Shaun Quegan

The mid-latitude trough in the electron concentration of the ionospheric F-layer: a review of observations and modelling*

Abstract Experimental observations and theoretical modelling of the terrestrial mid-latitude trough are reviewed. The mid-latitude trough is considered as an F -layer phenomenon, and its relationships to the lightion trough in the topside ionosphere and to the plasmapause are discussed. The observed morphology of the mid-latitude trough is summarised. Recent evidence on plasma temperatures in the trough is examined. The physical processes that may be important in the trough region are listed. Large-scale computational models that include some of those processes are described and the results compared with observations. Deficiencies in the models and possible future developments are mentioned.

The BIOMASS mission — An ESA Earth Explorer candidate to measure the BIOMASS of the earth's forests

The European Space Agency (ESA) released a Call for Proposals for the next Earth Explorer Core Mission in March 2005, with the aim to select the 7th Earth Explorer (EE-7) mission for launch in the next decade. Twenty-four proposals were received and subject to scientific and technical assessment. Six candidate missions were selected and further investigated in the preliminary feasibility studies (Phase 0). One of these missions is BIOMASS, which has recently been selected to proceed to Phase-A. BIOMASS is a response to the urgent need for greatly improved mapping of global biomass and the lack of any current space systems capable of addressing this need.

Multitemporal ERS SAR analysis applied to forest mapping

Examination of the physical background underlying the ERS response of forest and analysis of time series of ERS data indicates that the greater temporal stability of forest compared with many other types of land cover presents a means of mapping forest area. The processing chain necessary to make such area estimations involves reconstruction of an optimal estimate of the backscattering coefficient at each pixel using temporal and spatial filtering so that classification rules derived from large scale averaging are applicable. The rationale behind the filtering strategy and the level of averaging needed is explained in terms of the observed multitemporal behavior of forest and nonforest areas, much of this analysis is generic and applicable to a wide range of situation in which significant information is carried by multitemporal features of the data. The choice of decision rules is based on the forest observations, with the added requirement for robustness. The performance of a classifier based only on change is assessed on a range of test sites in the UK, Finland, and Poland. Error sources in this classifier are identified, and the possibility of improving performance by including radiometric information in the mapping strategy is discussed. Brief discussions of how the classification is affected by the addition of coherence and how the processing chain would need to be modified for other forms of satellite data are included.

A unified algorithm for phase and cross-talk calibration of polarimetric data-theory and observations

A unified approach to phase and cross-talk calibration of polarimetric data which can be applied to calibrating scattering matrix data or to extraction of the descriptors of distributed targets is described. It relies on the scene being dominated by targets with uncorrelated like and cross-polarized backscattering coefficients, but provides cross-talk calibration of targets for which this is not true. The algorithm needs unsymmetrized data, but uses only quantities derived from the covariance matrix of large areas. It makes no assumptions about system reciprocity, permits ready interpretation of the terms in the calibration procedure, allows comparison of the relative magnitude of the system-induced mixing of terms in the observed covariance matrix, is noniterative, and produces indicators which allow testing of whether it meets its own underlying assumptions. The linear distortion model is shown to lead to an inconsistent system of equations; this inconsistency can be removed by introducing an extra parameter which has properties expected of system noise. The modulus of the copolarized correlation coefficient, which is important in polarimetric classification and as a phase descriptor, is shown to be invariant under all effects embodied in the linear distortion model. Calibration of the scattering matrix data is based on a minimum least squares principle. This suggests that current methods of symmetrization are not optimal. The same analysis shows that estimates of parameters needed to form an equivalent reciprocal system are also nonoptimal. The method is more general than the well-known van Zyl algorithm for cross-talk removal, and permits an analysis of the conditions under which the van Zyl algorithm will yield valid results. Correction of phase distortion induced by channel imbalance Is treated as an optional extra step relying on a known HH-VV phase difference in some region of the image. Results from the algorithm are discussed using scattering matrix data from the 1989 MAESTRO campaign. >

High-resolution measurements of scattering in wheat canopies-implications for crop parameter retrieval

Polarimetric X- and C-band measurements by the University of Sheffield ground-based synthetic aperture radar (GB-SAR) indoor system provide three-dimensional images of the scattering processes in wheat canopies, at resolutions of around a wavelength (3-6 cm). The scattering shows a pronounced layered structure, with strong returns from the soil and the flag leaves, and in some cases a second leaf layer. Differential attenuation at horizontal (H) and vertical (V) polarization, due to the predominantly vertical structure of the wheat stems, gives rise to marked effects. At both C and X bands, direct return from the canopy exceeds the soil return at large incidence angles for VV polarization, but is comparable to or less than the soil return in all other cases. At HV, the apparent ground return is probably due to a double-bounce mechanism, and volume scattering is never the dominant term. Direct sensing of the crop canopy is most effective at X band, VV, and large incidence angles, under which conditions the return is dominated by the flag leaf layer. Field measurements with the outdoor GB-SAR system suggest, however, that for sensitivity to biomass and reduced susceptibility to disturbances by rainfall, a two-channel C-band system operating at a medium range of incidence angles is preferred.

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.

Modeling microwave interactions with crops and comparison with ERS-2 SAR observations

A comprehensive multilayer second-order radiative transfer model, driven entirely by intensive field observations, is used to show that second order terms contribute at most 0.5 dB to the backscattering coefficient at all polarizations from wheat and barley throughout the growing season and 1 dB to the copolar response of oilseed rape. Under these circumstances, an equivalent integrable first-order model, with coefficients derived from full model runs, can be formulated. This allows the role of each of the plant components in attenuating and scattering the radar signal to be clarified, and provides a basis for quantitative comparison of observed ERS-2 backscatter values with model calculations, taking full account of measurement uncertainties. Discrepancies between the two suggest that effective attenuation through mature cereal crops is overestimated by the model. This appears to be either an intrinsic failure of the radiative transfer formulation or (more likely) due to an inadequate adaptation of the notion of crop coverage to the microwave case. Nonplanarity of leaves is an important source of model error for oilseed rape.

Quantitative estimation of tropical forest cover by SAR

The potential of spaceborne synthetic aperture radar (SAR) for monitoring tropical forest areas is assessed, using three ERS images from the Tapajos region of Amazonia gathered in 1992 and a single JERS-1 image of the same area acquired in 1993. The multitemporal ERS-1 data indicate that primary forest areas display a very stable radar backscattering coefficient (/spl sigma//sup 0/), while in some cases, disturbed areas (nonforest and regenerating forest) exhibit changes that appear to be associated with soil moisture variations. To counteract /spl sigma//sup 0/ distortions caused by topography, change detection based on ratios of intensity images (or differences of log images) provides a more useful discrimination approach than /spl sigma//sup 0/ variations in single images. Change detection techniques are compared, and their ability to classify primary and disturbed forest is quantitatively assessed, assuming that a land cover map inferred from a 1992 Landsat thematic mapper (TM) image is correct. Even in the best case, less than 50% of the disturbed forest region is detected in the ERS-1 images. This figure may be improved by more frequent image acquisition, but there are fundamental limitations in using C-band data since the effects of soil moisture changes on /spl sigma//sup 0/ are masked once even comparatively low levels of standing biomass are present. At the longer wavelength of JERS-1, much better discrimination is possible, but the correction of topographic distortions is likely to present problems.

Impact of land cover uncertainties on estimates of biospheric carbon fluxes

Large-scale bottom-up estimates of terrestrial carbon fluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite-derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO2 fluxes derived using the different 1 km resolution maps differ from estimates made using the reference map. The ranges of these differences are 254 gC m−2 a−1 in Gross Primary Production (GPP); 133 gC m−2 a−1 in Net Primary Production (NPP); and 43 gC m−2 a−1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of −15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m−2. The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.

Quantitative comparison of the performance of SAR segmentation algorithms

Methods to evaluate the performance of segmentation algorithms for synthetic aperture radar (SAR) images are developed, based on known properties of coherent speckle and a scene model in which areas of constant backscatter coefficient are separated by abrupt edges. Local and global measures of segmentation homogeneity are derived and applied to the outputs of two segmentation algorithms developed for SAR data, one based on iterative edge detection and segment growing, the other based on global maximum a posteriori (MAP) estimation using simulated annealing. The quantitative statistically based measures appear consistent with visual impressions of the relative quality of the segmentations produced by the two algorithms. On simulated data meeting algorithm assumptions, both algorithms performed well but MAP methods appeared visually and measurably better. On real data, MAP estimation was markedly the better method and retained performance comparable to that on simulated data, while the performance of the other algorithm deteriorated sharply. Improvements in the performance measures will require a more realistic scene model and techniques to recognize oversegmentation.