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The Sissingué Gold Project is in Northern Côte d'Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks. [ABSTRACT FROM AUTHOR]

Remote sensing plays an increasingly important role in climate research, and as remote sensing observation files span longer periods of time, we are receiving more and more data on climate parameters such as clouds. Four decades ago, it was proposed that the area of planar shapes change with their perimeter following the power law. The exponent of this relation for the areas of clouds (typical and noctilucent) and rain determined by satellites and radar is 1.35 close to 4/3 of the classical turbulence theory of 1941. In this paper, we propose a new relationship between the area and the perimeter of rain and clouds using remote sensing observations. This relationship leads to the development of a new model for the detection of extreme natural phenomena related to rain and clouds. [ABSTRACT FROM AUTHOR]

Lineament mapping is a very important step in geological studies and mineral exploration, with the evolution of remote sensing, processing methods as well as the availability of several optical and radar satellite data, lineaments can be detected without using traditional methods. The objective of this work is to recommend the most suitable data for lineaments to be used in the field of geological science, by comparing the performance of three different RADAR data, namely ALOS PALSAR (The Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), Radarsat-1 and Sentinel-1 for automatic lineament extraction using a combination of edge detection and line linking algorithms. The methodology consists to link between the length, number, orientation and density of lineaments with surface characteristics such as the slope, faults, lithology, discontinuities and mineral veins. The results obtained show that the extracted lineaments from Sentinel-1 VH polarization have a better correlation with geological units, tectonic system direction, as well as shading and slope maps, which is due to the effectiveness of VH polarization, which is independent of soil properties compared to the other polarizations that have shown an overestimation of the lineaments. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) came into practical use as a geohazard monitoring tool in the 1990s. The uptake of this remote sensing technique however is geographically varying, and Australia has been one of many continents not fully utilizing the available InSAR data. As a result, limited knowledge exists on the application of InSAR data to obtain insights and benefits in geohazard risk monitoring and management. Due to the said limitation, this research carried out a concise technical background study and investigated a suite of case studies to demonstrate the potential for geohazard monitoring and assessment in Australia using InSAR. The differential InSAR workflow was applied to sites that experienced heaving and subsiding of the ground surface, mining, earthquake, landslides, and tunnelling in West Metro Melbourne (VIC), Musgrave (SA), Crinum mine (QLD), Pelham (TAS), Lake Muir (WA), and Sydney (NSW). The results of the Differential InSAR analysis resulted in acceptable ground movement and deformation estimates when compared with measured field data. Future opportunities related to new applications of the InSAR technique on geohazard reduction considering climate change contributions and probable coupling with artificial intelligence techniques were also discussed. This research has formed a conducive and scientific learning medium, which may encourage the growth of multidisciplinary research combining the fields of remote sensing and geotechnical engineering to investigate the impact of geohazards. [ABSTRACT FROM AUTHOR]

Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product. [ABSTRACT FROM AUTHOR]

In this paper, the possibilities of remote sensing of moisture profiles in the arable layer were theoretically and experimentally studied based on the nadir measurements of reflection coefficients at three frequencies of 1.26 GHz, 796 MHz and 641 MHz. The reflection coefficients were measured by the impulse method during natural cycles of evaporation and moistening of an arable layer at the agricultural field being under steam, located at 56°05ʹN, 92°40ʹ E in the area of the Minino village, Krasnoyarsk region, the Russian Federation. The soil moisture profiles were retrieved in the course of solving the inverse problem, in which the reflection coefficients at different frequencies acted as an informative sign. The root-mean-square error and the determination coefficient (R2) between retrieved and measured moisture values in the topsoil thickness of 0.15 m were 3.3% and 0.79, respectively. In the course of theoretical calculations, it was shown that in practice, it is impossible to predict the sensing depth of the arable layer without preliminary information on the form of moisture profile. Moreover, the sensing depth depends not only on the form of soil moisture profile but also on frequency. In this regard, it is impossible to correlate the effective soil moisture, retrieved from single-frequency measurements of the reflection coefficient in the approximation of homogeneous topsoil, with the specific thickness of topsoil. The study shows the promise of developing multi-frequency radar systems for remote sensing of soil moisture profiles in the arable layer, the potential of which can be realized on lightweight unmanned area vehicle (UAV) platforms. [ABSTRACT FROM AUTHOR]

The nonstationary clutter is one of the biggest challenge to ocean remote sensing radar system such as high frequency (HF) mixed-mode surface wave radar. The performance of space-time adaptive processing (STAP) degrades badly with limited homogeneous secondary training data support. Single dataset algorithms overcome the problem by working on primary data solely. But the heavy computational complexity as well as the inaccurate estimation of the clutter covariance matrix restricts the practical application of these methods. In this letter, we propose a novel reduce-rank-based single dataset STAP to suppress the nonhomogeneous clutter in practical HF radar system. A fast implementation of subspace tracking algorithm is introduced to estimate the clutter subspace as well as reduce the computational cost via pulse iteration method. The effectiveness of the proposed method is verified by both simulated and experimental data. The results show it outperforms traditional single dataset STAP methods and the nonstationary clutter can be greatly suppressed. [ABSTRACT FROM AUTHOR]

The primary objective of these studies was to quantitatively assess the ground deformation velocities and rates and their natural and man-made controlling factors as the potential risks along the seismically active 70 km section of buried Baku-Tbilisi-Ceyhan Oil, South Caucasus Gas, Western Route Oil and South Caucasus Pipeline Expansion Gas pipelines in Azerbaijan using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique. PS-InSAR analysis showed that the continuous subsidence was prevailing in the kilometer range of 13-70 of pipelines crossing two active seismic faults. The ground uplift deformations were observed in the pipeline kilometer range of 0-13. The minimum and maximum vertical ground movement velocities were observed to be −21.3 mm/y and 14.1 mm/y along 70 km section of pipelines with 250 m buffer zone. Both of these sites were observed at the range of seismic faults. The spatial distribution of sites with ground deformation velocity less than −15 mm/y and more than 15 mm/y was diverse and random all along 70 km of pipelines without any cumulative spatial patterns. Based on the lower mean, variation and standard deviation of pixel values, the seismic fault in the kilometer range of 21-31 revealed its higher vulnerability to subsidence processes rather than the Seismic Fault in the kilometer range of 46-54. The ground deformation velocities within the range of Seismic Fault KP21-31 revealed the minimum and maximum values of −19.74 mm/y and 14.1 mm/y, respectively whereas at the Seismic Fault KP46-54, the minimum and maximum values were −17.07 mm/y and 9.29 mm/y, respectively. Encouraging level of agreement with the regression coefficients of 0.92 and 0.96 for known subsiding sites at KP28 + 500 and KP52 + 750 and 0.97 and 0.96 for known uplifting sites at KP04 + 900 and KP35 + 050 respectively was observed between the high-precision GPS and PS-InSAR measurements. The diverse spatial distribution and variation of ground movement processes along pipelines demonstrated that general geological and geotechnical understanding of the study area is not sufficient to find and mitigate all the critical sites of subsidence and uplifts for the pipeline operators. The prediction of the potential subsidence or uplift locations based on the field visual verifications holds a lot of uncertainties without broad and detailed scale airborne and satellite space observation technologies. The justification of the budget for the geotechnical maintenance activities along long-range oil and gas pipelines requires sophisticated prioritization and planning of the remediation sites and clear quantitative and qualitative risk assessment proving the activeness of these sites and effectiveness of the remediation measures. This means that the PS-InSAR – based approach outlined in this paper is a significant improvement over current ground-based monitoring practices or can significantly contribute them in the initial phase of risk assessment and prioritization. [ABSTRACT FROM AUTHOR]

In this work, a mechanism is proposed to enhance the remote sensing accuracy of conducting objects located within continuous random media. Through our mechanism, operating frequency would be selected properly for preciously radar detection. In this regard, numerical analysis for the influence of medium characteristics on the radar cross section (RCS) is conducted. Illumination region is assumed as partially convex surface while considering targets having large sizes of about four wavelengths. We handle the effect of various parameters such as the medium randomness intensity and scale size, target configuration having curvature and size and incident waves polarization. Waves are backscattering from objects in both free space and random medium while considering horizontal polarization of incident waves. Such polarization generates creeping waves in which they have an extra impact on the backscattering waves where would be avoided through our mechanism. [ABSTRACT FROM AUTHOR]

A three-dimensional (3D) image reconstruction method for the spinning target based on the radar network is proposed in this letter and the design concept is sourced from the similarity of the two-dimensional (2D) imaging result with the target’s mapping on the imaging plane. The proposed method has the capacity to generate 3D image of the spinning target by reconstructing the scattering distribution in 3D Cartesian space. Firstly, the inverse Radon transform is applied to obtain the imaging result of the spinning target and the mapping formulas for mapping the air points onto the 2D image plane are derived. In addition, the radar network 3D image reconstruction model for the spinning target is constructed and the corresponding algorithm for solving the reconstruction model is proposed. Finally, some experiment results are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we provide a review of the different approaches used to detect activities inside a building using through-the-wall radar. We classify two main types of radar that are used in detection activities behind the wall. Stepped frequency and pulse compression radar are the most frequently used types of radar in through-the-wall radar imaging (TWRI). In addition, a preference has been shown for using frequencies in the range of 1-3 GHz. Though a high volume of research is being done in this area, there are still many open issues. Detectability of the human being, wall modeling, and target differentiation have been identified as the main open issues in the field of TWRI. [ABSTRACT FROM AUTHOR]

The range resolution is limited by insufficient continuous bandwidth in High Frequency Radar (HFR), due to the working frequency band (3-30 MHz) is heavily congested. In order to minimize the mutual interference from other users and improve bandwidth utilization, the Discontinuous Spectra Synthetic Bandwidth (DSSB) signal is proposed. For collecting all target information from echoes, multiple snapshots are needed to synthesize the range spectrum of multiple targets in the long-range observation window. The Multi-Snapshots Compressed Sensing-Information Extraction (MCS-IE) method is proposed to accurately estimate range and amplitude from multiple snapshots. For MCS-IE method, a new measurement matrix according to random frequencies of compressed sensing is designed for samples in every snapshot, and then information extraction method is used for the same range window with multiple samples and estimations. A series of comparison experiments based on measured data from HFR Frequency Monitoring System (FMS) are tested to verify proposed methods. [ABSTRACT FROM AUTHOR]

This contribution proposes an evaluation of lidar and radar data processing and its potential in revealing archaeological features within a level plain environment, the southern lowland of Verona (Italy), focusing on evidences dating back to the Bronze Age. Many archaeological sites in the research area, including some of the most outstanding settlements of Terramare Culture, were identified or at least examined through aerial photo observation. Even if in several occasions modern agricultural activities contributed to the discoveries, bringing to the surface artifacts and scrapes of buried layers, this kind of impact has also been progressively deteriorating the archaeological record, hence the proto-historic landscape is now discernible through evanescent marks which cannot be always detected using customary optical sensors. Lidar and radar data analysis has then been considered as an alternative, non-invasive method of investigation on such a vast area. [ABSTRACT FROM AUTHOR]

Terrestrial biosphere carbon dynamics are the most uncertain elements of the global carbon budget. Carbon stocks estimated using spatially extensive remote sensing are crucial in reducing this uncertainty, and using these stocks as initial conditions to biosphere models can improve carbon flux predictions beyond the site level. Yet remote-sensing data are not always consistently available for large regions, so methods assessing carbon uncertainty using data sources in one location may not be transferable to another. This study assesses the use of multiple-source data from lidar, radar, imaging spectroscopy, and national forest inventories to derive forest structure and composition necessary to initialise the Ecosystem Demography model (ED2), and hence evaluate short-term carbon flux uncertainty over Harvard Forest, Massachusetts. ED2 was initialized using forest structure and composition derived from lidar and national forest inventories, radar and national forest inventories, lidar and imaging spectroscopy, and radar and imaging spectroscopy resulting in net ecosystem productivity uncertainty of 26.3%, 41.9%, 19.6%, and 20.2%, respectively, compared to ground-based forest inventory initializations. This study uniquely offers a multitude of methods to estimate forest ecosystem state, with resulting carbon uncertainties, transferable to regions with potentially different data availability. Furthermore, in preparation for satellite radar, lidar, and imaging spectrometer, this study highlights the importance of combining techniques deriving forest structure and composition at different scales, binding regional to potentially global carbon-fluxes with remote sensing, reducing this uncertainty source in global climate models. [ABSTRACT FROM PUBLISHER]

The method based on the dual-frequency correlation function of reflected signals is one of the effective approaches for sea wave height estimation in radar altimetry. This method, however, has been applied for relatively low-altitude flights. As shown in our early studies, for the estimation of the sea wave heights using the dual-frequency correlation function, it is necessary to synthesize the array antennas, which are perpendicular to the flight direction. In this paper, we derived the limiting accuracy of the measurement of the sea wave heights using the dual-frequency microwave interferometry, and analyzed its dependence on the sea wave heights and the radar system parameters. Evaluation of the limiting accuracy of the measurement of the sea wave heights shown in the paper verified its high accuracy for sea status monitoring using a small remote sensing spacecraft. [ABSTRACT FROM AUTHOR]

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory. [ABSTRACT FROM PUBLISHER]

To overcome the inherent conflict between azimuth resolution and wide swath in conventional synthetic aperture radar (SAR) systems, the concept of raster scan SAR (RS-SAR) is proposed in this letter, where wide swath observation is realized by slant beam scanning and effective reception of echo data is achieved by varying the pulse repetition frequency. Details of the RS-SAR are provided, and its significantly improved performance is demonstrated by an example system based on several derived key performance indicators. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a straightforward approach for flood area mapping in a transboundary riverbed using Geographic Object-Based Image Analysis. Weak bilateral/multilateral cooperation among neighboring countries hampers effective disaster management and mitigation activities over transboundary areas and strengthens the demand for reliable remote-sensing-derived information. Three object-based classification approaches using ENVISAT/ASAR and multi-temporal LANDSAT TM data were developed and validated for flood area delineation. The accuracy assessment of the classification results was based on oblique air photo interpretation and an area-based comparison with the official flood map. The bi-level object-based model using the Normalized Difference Water Index and the original post-flood TM bands attained 92.67% overall accuracy in inundated-areas detection, while the ENVISAT/ASAR classification was the least accurate (85.33%), probably due to the lower spatial resolution of the Synthetic Aperture Radar image. A strong agreement (92.14%) was found between the LANDSAT flood extent and the official flood map, suggesting that the proposed method has the potential to be employed in the future as a standard part of a flood crisis management process. [ABSTRACT FROM PUBLISHER]

Background:It is essential that systems for measuring changes in carbon stocks for Reducing Emissions from Deforestation and Forest Degradation (REDD) projects are accurate, reliable and low cost. Widely used systems involving classifying optical satellite data can underestimate degradation, and by classifying the landscape ignore the natural heterogeneity of biomass. Aims:To assess the ability of repeat L-band radar to detect areas of small increases or decreases in above-ground biomass (AGB) across a Miombo woodland landscape. Methods:ALOS PALSAR L-band cross-polarised (HV) radar data from 2007 and 2009 were used to create maps of AGB, calibrated using 58 field plots. The change in AGB was assessed for land parcels with known landcover histories: (i) 500 ha of new agroforestry; (ii) 9500 ha of protected (REDD) areas; and (iii) 23 ha of land where degradation occurred between 2007 and 2009. Results:Increases in AGB were detected in both the agroforestry and REDD areas (0.4 and 1.1 Mg C ha−1year−1, respectively), while the degraded areas showed a decrease of 3 Mg C ha−1year−1. Conclusions:PALSAR data can be used to detect losses and gains in AGB in woodland ecosystems. However, further work is needed to precisely quantify the uncertainties in the change estimates, and the extent of false-positive and false-negative change detections that would result from using such a system. [ABSTRACT FROM PUBLISHER]

This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or transplanted field due to specular reflection for little exposure of crop, with increase in backscatter as the crop growth progresses due to volume scattering. Besides, the multiple interactions between SAR and vegetation/water also lead to an increase in backscatter as the crop growth progresses. Classification thresholds were established based on the information provided by each pixel in each image, the pixel's typical temporal behaviour due to crop phenology and changing water depth in rice field and their corresponding SAR signature. Based on this logic, the study site (i.e. South 24 Paraganas district, West Bengal) was classified into three major rice cultural types, namely shallow water rice (SWR; 5 cm ≤ water depth ≤ 30 cm), intermediate water rice (IWR; 30 cm ≤ water depth ≤ 50 cm) and deep water rice (DWR; water depth > 50 cm) during the kharif season. These three types represent most of the traditional rice-growing areas of India. The methodology was validated with the field data collected synchronously with the satellite passes. Classification results showed an overall accuracy of 98.5% (95.5% kappa coefficient) compared with a maximum-likelihood classifier (MLC) with an overall accuracy of 95.5% (84.2% of kappa coefficient) with 95% confidence interval. The relationship between field parameters, especially exposed plant height and water depth with SAR backscatter, was explored to design empirical models for each of the three rice classes. Significant relationships were observed in all the rice classes (coefficient of determination, R 2, value more than 0.85) even though they had similar growth profiles but varied with water depth. The two main conclusions drawn from this study are (i) the importance of multi-temporal SAR data for the classification of rice culture types based on water regimes and (ii) the advantages and flexibility of the knowledge-based classifier for classification of RADARSAT-1 data. However, being empirical, the approach needs modification according to the current rainfall pattern and rice-growing practice. [ABSTRACT FROM AUTHOR]

The geomorphologicalmap is an essential tool to performa proper urban planning inmountainous or hilly areas. In this paper amultidisciplinary approach to derive a 1:2000 geomorphologicalmap is described.The proposedmethodology consists of the integration between aerial photographs, acquired in 2003, and four datasets of Persistent Scatterer Interferometry (PSI) measures to update a pre-existing landslide inventory. The integrated data were used to achieve a validated geomorphological map by means of a geomorphological survey. The study area is located in southern Italy (Agrigento, Sicily). The city of Agrigento, included in the World Heritage List of UNESCO in 1997, is located on the Girgenti hill which is exposed on its northern side, to several landslide phenomena. The top of the hill is characterized by the presence of part of the cultural heritage of the city and is affected by rockfalls, rock topples and shallow-seated landslides, representing a serious risk for important historical buildings. The results demonstrate the validity of this method to achieve a suitable tool in landscape and cultural heritage management. [ABSTRACT FROM AUTHOR]

We have applied a non-parametric classifier (k nearest neighbour) to two calibrated orthogonal passes of airborne polarimetric synthetic aperture radar (POLSAR) image data over boreal forest for the purpose of discriminating canopy tree species of predefined stands. We found that a single classifier based on a single feature space (i.e. one set of POLSAR variables for all species) was less accurate than a hierarchical two-stage classifier that used different POLSAR variables for each species. We designed a two-stage classifier that first grouped stands into broad classes: pine, spruce and deciduous, and then classified each sample within the broad classes into individual species. We found that the most effective feature spaces had two or three dimensions. The two-stage classifier attained overall accuracies of between 60% and 75%. We provide a first use of an equivalency test applied to remote-sensing classification. We use Lloyd's test of equivalency to find equivalent classifiers and thus infer informative POLSAR variables. The POLSAR variables that were most informative varied between the two passes and between the various elements of the hierarchical classifier. For the initial three-class classifier the most informative POLSAR variables were the two circular polarization ratios, several of Touzi's Stokes vector variables, HHVV coherence, several texture measures such as the variance of several scattering coefficients and the order parameter of the K-distribution and characteristics of the polarization signature pedestal. These results demonstrate that C-band POLSAR has great potential for mapping boreal forest cover either on its own or in concert with other geospatial data. [ABSTRACT FROM AUTHOR]

A Ku-band rotating, fanbeam scatterometer (RFSCAT) will be used to measure the sea surface wind field from a polar-orbiting small satellite with a rotating, range-gated fanbeam radar scatterometer. The measurement principle and instrument are introduced in this article. An end-to-end simulation model is adopted to analyse and optimize the performances of the system, taking into account the radar observation conditions. Then two kinds of spatially coherent wind fields, namely a parallel wind field and a circular wind field, as well as an incoherently random wind are simulated to assess the system performances. The results show that with the chosen configurations, the performance for wind vector retrieval of the discussed scatterometer is roughly comparable to the pencil-beam radar scatterometers (e.g. SeaWinds on QuikSCAT), and exceeds it at some wind regimes over part of its swath. Particularly, it usually has a better performance for wind direction retrieval, though with a narrower swath. [ABSTRACT FROM AUTHOR]

Waves, winds and currents can be extracted from the Doppler power Spectra of HF Radar. Worldwide research has been done for this purpose during the last 4 decades. A new multi-frequency HF (MHF) radar is applied to enrich this study in a ten-day experiment completed in the East China Sea. The new MHF radar is designed to simultaneously work at four different frequencies at most between 7.5 MHz and 25 MHz. In this paper, we particularly focus on wave measurements, which were obtained both from MHF radar and wave buoy. A comparison of the two measurements is made, and the final result points out a potential and practical way to improve the performance of HF radar wave measuring. It is suggested that, when the fusion algorithm of multi-frequency data is robust enough, MHF radar can be widely used for ocean remote sensing. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration (AET) estimation using satellite sensors can improve water resources planningwater regulation in irrigated areas. This paper evaluates an AET estimation algorithm developed by integrating satellite remote sensingan environmental monitoring network in Oklahoma, USA for operational daily water management purpose. First, a surface energy balance evapotranspiration (ET) algorithm is implemented to estimate actual ET by integrating the twice-daily overpass of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor dataOklahoma's world-class environmental monitoring network—Mesonet—with 5-minute data acquisition in real time. Second, accuracy of the estimated ET is evaluated at the site scale using Ameriflux tower latent heat fluxMesonet site crop ET on daily, 8-dayseasonal basis. The results showed that MODIS/Mesonet-AET (MM-AET) estimation showed agreement with ground observations, with daily ET bias less than 15%seasonal bias less than 8%. Additionally, actual ET modelled from a water balance budget analysis in a heavily instrumented basin compares favourably (bias < 3%) with the MM-AET at catchment scales with an order of several hundreds square kilometres. This study demonstrates that (1) the MM-AET estimation is acceptable for daily actual ET estimation(2) it is feasible to implement the proposed MM-AET algorithm in real time for irrigational water resources management at the scale of irrigation projects in Oklahoma. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) from agricultural land is the most common form of water consumption in the North China Plain. Based on in situ measurements by an eddy covariance (EC) system, the present study tested the surface energy balance system (SEBS) model for estimating surface energy fluxes from a wheat/maize rotation cropland. Applicability of the SEBS model using Moderate Resolution Imaging Spectroradiometer (MODIS) land products to estimate ET was further validated. The model proved to be appropriate to measure heat flux during the wheat-growing season, but underestimated the sensible heat flux during the maize-growing season. Although the SEBS model performed better during the wheat-growing season, the relative error of the latent heat flux was within 20% in both wheat- and maize-growing seasons. Results showed that the SEBS model using remote sensing data could provide a reasonable estimate of the surface energy fluxes from an irrigated cropland in the North China Plain. [ABSTRACT FROM AUTHOR]

Land-cover studies based on optical remote sensing in regions which exhibit disorderly urban growth and quick-use conversion of farmland to non-farm usage face problems due to inaccurate discrimination of cover types and hence inaccurate extent estimations. The use of data in the visible and infrared areas of the electromagnetic spectrum for classifying crop types has been extensively explored, concluding that data acquisitions must be made during critical crop development periods. This raises a concern in Central Mexico where such periods coincide with important cloud coverage and where good estimates of the extent of agricultural areas and of particular crops are keenly sought by government agencies for planning purposes. Due to the interest in accurate and updated maps for this area, repeated studies have been carried out over a number of years by the National Institute of Research for Forestry, Agriculture and Livestock for the Ministry of Agriculture of Mexico. Taking into consideration the difficulties of acquiring and analysing data derived from optical sensors, the objective of this study was to assess the advantages of combining synthetic aperture radar (SAR) and optical remote sensing in producing more accurate maps. The study area covers 15 634 ha and is located in Central Mexico in a region where agricultural plots of varied sizes and forms are interspersed with rapid urbanization spaces. We investigated alternative supervised classification schemes combining the Radarsat-1 C-band with Landsat Enhanced Thematic Mapper Plus (ETM+) bands to estimate land cover distributions and assess the quality of results with field data. Then, we set forth and evaluated a methodology which applies data fusion of selected Landsat ETM+ bands and the C-radar band. The separation and similarities for vegetated and non-vegetated cover types depends on whether the selected agricultural crops are annual or perennial, and on whether there are bare soils present. This knowledge for the particular study area influenced the selection of dates for image take and analysis. Partial fused and non-fused land-cover maps were assessed for accuracy and were combined to obtain a final map. The results demonstrate that the combined utilization of optical and radar imagery yields useful land cover information and improved classification accuracy over those obtained using either type of image on its own. [ABSTRACT FROM AUTHOR]

A near-simultaneous satellite acquisition of a flood event on 12 December 2006 on the Dee River in Wales (UK) is used to illustrate the potential to develop standalone space-borne flood risk mapping techniques. Both the ERS-2 and ENVISAT satellites recorded the event close to flood peak and only 28 min apart. This unique opportunity enables the creation of a very rare but extremely useful observed data set for flood inundation studies. This letter illustrates how this unique set of space-borne radar images can be used for rapid flood risk mapping. An event-specific weighted hazard map was generated based on plausible flood area observations from an aggregation of widely applied image-processing techniques. This map can be further augmented to an event-specific fuzzy flood risk map by fusing the multi-algorithm ensemble map with vulnerability-weighted land cover vector data in a geographic information system (GIS) environment. The technique presented is fairly flexible and leads to a potentially useful data set for direct use in flood management. [ABSTRACT FROM AUTHOR]

Space-borne synthetic aperture radar interferometry (INSAR) is a well-known widely used remote sensing technique to get precise (to the millimeter) surface deformation measurements on large areas (thousands of square kilometers) and high spatial density of measurement points (hundreds per square kilometer). A short review of the INSAR basics is dedicated to the readers who are not INSAR specialists. Then, an analysis of the improvement of ground motion measurement offered by multiple repeated space-borne SAR observations gathered by the new generation of satellites is given and compared with ground-based radar results. Examples from Cosmo-SkyMed and TERRASAR-X are shown and compared with C-band or L-band systems. Finally, the impact of the forthcoming C-band satellites like Sentinel 1 A, B is discussed. [ABSTRACT FROM AUTHOR]

A modified scattering model-based speckle filter (SMBSF) based on the spatial proximity principle was applied to the analysis of phased array type L-band synthetic aperture radar (PALSAR) polarimetric data in the coastal environment of North Carolina, USA. The modified filter preserved polarimetric characteristics and further reduced speckle noise qualitatively and quantitatively. Classification accuracy using the SAR data filtered by the modified filter was improved, especially for the forest class. [ABSTRACT FROM AUTHOR]

This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings. [ABSTRACT FROM AUTHOR]

Precise global/regional land cover mapping is of fundamental importance in studies of land surface processes and modelling. Quantitative assessments of the map quality and classification accuracy for existing land cover maps will help to improve accuracy in future land cover mapping. We compare and evaluate four land cover datasets over China. The datasets include the Version 2 global land cover dataset of IGBP, MODIS land cover map 2001, a global land cover map produced by the University of Maryland, and the land cover map produced by the global land cover for the year 2000 (GLC 2000) project coordinated by the Global Vegetation Monitoring Unit of the European Commission Joint Research Centre. The four maps used different classification systems, which made the comparison difficult. So we first aggregated these maps by reclassifying them using a unified legend system. A large-scale, i.e. 1:100 000 land cover map of China was used as the reference data to validate the four maps. The results show that the GLC2000 land cover map represents the highest accuracy. However, it has obvious local labelling errors and a zero labelling accuracy for the wetland type. The MODIS land cover map ranks second for type area consistency and third for sub-fraction overall accuracy compared with reference data, which may be affected by the local labelling error. The IGBP land cover map has good labelling accuracy, although it has a local labelling error and third consistency for type area. The labelling accuracy and type area consistency for the reference data of UMd land cover map is low. We conclude that the accuracies of all the datasets cannot meet the requirements of land surface modelling. For the reference data, i.e. the 1:100 000 land cover map, the classification system needs to be transferred to a well recognized one that has been used commonly in land surface modelling. In addition, we propose an information fusion strategy to produce a more accurate land cover map of China whose classification system should be compatible with the well-accepted classification system used in land surface modelling. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of accounting for temporal contextual information in order to improve the accuracy of land-cover classification in summer with Synthetic Aperture Radar (SAR) data. Bi-temporal multi-sensor datasets collected in the Nonsan area of Korea were used to illustrate this approach. Multi-sensor data, including Japanese Earth Resources Satellite (JERS)-1 Optical Sensor (OPS) data acquired in April, and three different SAR sensor datasets from European Resource Satellite (ERS)-2, JERS-1, and Radarsat-1 obtained in the following July, were used for supervised classification in July. By comparing the classification result in April with a training set in July, transition probabilities between land-cover classes in the April-July period were empirically estimated and regarded as the temporal contextual information. A tau model is applied as a main integration methodology to combine multiple SAR data and the temporal contextual information. From the evaluation of the classification results in terms of accuracy statistics, using multiple SAR sensor data showed an increase of about 29% in overall accuracy compared with the case of single SAR sensor data. The incorporation of temporal contextual information into scattering information greatly contributed to a significant improvement of about 25% in overall accuracy over multiple SAR sensor integration only, and showed the best discrimination capability. [ABSTRACT FROM AUTHOR]

This paper addresses the crucial need for a non-invasive method for precisely detecting the subsurface defects in wooden logs. The method utilises ground penetrating radar (GPR) technology which has the potential to be implemented in real time in saw mills in the near future. Internal defects such as knots, decays and embedded metals are of great concern for lumber production. Nondestructive scanning of logs using GPR to detect defects in logs prior to sawing can greatly increase the productivity and yield of high value defect-free lumber by optimal orientation of the saw blade, and prevent damage to saw blade from embedded metals. Also, GPR offers high speed scanning capability which is needed for future online implementation in saw mills. In this research, the scanned GPR data have been analysed to investigate the capability of GPR to detect subsurface defects such as metals, decays and knots. This paper presents the comprehensive results from GPR scanning of several wooden logs and shows comparisons with actual condition inside the log observed after sawing the log. The advantages of the GPR technique, experimental setup and data processing used for detection of subsurface defects in logs are explained in detail. This study has shown that GPR can be used to accurately identify the subsurface defects such as knots, decays and metallic nails inside the logs which are not visible from outside observations. The results show that GPR can be a very promising technique for future online implementation in saw mills. [ABSTRACT FROM AUTHOR]

Forest age class is essential to make forest policy and management decisions. Remote sensing is a viable alternative to time consuming field and aerial investigations to determine forest age. In this study, 76 stands of nine 20-year age classes of western red cedar from the interior of British Columbia, Canada were selected to test the ability of pan-sharpened SPOT-5 imagery to classify stand age. For each stand, a Structural Complexity Index (SCI) was calculated using principal components analysis of stand-level variables (diameter at breast height, crown diameter, basal area, and stem density). To further increase classification accuracy, three window sizes (5×5, 11×11 and 25×25) of first-order and second-order statistical measures of texture were calculated. Stepwise discriminant analysis and multivariate regression were used to determine the best explanatory model of forest age and SCI using the spectral and textural data. The most accurate model used a combination of these measures of texture and the SCI to predict age class. [ABSTRACT FROM AUTHOR]

High spatial resolution topographic data are crucial as a prerequisite for evaluating and modelling volcanic hazards. Remote sensing now provides some of the best methods of retrieving digital elevation models (DEMs) over extensive volcanic regions. Here we compare the advantages and limitations of Shuttle Radar Topography Mission (SRTM) DEMs, derived from radar interferometry, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, derived from digital photogrammetry. Applications are presented for Mauna Kea (MK), Hawaii and Oldoinyo Lengai (OL), Tanzania. We quantitatively assess the accuracy of the respective DEMs to document the size of moderate-sized volcanic features. ASTER DEM accuracy depends on spectral contrast within the image and on the availability of high-quality ground control points. Unlike the ASTER DEMs, which are user-derived, processed SRTM DEMs are provided without estimations of the vertical accuracy for each scene, and the end-user has no control over the processing method. From comparison with a 10-m spatial resolution DEM derived from 1 : 24 000 scale topographic maps of MK, we estimate root mean square errors at 8, 10 and 13 m for SRTM 30-m, SRTM 90-m and ASTER 30-m DEMs, respectively. For scoria cones (<200 m high, <2 km basal diameter), SRTM 30-m, SRTM 90-m and ASTER 30-m DEMs underestimate cone height by 9.5, 27 and 14%, respectively, mostly because of the averaging effect of decreasing spatial resolution. For height estimations of volcanic features higher than ∼100 m in the OL region, all of the DEMs tested were found to be consistent. [ABSTRACT FROM AUTHOR]

In this Letter, a method is proposed for interferometric synthetic aperture radar (SAR) phase unwrapping based on image segmentation. An auxiliary phase map was generated and the interferometric phase was divided into many connected regions without discontinuity by separation and combination operations. A local optimal strategy was used to combine these regions with as few discontinuities as possible. The coherence map is not necessary. The authors compared the proposed method with Goldstein's branch-cut algorithm (BC) and the weighted multi-grid algorithm (WMG), demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The estimation of geophysical parameters from Synthetic Aperture Radar (SAR) data necessitates well-calibrated sensors with good radiometric precision. In this paper, the radiometric calibration of the new Advanced Synthetic Aperture Radar (ASAR-ENVISAT) sensor was assessed by comparing ASAR data with ERS-2 and RADARSAT-1 SAR data. By analysing the difference between radar signals of forest stands, the results show differences of varying importance between the ASAR on the one hand, and the ERS-2 and the RADARSAT-1 on the other. For recent data acquired at the end of 2005, the difference varies from -0.72 to +0.72 dB, with temporal variations that can reach 1.1 dB. For older data acquired in 2003 and 2004, we observe a sharp decrease in the radar signal in the range direction, which can attain 3.5 dB. The use of revised calibration constants provided recently by the European Space Agency (ESA) significantly improves the results of the radiometric calibration, where the difference between the ASAR and the other SARs will be lower than 0.5 dB. [ABSTRACT FROM AUTHOR]

Brazilian Amazon Forest biomes are presently under intensive land cover conversion from natural vegetation to agriculture. Timely detection of recent deforestation through orbital remote sensing is a critical requirement for an operational land cover monitoring system in order to provide information to the regulatory systems and decision makers. Optical images present drawbacks for operation in the moist tropics and synthetic aperture radar (SAR) data are a real alternative. The feasibility of using multipolarized L-band images simulating the Multi-Application Purpose SAR (MAPSAR) satellite was examined for the detection of recent deforestation in the Tapajos region. The discrimination of recent deforestation from other land cover classes was evaluated through a quantitative analysis based on Jeffreys-Matusitas (JM) distances derived from training samples using amplitude values and supported by field survey. The investigation confirmed the possibility of the discrimination of recently deforested classes from other classes based on the L-band images as proposed in the MAPSAR. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of multitemporal/polarization C-band SAR data for land-cover classification. Multitemporal Radarsat-1 data with HH polarization and ENVISAT ASAR data with VV polarization acquired in the Yedang plain, Korea are used for the classification of typical five land-cover classes in an agricultural area. The presented methodologies consist of two analytical stages: one for feature extraction and the other for classification based on the combination of features. Both a traditional SAR signal property analysis-based approach and principal-component analysis (PCA) are applied in the feature extraction stage. Special concerns are in the interpretation of each principal component by using principal-component loading. The tau model applied as a decision-level fusion methodology can provide a formal framework in which the posteriori probabilities derived from different sensor data can be combined. From the case study results, the combination of PCA-based features showed improved classification accuracy for both Radarsat-1 and ENVISAT ASAR data, as compared with the traditional SAR signal property analysis-based approach. The integration of PCA-based features based on multiple polarization (i.e. HH from Radarsat-1, and both VV and VH from ENVISAT ASAR) and different incidence angles contributed to a significant improvement of discrimination capability for dry fields which could not be properly classified by using only Radarsat-1 or ENVISAT ASAR data, and thus showed the best classification accuracy. The results of this case study indicate that the use of multiple polarization SAR data with a proper feature extraction stage would improve classification accuracy in multitemporal SAR data classification, although further consideration should be given to the polarization and incidence angle dependency of complex land-cover classes through more experiments. [ABSTRACT FROM AUTHOR]

A huge amount of various remote sensing data have been acquired and archived during recent years. Information extraction from these data is still a challenging task, for example using the data classification. We propose the Bayesian approach to image classification using information fusion from different sources of data. The method of classification is based on the three processing steps: (1) information fission by feature extraction, (2) data and dimensionality reduction by unsupervised clustering, and (3) supervised classification with information fusion. The potential of the classification method is illustrated by the examples on ERS-1/2 Tandem interferometric synthetic aperture radar data. The continuity of tandem pairs of SAR images is ensured by already started or future missions such as TerraSAR-X, TanDEM-X, and COSMO-SkyMed. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanner (TLS) and interferometric synthetic aperture radar (InSAR) allow the acquisition of data on an observed surface with high spatial sampling rate. The data provided by TLS observation of a landslide ground surface can be used to generate a very detailed digital model of this surface, and multitemporal observations with TLS or continuous or multitemporal observation with InSAR can provide a reliable displacement map. In order to acquire useful information about the analogies, differences, and capabilities, as well as limitations of these techniques, a joint experimentation of TLS and InSAR was performed over two years in various sites in the Italian Alps. The results have indicated that these techniques can provide high-quality data, can be very useful in the monitoring intended for the mitigation of hydrogeological risk in a wide range of cases, and must be supported by a topographical georeferenced network. [ABSTRACT FROM AUTHOR]

Savannas constitute approximately 40% of the tropics and occur primarily in developing countries. Their net primary productivity rates are third only to tropical and temperate forests. Due to their high carbon sequestration potential savannas are likely to become important areas under Kyoto Protocol initiatives for removing CO2 from the atmosphere. They are, however, prone to human pressure. It is therefore important that accurate methods for the estimation of savanna biomass are developed which can be repeated for monitoring purposes. The paper examines research conducted to evaluate the use of Synthetic Aperture Radar (SAR) for estimating the biomass of the woody vegetation within a savanna in Belize, Central America. Results show that established SAR methods for estimating biomass for closed canopy forest are not directly transferable to the non-continuous cover woodlands that frequently occur in savanna areas. This has important implications for Earth observation satellites launched for the purpose of global biomass estimation. [ABSTRACT FROM AUTHOR]

The present paper gives an account of potential of Environment Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data in forest parameter retrieval and forest type classification over deciduous forests of Tadoba Andhari Tiger Reserve (TATR), central India. Ground data on phyto-sociology and Leaf Area Index (LAI) over the study area was collected in 23 sampling points (20m×20m) over the study area. Phyto-sociological data collected over the study area was used to compute plot-wise biometric parameters like basal area, volume, stem density and dominant height. ENVISAT ASAR data covering the study area, pertaining to 24 November 2005, has been geo-referenced and digital number (DN) values were converted to radar backscatter values. Regression analysis between backscatter and the retrieved biometric variables has been done to explain the relationships between SAR backscatter and forest parameters. Analysis showed a significant correlation between backscatter and biometric parameters and backscatter values typically increased with increase in basal area, volume, stem density and dominant height. The scatter observed between ASAR backscatter and stem density, basal area and dominant height suggested limitation of C-band data in estimating biometric variables in heterogeneous forest systems. Further, ASAR data was used in conjunction with Indian Remote sensing Satellite (IRS-P6) - Linear Imaging Self Scanner (LISS) III data of 16 October 2004 to classify the study area into different land use/land cover (LU/LC) classes. Various texture and adaptive filters were applied on ASAR image to reduce speckle noise and enhance image features. An attempt is made to merge ASAR image with LISS-III to enhance feature discrimination. Training sets corresponding to the ground data have been used to derive confusion matrices for the ASAR and LISS-III images. Results suggested better discrimination of vegetation types in the merged data suggesting the possible use of ASAR data in forest type discrimination. [ABSTRACT FROM AUTHOR]

A feasibility study is prepared for creating a top-of-canopy Digital Elevation Model (DEM) based on Radarsat data from the tropical rain forest regions of Malaysia. Through a case study, the accuracy of the generated DEM is verified, showing reasonable accuracy. [ABSTRACT FROM AUTHOR]

In this letter, a novel method is proposed for estimating the median droplet diameter by using the Ka- and W-band dual-wavelength differences of the mean Doppler velocity and the Doppler spectrum width. The estimated median diameter together with the dual-wavelength ratio of the radar reflectivity can be effectively used to measure and compensate the attenuation in precipitation or clouds. The estimate of the median diameter is not affected by the attenuation and does not require an accurate radar calibration. Furthermore, the vertical air flows and the turbulent broadening of the Doppler spectra have no influence on the estimate. [ABSTRACT FROM AUTHOR]

Results from the Shuttle Radar Topography Mission (SRTM) are presented. The SRTM C-band and X-band digital elevation models (DEMs) are evaluated with regard to elevation accuracies over agricultural fields, forest areas and man-made features in Norway. High-resolution digital maps and satellite images are used as background data. In general, many terrain details can be observed in the SRTM elevation datasets. The elevation accuracy (90% confidence level) of the two SRTM systems is estimated to less than 6.5 m for open agricultural fields and less than 11 m considering all land surface covers. This is better than specifications. Analysis of dense Norwegian forest stands shows that the SRTM system will produce elevation data that are as much as 15 m higher than the ground surface. The SRTM DEM products will therefore partly indicate canopy elevations in forested areas. We also show that SRTM data can be used to update older DEMs obtained from other sources, as well as estimating the volume of rock removed from large man-made gravel pits. [ABSTRACT FROM AUTHOR]

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C-band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low-density vegetation, using low-incidence-angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high-density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal- and vertical-polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual-polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture. [ABSTRACT FROM AUTHOR]

Several ocean colour (OC) Earth observation (EO) sensors are presently collecting data on an operational basis, including the Envisat MERIS sensor. This study aims at quantifying the differences in performance of the ocean colour EO sensors MERIS, MODIS/Aqua, and SeaWiFS for coastal monitoring and for the particular case of coastal algal bloom situations. The standard chlorophyll a, and radiance and reflectance products from the three sensors have been processed and compared for data acquired during the development of an early-spring algal bloom in 2004 in the eastern North Sea region. The study shows a high level of consistency between the Case 1 water Chl a products for all sensors. We conclude that the use of MERIS Chl a and other relevant products enable continued and possibly improved performance of an existing system for harmful algal bloom (HAB) detection and monitoring which up to December 2004 has been based on SeaWiFS. [ABSTRACT FROM AUTHOR]

The capability to retrieve cloud base heights was developed under the US National Polar‐orbiting Operational Environmental Satellite System (NPOESS) programme as one of 27 data products to be created from data collected by the Visible Infrared Imager Radiometer Suite (VIIRS). First launch of the VIIRS sensor, which is the high‐resolution Earth imager of the NPOESS programme, comes on National Aeronautics & Space Administration's (NASA) NPOESS Preparatory Project (NPP). In preparation for this launch, extensive testing of the VIIRS cloud algorithms was completed to verify that product performance will satisfy system requirements before the cloud algorithms were hosted in the NPOESS ground processing centre. The approach taken to retrieve cloud base height converts cloud optical properties into a geometric thickness which is then subtracted from the cloud top height. Performance of the cloud base height algorithms has been verified recently using MODIS data, together with temporarily and spatial coincident observations of cloud thickness values made with the millimetre cloud radar operated at the US Department of Energy Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma. Of particular significance is the clear demonstration that both cloud optical properties and cloud base heights are retrieved accurately during night‐time conditions with the VIIRS algorithms, since neither of these products is currently produced by the NASA EOS programme. Based upon these analyses, the VIIRS cloud algorithms are expected to satisfy NPOESS requirements, making VIIRS the first operational satellite sensor capable of retrieving three‐dimensional cloud fields. [ABSTRACT FROM AUTHOR]