Relationship between green leaf biomass volumetric density and ERS-2 SAR backscatter of four vegetation formations in the semi-arid zone of Israel

Relationships between ERS-2 SAR backscatter and the biophysical properties of four Mediterranean vegetation formations (forest, shrubs, dwarf shrubs and herbaceous vegetation) were assessed. Low correlation was found between ERS-2 SAR backscatter and both aboveground biomass and LAI. However, significantly higher correlation (r =0.92) was found between ERS-2 SAR backscatter and a new index of Green leaf biomass Volumetric Density (GVD). These results stress the dominant influence of leaves in the uppermost part of the vegetation layer on ERS-2 SAR backscatter.     

Orbital effects on ERS-1 SAR temporal backscatter profiles of agricultural crops

Multi-temporal radar backscatter characteristics of crops and their underlying soils were analysed for an agricultural area in south-western Ontario, Canada using nine dates of ERS-1 SAR imagery acquired during the 1993 growing season. From the calibrated data, SAR temporal backscatter profiles were generated for each crop type. The results indicate that small changes in incidence-angle can have strong impacts on radar backscatter. Thus, attention must be given to local incidence-angle effects when using ERS-1 SAR data,especially when comparing backscatter coefficients of the same area from different scenes or different areas within the same scene.     

Evaluation of JERS-1, ERS-1 and Almaz SAR backscatter for rubber and oil palm stands in West Malaysia

While many reports have been published on radar backscatter characteristics of coniferous and deciduous forests, little work appears to have been done on investigating the backscatter properties of palm trees. In this study, Japanese JERS-1 L _HH band, European ERS-1 C _VV band and Russian Almaz-1B S _HH band SAR data have been acquired over parts of Kedah and Penang states in West Malaysia in order to investigate the radar backscatter properties for oil palms and rubber trees for each of these sensors.

Results show that the radar backscatter for the deciduous rubber trees, for both JERS-1 and ERS-1, appear to behave in accordance with what has been reported earlier for coniferous and deciduous trees, that is, scattering on trunks, branches and twigs at L-band and scattering in the canopy at C-band. The JERS-1 backscatter shows limited correlation with the rubber growth while no relation is found in the ERS-1 data.

Oil palms with their characteristic structures affect the radar signal differently compared to the situation for rubber trees. Scattering in the large crown is the dominating backscatter mechanism in both the JERS-1 and ERS-1 data. Leaf area index is correlated closest to the backscatter intensity at both bands.

Results from the investigation of the Almaz S-band data are rather discouraging, contradicting earlier more positive reports on the usefulness of the sensor. In this study, the forest types and their intermediate growing stages were found to be virtually indistinguishable, including the clear felled areas. These results should however not be attributed to S-band or Almaz data in general, but rather to this particular data set. It is obvious that the quality of Almaz data varies significantly.     

Comparison of annual changes in winter ERS-1 SAR images and glacier mass balance of Slakbreen,Svalbard

The Arctic glaciers are sensitive to climate change, and glacier mass balance is used as an indicator of climate change. However, few mass balance observations are available from the Arctic region. Winter ERS-1 SAR images of the Arctic glacier Slakbreen (78degreesN, 16degrees 30'E) on Svalbard were analysed to investigate a possible relation between SAR backscatter and temporal variations in glacier mass balance. A winter ERS-1 SAR image acquired in 1993 after a summer with large ablation was compared with a winter ERS-1 SAR image acquired after the following summer with low ablation. Changes in mass balance from one year to another were difficult to detect using SAR backscatter data only. Comparison of ground-penetrating radar and SAR data showed that the SAR data contain a signal of the near-surface glacier properties. SAR data were interpreted to reflect variations in accumulation and ablation integrated over several years.     

Relationships between radar backscatter and accumulation rates on the Greenland ice sheet

C-band radar backscatter generally seems to be inversely related to accumulation rate across Greenland's dry snow zone. A coupled snow metamorphosis-backscatter model was constructed in order to investigate the observation. Accumulation rate and mean annual temperature were the coupled-model inputs. In a local area where accumulation rate is known very accurately the model predicts the backscatter level along several transects within the calibration uncertainties of the ERS-1 SAR data. The analysis was applied to the remainder of the dry snow zone. The model showed that both accumulation rate and temperature can modulate surface backscatter across Greenland's dry snow zone with accumulation rate being the primary influence.     

Short contributions

Studies of ERS-1 synthetic aperture radar (SAR) imagery have shown that fire scars in Alaskan forests are significantly brighter (3–6 dB) than surrounding unburned forest. The signature varies seasonally and changes as vegetation re-establishes on the site over longer time periods (>5years). Additionally, it is known that soil water content typically increases following forest fires due to changes in evapotranspiration rates and melting of the permafrost.

The objective of this study was to understand the relation between soil water content and the ERS-1 SAR signature at fire-disturbed sites. To accomplish this objective, we compared soil water in six burned black spruce (Picea mariana (Mill.) B.S.P.) forest sites in interior Alaska to ERS-1 SAR backscalter measurements. The six sites are of various age since burn. Soil water was periodically measured at each site during the summer of 1992 and at one site in 1993 and 1994 when the ERS-1 imaging radar was scheduled to pass overhead. Results indicate that a positive linear relation exists between soil water content and the SAR backscatter coefficient in young burns ( < ～4years). Older burns do not show this relation, a result of vegetation establishment following the burn. This interaction between soil moisture condition and ERS-1 SAR backscatter shows great potential for measuring soil water content and monitoring seasonal variations in soil water content in black spruce sites recently disturbed by wildfire.     

Investigating the potential for soil moisture and surface roughness monitoring in drylands using ERS SAR data

Multitemporal ERS-1 and ERS-2 SAR data were acquired for northern Jordan between 1995 and 1997 to investigate changes in the backscatter coefficients of a range of typical desert land surfaces. The changes in backscatter found were ascribed to variations in surface soil moisture, and changes in surface roughness caused by a range of natural and anthropogenic factors. Data collected from monitored sites were input into the Integral Equation Model (IEM). The model outputs were strongly correlated with observed backscatter coefficients (r ²=0.84). The results show that the successful monitoring of soil moisture in these environments is strongly dependent on the surface roughness. On surfaces with RMS height 0.5 cm, the sensitivity of the backscatter coefficient to changes in surface microtopography did not allow accurate soil moisture estimation. Microtopographic change on rougher surfaces has less influence on the backscatter coefficient, and the probability of soil moisture estimation from SAR imagery is greater. These results indicate that knowledge of the surface conditions (both in terms of surface roughness and geomorphology) is essential for accurate soil moisture monitoring, whether in a research or operational context. The potential benefits of these findings are discussed in the context of the Jordan Badia Research and Development Project.     

Estimating forest structure in wetlands using multitemporal SAR

Data from 202 forest plots on the Roanoke River floodplain, North Carolina were used to assess the capabilities of multitemporal radar imagery for estimating biophysical characteristics of forested wetlands. The research was designed to determine the potential for using widely available data from the current set of satellite-borne synthetic aperture radar (SAR) sensors to study forests over broad geographic areas and complex environmental gradients. The SAR data set included 11 Radarsat scenes, 2 ERS-1 images, and 1 JERS-1 scene. Empirical analyses were stratified by flood status such that sites were compared only if they exhibited common flooding characteristics. In general, the results indicate that forest properties are more accurately estimated using data from flooded areas, probably because variations in surface conditions are minimized where there is a continuous surface of standing water. Estimations yielded root mean square errors (RMSEs) for validation data around 10 m²/ha for basal area (BA), and less than 3 m for canopy height. The r² values generally exceeded .65 for BA, with the best predictions coming from sample sites for which both nonflooded and flooded SAR scenes were available. The addition of early spring normalized difference vegetation index (NDVI) values from Landsat Thematic Mapper (Landsat TM) improved model predictions for BA in forests where BA levels were <55 m²/ha. Further analyses indicated a very limited sensitivity of the individual SAR scenes to differences in forest composition, although soil properties in nonflooded areas exerted a weak but nevertheless important influence on backscatter.     

Agricultural crop condition monitoring using airborne C-band synthetic aperture radar in southern Alberta

Applications of airborne C-band synthetic aperture radar imagery for determining variations in agricultural crop characteristics were investigated at a test site in southern Alberta, Canada. Synthetic aperture radar (SAR) imagery and ground-based crop characteristics data were acquired on 19–20 July 1994 for wheat, canola, beans, peas, and wheat + alfalfa cultivated under a variety of irrigation conditions. The results indicate that the statistically significant relationships that were derived between the ground-based data and SAR imagery are a function of crop type, crop condition parameter, and image processing procedures, and that crop characteristics such as leaf area index and plant height are negatively correlated with radar backscatter.     

The detection and mapping of Alaskan wildfires using a spaceborne imaging radar system

The study presented here focuses on using a spaceborne imaging radar, ERS-1, for mapping and estimating areal extent of fires which occurred in the interior region of Alaska. Fire scars are typically 3 to 6 dB brighter than adjacent unburned forests in the ERS-1 imagery. The enhanced backscatter from burned areas was found to be a result of high soil moisture and exposed rough ground surfaces. Fire scars from 1979 to 1992 are viewed in seasonal ERS-1 synthetic aperture radar (SAR) data obtained from 1991 to 1994. Three circumstances which influence the detectability of fire scars in the ERS-1 imagery are identified and examined; seasonality of fire scar appearances, fires occurring in mountainous regions, and fires occurring in wetland areas. Area estimates of the burned regions in the ERS-1 imagery are calculated through the use of a Geographic Information System (GIS) database. The results of this analysis are compared to fire records maintained by the Alaska Fire Service (AFS) and to estimates obtained through a similar study using the Advanced Very High Resolution Radiometer (AVHRR) sensor.     

Use of multitemporal ERS-2 SAR images for identification of burned scars in south-east Asian tropical rainforest

Evaluation of an area severely affected by fires in 1998 using a multitemporal series of ERS-2 Synthetic Aperture Radar (SAR) images showed that fire induced changes of the vegetation cover strongly affected C-band radar backscatter. We investigated the changes in radar backscatter over a period of ten months in areas of interest that represented different land-cover types at a study site in East Kalimantan, Indonesia. The impact of fire was found to cause a strong decrease in backscatter (2-5 dB) for all land-cover classes while areas not affected by fire showed only slight variations in backscatter (maximum 0.5 dB). Ground and aerial evidence suggests that the marked decrease in backscatter can be attributed to the removal of the vegetation cover and subsequently higher contribution of backscatter from dry soil. After the onset of rain the radar backscatter increased to 5.5 dB in areas severely affected by fire while in unburned forests it returned to values similar to those before the drought. Burned scars could be identified visually in multitemporal principal component analysis-enhanced ERS SAR colour composites.     

干涉雷达永久散射体识别方法的对比分析

基于永久散射体的干涉测量技术可以克服卫星合成孔径雷达应用于区域地表形变探测所面临的时空失相关和大气影响.永久散射体的有效识别是干涉系统中的一个关键环节.本文对永久散射体的两种识别方法, 即相关系数阈值法和振幅离差指数阈值法进行了理论上的分析和实验上的对比.以上海市局部地区作为研究对象, 采用26幅欧洲空间局的ERS-1/2卫星C波段SAR影像为源数据, 使用两种识别方法分别进行了永久散射体的提取, 结果表明, 振幅离差指数方法具有更为稳健的抗噪能力.     

The Bothnian Experiment in Preparation for ERS-1, 1988 BEPERS-88) — an overview

Abstract

BEPERS-88 was an extensive field campaign on the use of Synthetic Aperture Radar (SAR) in sea ice remote sensing in the Baltic Sea. This experiment was performed in order to study the possibilities of using the ERS-1 satellite SAR (and radar altimeter) in connection with the brackish ice in the Baltic Sea. The Canada Centre for Remote Sensing CV-580 C/X-band SAR was flown and an extensive validation programme was carried out. The data have been used for SAR image analysis, backscatter investigations, geophysical validation of SAR over sea ice, and evaluation of the potentials of SAR in operational ice information services. The results indicate that SAR can be used to discriminate between ice and open water, classify ice types into thrcc categories, quantify ice ridging intensity, and determine the ice drift. As an operational tool SAR is expected to be an excellent complement to NOAA imagery and ground truth.     

Patterns of irrigated rice growth and malaria vector breeding in Mali using multi-temporal ERS-2 synthetic aperture radar

We explored the use of the European Remote Sensing Satellite 2 Synthetic Aperture Radar (ERS-2 SAR) to trace the development of rice plants in an irrigated area near Niono, Mali and relate that to the density of anopheline mosquitoes, especially An. gambiae. This is important because such mosquitoes are the major vectors of malaria in sub-Saharan Africa, and their development is often coupled to the cycle of rice development. We collected larval samples, mapped rice fields using GPS and recorded rice growth stages simultaneously with eight ERS-2 SAR acquisitions. We were able to discriminate among rice growth stages using ERS-2 SAR backscatter data, especially among the early stages of rice growth, which produce the largest numbers of larvae. We could also distinguish between basins that produced high and low numbers of anophelines within the stage of peak production. After the peak, larval numbers dropped as rice plants grew taller and thicker, reducing the amount of light reaching the water surface. ERS-2 SAR backscatter increased concomitantly. Our data support the belief that ERS-2 SAR data may be helpful for mapping the spatial patterns of rice growth, distinguishing different agricultural practices, and monitoring the abundance of vectors in nearby villages.     

Geocoding of fast-delivery ERS-l SAR image mode product using DEM data

An algorithm for geocoding and radiometric correction of ERS–1 Synthetic Aperture Radar images by using Digital Elevation Model data is described a.nd applied to images over a mountainous area in Norway. Several images covering the same area are calibrated and geocoded into a UTM map projection and the results are evaluated against map and i/1 situ measurements. The map to SAR geometric precision is given by a rms value of 54m in the casting direction and 45m in the northing direction. The SAR to SAR geometric precision is given by a rms value of 43m in the casting direction and 44m in the northing direction. Estimates of the radar backscatter coefficient are extracted from the geocoded SAR backscatter images. Obtained values for wet snow–13dB and for dry snow–7dB which agrees favourably with earlier radar measurements and theory.     

Assessment of the capabilities of multi-temporal ERS-1 SAR data to discriminate between agricultural crops

Land-cover data are used for the enforcement of quota restrictions, yield estimations and agricultural statistics. With the launch of the European Remote sensing Satellite (ERS-1) the first long-term spaceborne synthetic aperture radar (SAR) has become available for the mapping of land-cover. This paper assesses the capability of ERS-1 SAR precision data to (i) discriminate between the crop types for land-cover inventory purposes at (ii) the earliest stage in the growing season, using field-based classification. The objectives were tested for an agricultural region in The Netherlands where 12 crop types are found. Fourteen ERS-1 SAR images were available for this area, covering the 1992 growing season between May and November. The field-based classification yielded an overall classification accuracy of 80 per cent with the optimal data set. The stage at which the crop types could be assessed is crop dependent. Most crop types could not be distinguished before the month of August.     

C-band radar backscatter of Baltic sea ice: theoretical predictions compared with calibrated SAR measurements

Abstract

Airborne Synthetic Aperture Radar (SAR) data have been analysed together with in situ measurements of sea ice during the Bothnian Experiment in Preparation for ERS-1 (BEPERS) in March 1988. Based on the physical properties of the snow-covered level ice, a scattering model is used to predict the C-band like-polarization backscattering coefficient in an experiment area. Both the average backscattering coefficient and the SAR image texture were found to be in good agreement with the scattering model predictions. The backscatter signature of the level ice was found to be dominated by the ice surface r.m.s. height and autocorrelation function. These parameters were determined from profiles of the ice surface height, which were measured using a laser profiler device with sub-mm accuracy. The present model is expected to be accurate when the backscattering is dominated by scattering from the cm-scale snow or ice surface roughness.     

Application of an artificial neural network in canopy scattering inversion

Owing to their recent success in other inversion tasks, application of an artificial neural network to the development of an inversion algorithm for radar scattering from vegetation canopies is considered. Because canopy scattering models are complicated functions of the desired biophysical parameters (vegetation biomass, leaf area index, soil moisture content, etc.), the development of an effective inversion algorithm is not a straightforward task. The Michigan Microwave Canopy Scattering (MIMICS) model, which has shown remarkable success in predicting the radar response to vegetation canopies, was used, as were measured polarimetric backscatter values. Hence, the radiative transfer simulation code, MIMICS, was used to produce some of the training data. The inputs to the neural network were the expected polarimetric backscatter values from specific canopies, while the outputs were the desired parameters, such as tree heights, crown thickness, leaf density, etc.

Two special cases were examined: (1) inversion of MIMICS given modelled aspen stands of different ages; (2) inversion of measured data from the Duke forest loblolly pine stands. The MIMICS inversion shows that neural networks are capable of accurately inverting some of the parameters of such a complicated model. The implication is that once MIMICS is made to model the radar data for a specific application, then inversion of the radar data may be accomplished. The measured data inversion shows that, even without a model such as MIMICS, one may train a neural network to invert several parameters of interest. However, this depends on accurate and complete surveys of the ground truth data to be useful.     

Classification of sea ice types in the Ross Sea,Antarctica from SAR and AVHRR imagery

ERS-2 synthetic aperture radar (SAR) and Advanced Very High Resolution Radiometer (AVHRR) imagery are used to examine spectral characteristics of late winter/early spring ice in the Ross Sea, Antarctica. The combined spectral signatures are used to distinguish six ice types: fast ice, new ice, smooth first year ice, rough first year ice, thin new ice/wind roughened open water and glacial ice. The procedure firstly involves 'picking' class boundaries from SAR imagery based on the morphology of a speckle reduced backscatter spectrum. These class boundaries are then used as input to an iterative segmentation procedure that involves the repeated application of a speckle reduction filter to the image. For an image from late September 1996 the segmentation procedure enabled separation of five general ice categories each with a characteristic backscatter range. However because of the combined contributions of ice thickness, surface roughness, salinity and water content to the SAR backscatter, further decision criteria are required to separate some physical ice types unable to be resolved individually using this method. Coincident and co-registered infrared data from the AVHRR sensor are used to extract spectral characteristics for the final ice classes. Using this procedure we were able to distinguish floating glacier ice from thin new ice/wind roughened open water and new ice from nearshore fast ice. These ice types were unable to be separated using SAR backscatter intensity alone. In addition image subtraction was also able to clearly delineate areas of shore fast ice.     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's 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.