Water Pixels Research Articles

Multi-temporal Synthetic Aperture Radar Metrics Applied to Map Open Water Bodies

Multi-temporal synthetic aperture radar (SAR) metrics are assessed to map open water bodies. High temporal variability and low minimum value in a time series of Envisat Advanced SAR (ASAR) Wide Swath Mode (WSM) backscatter measurements characterize open water bodies with respect to other land cover types. Confusion occurs in the case of steep terrain (slope angle > 10°), less than 10 backscatter observations and for mixed pixels with a water fraction. The behavior of the two SAR multi-temporal metrics is consistent at six study areas in Europe and Central Siberia. A simple thresholding algorithm applied to the multi-temporal SAR metrics to map open water bodies performs with overall accuracies above 90% in the case of pure pixels of water or land. The accuracy decreases when mixed pixels are accounted for in the reference dataset and for increasing land fraction in the reference samples. An overall accuracy of approximately 80% was obtained for a 50% threshold of the water fraction. Omissions of water areas occur mostly along shorelines. Specific conditions of the land surface can distort the minimum, causing commission in the water class. The use of a low order rank or percentile instead of the lowest backscatter value can reduce such commission error.

Extraction of water areas based on similarity analysis using pixel-level SAR image time series

In view of the characteristics of SAR images stack data,this paper presents a method for extraction of the water area based on similarity analysis using pixel- level SAR image time series. The basic idea is as follows:Firstly,the pixel- level SAR image time series is constructed,and the dynamic time warping( DTW) is chosen as the similarity measurement to compute the similarities between all the pixels and sampled water pixel. Then,the water pixels are extracted with the threshold value method,referring to the DTW distance of mixed pixels on borders of water for threshold value. Lastly,by replacing the backward scattering coefficients with DTW distance of each pixel,the accuracy of water identification is improved by 8- connected method. 25 wide- swath ENVISAT ASAR images collected during the period from January to December in 2008,which have 150 m spatial resolution,were utilized to extract water pixels in the study area. The result shows that the water pixels extracted by this method have high accuracy and integrity,thus proving that this method is practical in the extraction and mapping of water body distribution at the large regional scale.

Long‐term hydrological changes of the Aral Sea observed by satellites

AbstractThe Aral Sea has been shrinking since the former Soviet Union constructed irrigation projects to divert water from its main rivers in the 1960s. The diminishing of the Aral Sea is “one of the worst environmental disasters in the world” (from United Nations Secretary‐General Ban Ki‐moon). In this study, 33 years of satellite observations from Advanced Very High Resolution Radiometer (AVHRR) and 21 years of satellite altimetry sea level data from TOPEX/Poseidon, Jason‐1, and Jason‐2 are used to quantify the long‐term hydrological changes in the Aral Sea. A simple algorithm with AVHRR channels 1 and 2 albedo is developed to identify and discriminate the water pixels from land and cloud. Thus, monthly water coverage in the region can be reliably generated. The water coverage maps since 1981 show constant decline of the Aral Sea. The coverage dropped from ∼4.7–4.8 × 104 km2 in 1981 to about ¼ of this value in the recent years. In fact, drastic hydrological change was observed in the main Aral Sea during the 2000s. In the South Aral Sea, sea level shows a steady decrease from 35 m above sea level to <26 m since 1993. Total loss of water storage since 1993 is estimated to be ∼2.0 × 102 km3 for the South Aral Sea with a rate of ∼16−20 km3/yr before 2002, and a smaller value after 2002. In 1990, the North Aral Sea was observed to separate from the main Aral Sea. Water coverage, sea level, and total water storage were kept relatively stable for the period between 1993 and 2013 in the North Aral Sea. A water level increase and coverage expansion occurred during the 2005−2006 period when a dam was built in 2005 between the North Aral Sea and the South‐East Aral Sea.

An Automated Method for Extracting Rivers and Lakes from Landsat Imagery

The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and commission errors, edge position accuracy and completeness, were calculated to assess the method’s performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed.

What is the minimum river width for the estimation of water clarity using medium-resolution remote sensing images?

Numerous studies have demonstrated the feasibility of remotely sensing water clarity of lakes, reservoirs, and larger rivers using medium-resolution spatial images. However, addressing relatively small rivers or river sections is very challenging due to the adjacency effect from the riverbanks. The objectives of this study are to quantify the minimum river width for water quality remote sensing and to validate the feasibility of using medium-resolution spatial images for estimating the Secchi disk depth (SDD). A methodology was developed to quantify the minimum river width for water quality remote sensing using high-resolution spatial images from WorldView-2 and Pleiades. Our results suggest that the influential distance of the adjacency effect from the riverbank is 17.3 m, i.e., water pixels with a distance of more than 17.3 m from the shoreline experienced a minimal disturbance from the riverbank. For the 30 m spatial resolution HJ-1A image (one of Chinese civilian satellites launched in 2008), the minimum river width is 64.6–98.5 m (the variation was determined according to the river flow direction and the pixel position relative to the shoreline). Using the sections that satisfied the minimum river width requirement, a significant estimation model was established between the spectral reflectance and the SDD (R2 = 0.94), demonstrating that the minimum river width recommended in this study is practical. This work is the first study to quantify the minimum river width for water quality remote sensing and thus provides a valuable reference for remote sensing of relatively small rivers.

Automated flood detection with improved robustness and efficiency using multi-temporal SAR data

Flood detection from synthetic aperture radar (SAR) images should be performed with accurate, stable, automated and time-efficient algorithms; however, few methods meet all these requirements. Recently, Giustarini et al. proposed an automated promising methodology, capable of providing satisfactory results in flood detection. The algorithm is based on the assumption that a flood image contains a relatively high number of pixels with low backscatter values, exhibiting a bimodal histogram. For the case of a histogram that is not bimodal, the optimization of the theoretical curve describing the water pixels has to be manually constrained in a user-defined range. To overcome this shortcoming, this letter proposes an alternative procedure for core water body identification. First, by thresholding the difference image, derived by change detection between the flood and reference images, a mask of core water bodies is identified. Then, the mask is applied on the flood image, to extract the water pixels located in the core water bodies and straightforwardly derive the statistical curve describing water pixels. Successively, a sequence of thresholding, region growing and change detection is applied. The experimental results with two pairs of SAR images show that the proposed automated algorithm is stable and time-efficient, and provides accurate results.

Mapping shallow lakes in a large South American floodplain: A frequency approach on multitemporal Landsat TM/ETM data

• Paraná River floodplain is a complex mosaic of marshes dotted with shallow lakes. • We performed a multi-decadal frequency analysis of NDVI derived from Landsat images. • A selected threshold value of NDVI allowed the separation of standing water pixels. • We identified and mapped the shallow lakes of the Lower Paraná River floodplain. We propose a methodology to identify and map shallow lakes (SL) in the Paraná River floodplain, the largest freshwater wetland ecosystem in temperate South America. The presence and number of SL offer various ecosystem services and habitats for wildlife biodiversity. Our approach involved a frequency analysis over a 1987–2010 time series of the Normalized Difference Vegetation Index (NDVI), derived from Landsat 5 and 7 TM/ETM data. Through descriptive statistics of samples of pixels and field work in different types of SL, we established an NDVI threshold of 0.34 below which we assumed the presence of water in each pixel. The standard deviation of the estimated SL area decreases with the number of images in the analysis, being less than 10% when at least 30 images are used. The mean SL area for the whole period was 112,691 ha (10.9% of the study area). The influence of the hydrological conditions on the resulting SL map was evaluated by analyzing twelve sets of images, which were selected to span the whole period and different time frames according to multiannual dry and wet periods and to relative water level within each period. The Kappa index was then calculated between pairs of resulting SL maps. We compared our maps with the available national and international cartographic documents and with other published maps that used one or a few Landsat images. Landsat images time series provide an accurate spatial and temporal resolution for SL identification in floodplains, particularly in temperate zones with a good provision of cloud free images. The method evaluated in this paper considers the dynamics of SL and reduces the uncertainties of the fuzzy boundaries. Thus, it provides a robust database of SL and its temporal behavior to establish future monitoring programs based on the recent launch of Landsat 8 satellite.

Effective Compositing Method to Produce Cloud-Free AVHRR Image

The Advanced Very High Resolution Radiometer (AVHRR) series of instruments have been frequently used for land cover change and global environment studies. The availability of more than 30 years' records have made important time-series studies possible. Removing cloud effects from AVHRR images is a critical task when using the data to monitor land cover changes. Considering that the maximum normalized difference vegetation index (NDVI) compositing method is not suitable for land cover types with low NDVI (e.g., water, snow, and bare ground), we propose a new compositing method to remove cloud effects while keeping information of all land cover types. The developed method includes three steps: 1) compositing daily AVHRR images using maximum brightness temperature in Channel 4; 2) replacing water pixels with pixels having maximum Channel 1/Channel 2 ratio; and 3) replacing vegetation pixels with pixels having maximum NDVI. The proposed method was tested on the Land Long-Term Data Record-AVHRR dataset in a region of 37°E-180°E, 3°S-73°N in 1993 to generate monthly minimal cloud-effected images. After compositing, the average ratio of cloud-contaminated and invalid pixels is reduced from 64.8% to 22.0%. Compared with the other two methods (composite using maximum NDVI and composite using cloud masks), our method is shown to be more effective and convenient. Moreover, the compositing process takes consideration of the temporal profile of land surface, which is suited for long time global or continental land cover change studies. Composite images generated in different time periods are also evaluated to identify the proper compositing period.

A Water Index for SPOT5 HRG Satellite Imagery, New South Wales, Australia, Determined by Linear Discriminant Analysis

A new water index for SPOT5 High Resolution Geometrical (HRG) imagery normalized to surface reflectance, called the linear discriminant analysis water index (LDAWI), was created using training data from New South Wales (NSW), Australia and the multivariate statistical method of linear discriminant analysis classification. The index uses all four image bands, and is better at separating water and non-water pixels than the two commonly used variations of the normalized difference water index (NDWI), which each only use two image bands. Compared across 2,400 validation pixels, from six images spanning four years, the LDAWI attained an overall accuracy of 98%, a producer’s accuracy for water of 100%, and a user’s accuracy for water of 97%. These accuracy measures increase to 99%, 100% and 98% if cloud shadow and topographic shadow masks are applied to the imagery. The NDWI achieved consistently lower accuracies, with the NDWI calculated from the green and shortwave infrared (IR) bands performing slightly better (91% overall accuracy) than the NDWI calculated from the green and near IR bands (89% overall accuracy). The LDAWI is now being routinely used on an archive of over 2,000 images from across NSW, as part of an operational environmental monitoring program.

Algorithms for Moderate Resolution Imaging Spectroradiometer cloud-free image compositing

The presence of clouds is the biggest obstacle in the investigation of land cover, and many techniques have been developed to detect clouds. However, few indicators have been proposed for the detection of cloud-free conditions. To address this, we propose two indicators for use in compositing 8-day cloud-free images: the B 1/7 ratio is the ratio of the band 1 reflectance to the band 7 reflectance of Terra surface reflectance images (MOD09GA), and saturation refers to the color saturation of these images. Here, we describe the principles underlying these two indicators and analyze their characteristics for vegetation, water, urban, and nonvegetation pixels under cloud-free, cloud shadow, and cloudy conditions using MOD09GA from October 16 to 23, 2007, in North China (sample A) and using data published by the U.S. Geological Survey (USGS). We found that the B 1/7 ratio and saturation are suitable for extracting cloud-free pixels over land and water, respectively; therefore, we combined these two indicators to develop a single-unified model. In particular, our results demonstrate that the pixels exhibiting the lowest B 1/7 ratios should be adopted as cloud-free pixels over land when the value of B 1/7 for land surfaces is between 0 and 1, and the surface reflectance of Moderate Resolution Imaging Spectroradiometer band 1 is less than 0.3. Otherwise, the pixels exhibiting the greatest saturation values should be adopted. We used our model to composite cloud-free images for two additional regions in China: the Tarim basin (sample B) from October 2 to 9, 2012, and the coastal areas of southeastern China (sample C) from April 15 to 23, 2013. We compared the cloud-free images of these regions with the 8-day surface reflectance product (MOD09A1) with respect to clouds, cloud shadow, and cirrus clouds, and we found that our proposed cloud-free image compositing approach can accurately eliminate both clouds and cirrus clouds. Specifically, the percentage of residual cloud pixels in sample C was found to be less than that in MOD09A1. Moreover, in the cloud-free images obtained using our newly developed method, cloud-free pixels are typically associated with greater sensor zenith angles and smaller scatter angles than those in MOD09A1. However, our method retains some limitations. In particular, 9.68, 33.22, and 33.00% of cloud-shadow pixels remain in the cloud-free images for samples A, B, and C, respectively.

Direct water and fat determination in two‐point Dixon imaging with flexible echo times

Identifying water and fat unambiguously in multipoint Dixon imaging often requires phase correction, which can be challenging and may fail. The purpose of this work is to present a geometric interpretation of the two-point Dixon method with flexible echo times (TEs) and to investigate the conditions under which water and fat can be determined directly without phase correction. Geometrically, the equation for the magnitude of the acquired signal at a given TE represents an ellipse in the water-fat plane centered at the origin. Determining water and fat in two-point Dixon imaging thus amounts to finding the correct intercept between two ellipses from the signals at two TEs. At the right TE combinations, the physicality requirement that water and fat be non-negative can be used to select a unique water and fat solution. Systematic computer simulations were conducted to examine the ranges of the TEs for which this approach is feasible and how different noise levels impact the feasibility. Phantom and in vivo experiments on a 1.5-T whole-body MRI scanner were used to validate the computer simulations. In simulation and phantom experiments, nearly all pixels of pure water or pure fat were reliably identified based on the physicality requirement alone for a range of practically useful TE combinations (e.g., around 3 ms/6 ms at 1.5 T) and at moderate to high SNR levels (≥ 25). At other TE combinations, finding the correct solution based on the physicality requirement alone was not feasible or became sensitive to noise. In vivo findings were in overall agreement with the simulation and phantom studies, although the percentage of pixels that were correctly determined was lower. The problem of direct water and fat determination without phase correction can be understood geometrically. Using the physicality requirement, it is possible to identify the different TE combinations and imaging conditions under which water and fat imaging can be performed either completely without phase correction or by generating a first-pass solution that can be used to improve the processing reliability of a phase-correction based method.

Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data

In this study medium resolution remote sensing data of the AVHRR and MODIS sensors were used for derivation of inland water bodies extents over a period from 1986 till 2012 for the region of Central Asia. Daily near-infrared (NIR) spectra from the AVHRR sensor with 1.1km spatial resolution and 8-day NIR composites from the MODIS sensor with 250m spatial resolution for the months April, July and September were used as input data. The methodological approach uses temporal dynamic thresholds for individual data sets, which allows detection of water pixel independent from differing conditions or sensor differences. The individual results are summed up and combined to monthly composites of areal extent of water bodies. The presented water masks for the months April, July, and September were chosen to detect seasonal patterns as well as inter-annual dynamics and show diverse behaviour of static, decreasing, or dynamic water bodies in the study region. The size of the Southern Aral Sea, as the most popular example for an ecologic catastrophe, is decreasing significantly throughout all seasons (R2 0.96 for April; 0.97 for July; 0.96 for September). Same is true for shallow natural lakes in the northern Kazakhstan, exemplary the Tengiz-Korgalzhyn lake system, which have been shrinking in the last two decades due to drier conditions (R2 0.91 for July; 0.90 for September). On the contrary, water reservoirs show high seasonality and are very dynamic within one year in their areal extent with maximum before growing season and minimum after growing season. Furthermore, there are water bodies such as Alakol-Sasykol lake system and natural mountainous lakes which have been stable in their areal extent throughout the entire time period. Validation was performed based on several Landsat images with 30m resolution and reveals an overall accuracy of 83% for AVHRR and 91% for MODIS monthly water masks. The results should assist for climatological and ecological studies, land and water management, and as input data for different modelling applications.

Spectral relationships for atmospheric correction II Improving NASA's standard and MUMM near infra-red modeling schemes

Spectral relationships, reflecting the spectral dependence of water-leaving reflectance, ρw(λ), can be easily implemented in current AC algorithms with the aim to improve ρw(λ) retrievals where the algorithms fail. The present study evaluates the potential of spectral relationships to improve the MUMM [Ruddick et al., 2006, Limnol. Oceanogr. 51, 1167-1179] and standard NASA [Bailey et al., 2010, Opt. Express 18, 7521-7527] near infra-red (NIR) modeling schemes included in the AC algorithm to account for non-zero ρw(λNIR), based on in situ coastal ρw(λ) and simulated Rayleigh corrected reflectance data. Two modified NIR-modeling schemes are investigated: (1) the standard NASA NIR-modeling scheme is forced with bounding relationships in the red spectral domain and with a NIR polynomial relationship and, (2) the constant NIR ρw(λ) ratio used in the MUMM NIR-modeling scheme is replaced by a NIR polynomial spectral relationship. Results suggest that the standard NASA NIR-modeling scheme performs better for all turbidity ranges and in particular in the blue spectral domain (percentage bias decreased by approximately 50%) when it is forced with the red and NIR spectral relationships. However, with these new constraints, more reflectance spectra are flagged due to non-physical Chlorophyll-a concentration estimations. The new polynomial-based MUMM NIR-modeling scheme yielded lower ρw(λ) retrieval errors and particularly in extremely turbid waters. However, including the polynomial NIR relationship significantly increased the sensitivity of the algorithm to errors on the selected aerosol model from nearby clear water pixels.

Suspended Sediment Concentration Profiles From Synoptic Satellite Observations

A method is developed to estimate vertical suspended sediment concentration (SSC) profiles in Gulf of Kachchh, from the sediment concentration values derived from synoptic observations of Ocean Colour Monitor (OCM). Under the influence of currents, vertical SSC profiles are defined by power law being based on the frictional velocities and sediment settling velocities. For each water pixel in OCM, the vertical variations in SSC are determined from the SSC profiles calculated from power law using corresponding simulated frictional velocities and sediment size parameters. OCM derived SSC is considered to be summation of sediment concentration values from surface to first attenuation depth (hereafter OCM-depth) with an exponential decay of values with depth. Vertical variation profiles from surface to OCM-depth are scaled with respect to the corresponding OCM SSC values and the projected concentrations at the bottom are derived. The bottom concentrations derived is observed to vary chiefly with the tidal conditions prevailing within the gulf, when during the slack hours increase in bottom SSC values get enhanced owing to the settling of sediments from suspension.

Improving remote sensing flood assessment using volunteered geographical data

Abstract. A new methodology for the generation of flood hazard maps is presented fusing remote sensing and volunteered geographical data. Water pixels are identified utilizing a machine learning classification of two Landsat remote sensing scenes, acquired before and during the flooding event as well as a digital elevation model paired with river gage data. A statistical model computes the probability of flooded areas as a function of the number of adjacent pixels classified as water. Volunteered data obtained through Google news, videos and photos are added to modify the contour regions. It is shown that even a small amount of volunteered ground data can dramatically improve results.

A New Short-Wave Infrared (SWIR) Method for Quantitative Water Fraction Derivation and Evaluation With EOS/MODIS and Landsat/TM Data

A quantitative method is developed for deriving water fraction from coarse- to medium-resolution satellite data with visible to short-wave infrared (SWIR) channels based on the linear mixture theory. The method uses a SWIR channel (1.64 μm) by assuming that the water-surface-leaving radiance in this channel is insignificant and is thus less affected by water types and water depth than near-infrared (NIR) channels for inland water bodies. For a mixed water pixel, a dynamic nearest neighbor searching (DNNS) method is used to find the nearby land pixels to determine the average land reflectance. The nearby pure water pixels with a similar water type to the subpixel water portion of the mixed water pixel are found dynamically to derive the average water reflectance. The average reflectance in the SWIR channel from both pure land pixels and water pixels is used to calculate the water fraction from a linear mixture model. The developed method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data and shows promising results. High-resolution satellite data from the Thematic Mapper (TM) are used to evaluate the water fraction derived from MODIS. During pixel-to-pixel water fraction evaluation, TM data are spatially aggregated to MODIS resolution. When evaluated against the high-resolution TM observations, water fractions derived from MODIS using the DNNS method with the SWIR channel show a bias of -0.021 with a standard deviation of 0.0338. Comparing lake areas between TM and MODIS data also shows consistent results with the pixel-to-pixel water fraction comparison. The DNNS method is also compared to the traditional histogram method both with SWIR channel and NIR channel. The results show that the DNNS method is more accurate than the histogram method and that the SWIR channel is better than the NIR channel to derive highly accurate water fraction from coarse- to medium-resolution satellite data.

Mapping Nighttime Flood from MODIS Observations Using Support Vector Machines

0099-1112/12/7811–1151/$3.00/0 © 2012 American Society for Photogrammetry and Remote Sensing Abstract This work proposes a nighttime flood mapping method for Moderate Resolution Imaging Spectroradiometer (MODIS) data. Brightness temperatures at 3.9 m, and 11 m channels (BT 3.9, and BT 11, respectively) and differences of brightness temperatures between 3.9 m and 4.0 m, and between 11 m and 12 m (BT 3.9-BT 4.0, and BT 11-BT 12, respectively) are used to identify nighttime water pixels by a support vector machines (SVM) classifier. Prominent flood locations are detected by a change detection process using a reference water-land map. To test the effectiveness of the proposed method, two flood cases caused by heavy rains were chosen as trial scenarios. The nighttime mapping results are validated with the flood maps, which are obtained from the visual interpretation based on the daytime flood identification results. The experimental results indicate that the proposed method is effective for the delineation of inundated areas with standing water during the nighttime.

Alternative spatially enhanced integrative techniques for mapping seagrass in Florida's marine ecosystem

Seagrass is an important component of coastal marine ecosystems. Seagrass mapping provides a means for assessing seagrass health by monitoring the spatial distribution and density of seagrass habitat in coastal waters. Recent image processing and satellite technologies present the opportunity to leverage quantitative techniques that have the potential to improve upon traditional photo-interpretation techniques in terms of cost, mapping fidelity, and objectivity. Integrated spatial and spectral processing techniques were identified as an alternative method for mapping seagrass extent and density from an IKONOS satellite image of Springs Coast, Florida. These spatially enhanced integrative mapping techniques objectively standardize seagrass-monitoring efforts and enhance mapping capabilities by characterizing spatial seagrass density gradients. A combination of water column correction, pixel classification, and image segmentation techniques provided a seagrass density index map that represented seagrass density and distribution with high spatial detail and overall accuracy (77%) comparable to photo-interpretation techniques. Satellite imagery-based spatially enhanced image processing techniques were found to provide a consistent, quantitative, and cost-effective alternative for seagrass mapping in Springs Coast with the potential to be transferred to other parts of the world. A cost savings analysis concluded that there was a 13% cost saving using satellite photo-interpretation and a 47% cost saving using enhanced satellite classification when compared to aerial photo-interpretation.

SAR Algorithm for Sea Ice Concentration—Evaluation for the Baltic Sea

A new sea ice concentration algorithm has been developed for C-band synthetic aperture radar data. Detailed autocorrelation statistics are derived and adopted by the algorithm, and a neural network is utilized for training against 41 sea ice charts. The charts are produced by ice analysts at the Swedish Ice Service and cover the Baltic Sea. The classification of open water pixels is accurate to 94% on average, and the classification of sea ice pixels has an accuracy of 87%. This results in a root-mean-square error of 6.7 percentage points in estimating the sea ice concentration.

Near Real-Time Flood Detection in Urban and Rural Areas Using High-Resolution Synthetic Aperture Radar Images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high-resolution synthetic aperture radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high-resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18%, respectively.