and soil moisture content temporal changes as a function of different SAR bands and polarizations is studied. Observations indicate that rationing of the multi-temporal radar backscatter can be a simple and effective way to decouple the effect of vegetation and surface roughness from the effect of soil moisture changes, when volume scattering is not dominant. The study also assesses to which extent changes in the incidence angle between subsequent radar acquisitions may affect the radar sensitivity to soil moisture content. Finally, an algorithm based on the change detection technique retrieving superficial soil moisture content is proposed and assessed both on simulated and experimental data. Results indicate that for crops relatively insensitive to volume scattering in the vegetation canopy (as for instance winter wheat at C-band or winter rape and winter wheat at L-band), mv can be retrieved during the whole growing season, with accuracies ranging between 5% and 6% [m3/m3]. We also show that low incidence angles (e.g., 20-35 ) and HH polarization are generally better suited to mv retrieval than VV polarization and higher incidence angles.

Parameterization of tillage-induced single-scale soil roughness from 4-m profiles

Soil roughness greatly affects the scattering process of microwaves to the soil surface. Previous studies showed that the values of roughness parameters increase asymptotically with increasing profile length. In this paper, 25-m profiles are used to study the influence of profile length on the roughness parameters and on the shape of the autocorrelation function. It is further investigated whether correct soil roughness parameters, as obtained from long surface roughness profiles, can be determined from 4-m-long profiles. Therefore, the extrapolation of an empirical relationship between roughness parameters and profile length is investigated, for three different roughness classes. The technique yields parameter values which are comparable to the 25-m roughness parameters.

On current limits of soil moisture retrieval from ERS-SAR data

Assesses the feasibility of retrieving soil moisture content over smooth bare-soil fields using European Remote Sensing synthetic aperture radar (ERS-SAR) data. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration is assessed using appropriately trained neural networks. Three sources of error affecting soil moisture retrieval (inversion, measurement, and model errors) are identified, and their relative influence on retrieval performance is assessed using synthetic datasets as well as a large pan-European database of ground and ERS-1 and ERS-2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS configuration, even for the restricted roughness range considered.

Crop Classification Using Short-Revisit Multitemporal SAR Data

Classification of crops and other land cover types is an important application of both optical/infrared and SAR satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. An airborne SAR data set with a short revisit time acquired by the German ESAR system during the ESA-campaign, AgriSAR 2006, has been used to assess the performance of different polarization modes for crop classification. Both C-and L-band SAR data were acquired over the Demmin agricultural test site in North Eastern Germany on a weekly basis during the growing season. Single-and dual-polarization, and fully polarimetric data have been used in the analysis (fully polarimetric data were only available at L-band). The main results of the analysis are, that multitemporal acquisitions are very important for single-and dual-polarization modes, and that cross-polarized backscatter produces the best results, with errors down to 3%-6% at the two frequencies. There is a trade-off between the polarimetric information and the multitemporal information, where the best overall results are obtained using the multitemporal information. If only a few acquisitions are available, the polarimetric mode may perform better than the single-and dual polarization modes.

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.

Using the Interferometric Capabilities of the ESA CryoSat-2 Mission to Improve the Accuracy of Sea Ice Freeboard Retrievals

A significant source of error in the retrieval of sea ice freeboard from pulse-limited radar altimeters arises when scattering from off-nadir leads dominates the power echo causing the onboard tracker to “snag” and overestimate the two-way travel time. This range overestimate translates into an ocean elevation underestimate relative to the ice surface and an overestimate of the sea ice freeboard. We demonstrate using interferometric CryoSat-2 data that it is possible to infer the across-track angle of return to off-nadir leads and their location in the CryoSat-2 footprint and hence correct for the associated range error for the first time. It is found that specular scattering from leads can dominate the radar echo some 1530 m off nadir. Over the region studied, the mean ocean elevation bias is closely associated with the “pulse peakiness” (PP) parameter used for identifying specular waveforms. Considering only the most specular waveforms, the elevation bias was measured to be -1.21 ±0.93 cm. However, lowering the PP threshold includes lower power waveforms originating from higher off-nadir angles, and the elevation bias becomes -4.06 ±1.66 cm. Unaccounted for, these biases represent an ~ 10-40-cm overestimate in ice thickness. Despite the relatively large error on the mean bias, correcting for off-nadir ranging contributes only a small amount to the elevation uncertainty when combined with range and orbit determination uncertainties. We found that making use of CryoSat-2s interferometric mode over sea ice ultimately decreases the uncertainty on the area-averaged ocean elevation by allowing the inclusion of more waveforms in the analysis.

Accuracy assessment of a large-scale forest cover map of central Siberia from synthetic aperture radar

Russias boreal forests host 11% of the worlds live forest biomass. They play a critical role in Russias economy and in stabilizing the global climate. The boreal forests of central and western Siberia represent the largest unbroken tracts of forest in the world. The European Commission funded SIBERIA project aimed at producing a forest map covering an area of 1.2 million square kilometres. Three synthetic aperture radars (SAR) on board the European remote sensing satellites ERS-1 and ERS-2 and the Japanese Earth resources satellite JERS-1 were used to collect remote sensing data. Radar is the only sensor capable of penetrating cloud cover and imaging at night. An adaptive, model-based, contextual classification to derive ranked total growing stock volume classes suitable for large-scale mapping is described. The accuracy assessment of the Siberian forest cover map is presented. The weighted coefficient of agreement κw is calculated to quantify the agreement between the classified map and the reference data. First, the classified map is compared with Russian forest inventory data (κw = 0.72). The inherent uncertainty in the forest inventory data is simulated by allowing for fuzziness. The effect of uncertainty on the unweighted coefficient of agreement κ is stronger than that on the weighted coefficient of agreement κw. Second, the map is compared with a more reliable, independent posterior ground survey by Russian forestry experts (κw = 0.94). The follow-on project SIBERIA-II started in January 2002 and is striving to develop multisensor concepts for greenhouse gas accounting (www.siberia2.uni-jena.de).

SENTINEL-1 SYSTEM CAPABILITIES AND APPLICATIONS

The paper provides an overview of the Copernicus Sentinel-1 system capabilities and applications. In particular, the characteristics of the Sentinel-1 SAR imaging modes and their key performance parameters are described. In addition, the Sentinel-1 SAR interferometry (InSAR) capabilities, especially for TOPS InSAR and the strategy for maintaining the orbital baseline as well as the requirements for TOPS image co-registration are discussed.