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At least 73 records · Page 4Linked to original sources
USGS2008

Numerical modeling of higher order magnetic moments in UXO discrimination

The surface magnetic anomaly observed in unexploded ordnance (UXO) clearance is mainly dipolar, and consequently, the dipole is the only magnetic moment regularly recovered in UXO discrimination. The dipole moment contains information about the intensity of magnetization but lacks information about the shape of the target. In contrast, higher order moments, such as quadrupole and octupole, encode asymmetry properties of the magnetization distribution within the buried targets. In order to improve our understanding of magnetization distribution within UXO and non-UXO objects and to show its potential utility in UXO clearance, we present a numerical modeling study of UXO and related metallic objects. The tool for the modeling is a nonlinear integral equation describing magnetization within isolated compact objects of high susceptibility. A solution for magnetization distribution then allows us to compute the magnetic multipole moments of the object, analyze their relationships, and provide a depiction of the anomaly produced by different moments within the object. Our modeling results show the presence of significant higher order moments for more asymmetric objects, and the fields of these higher order moments are well above the noise level of magnetic gradient data. The contribution from higher order moments may provide a practical tool for improved UXO discrimination. ?? 2008 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2008

The effects of layers in dry snow on its passive microwave emissions using dense media radiative transfer theory based on the quasicrystalline approximation (QCA/DMRT)

A model for the microwave emissions of multilayer dry snowpacks, based on dense media radiative transfer (DMRT) theory with the quasicrystalline approximation (QCA), provides more accurate results when compared to emissions determined by a homogeneous snowpack and other scattering models. The DMRT model accounts for adhesive aggregate effects, which leads to dense media Mie scattering by using a sticky particle model. With the multilayer model, we examined both the frequency and polarization dependence of brightness temperatures (Tb's) from representative snowpacks and compared them to results from a single-layer model and found that the multilayer model predicts higher polarization differences, twice as much, and weaker frequency dependence. We also studied the temporal evolution of Tb from multilayer snowpacks. The difference between Tb's at 18.7 and 36.5 GHz can be S K lower than the single-layer model prediction in this paper. By using the snowpack observations from the Cold Land Processes Field Experiment as input for both multi- and single-layer models, it shows that the multilayer Tb's are in better agreement with the data than the single-layer model. With one set of physical parameters, the multilayer QCA/DMRT model matched all four channels of Tb observations simultaneously, whereas the single-layer model could only reproduce vertically polarized Tb's. Also, the polarization difference and frequency dependence were accurately matched by the multilayer model using the same set of physical parameters. Hence, algorithms for the retrieval of snowpack depth or water equivalent should be based on multilayer scattering models to achieve greater accuracy. ?? 2008 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2009

SSTL UK-DMC SLIM-6 data quality assessment

Satellite data from the Surrey Satellite Technology Limited (SSTL) United Kingdom (UK) Disaster Monitoring Constellation (DMC) were assessed for geometric and radiometric quality. The UK-DMC Surrey Linear Imager 6 (SLIM-6) sensor has a 32-m spatial resolution and a ground swath width of 640 km. The UK-DMC SLIM-6 design consists of a three-band imager with green, red, and near-infrared bands that are set to similar bandpass as Landsat bands 2, 3, and 4. The UK-DMC data consisted of imagery registered to Landsat orthorectified imagery produced from the GeoCover program. Relief displacements within the UK-DMC SLIM-6 imagery were accounted for by using global 1-km digital elevation models available through the Global Land One-km Base Elevation (GLOBE) Project. Positional accuracy and relative band-to-band accuracy were measured. Positional accuracy of the UK-DMC SLIM-6 imagery was assessed by measuring the imagery against digital orthophoto quadrangles (DOQs), which are designed to meet national map accuracy standards at 1 : 24 000 scales; this corresponds to a horizontal root-mean-square accuracy of about 6 m. The UK-DMC SLIM-6 images were typically registered to within 1.0-1.5 pixels to the DOQ mosaic images. Several radiometric artifacts like striping, coherent noise, and flat detector were discovered and studied. Indications are that the SSTL UK-DMC SLIM-6 data have few artifacts and calibration challenges, and these can be adjusted or corrected via calibration and processing algorithms. The cross-calibration of the UK-DMC SLIM-6 and Landsat 7 Enhanced Thematic Mapper Plus was performed using image statistics derived from large common areas observed by the two sensors.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2009

Atmospheric correction at AERONET locations: A new science and validation data set

This paper describes an Aerosol Robotic Network (AERONET)-based Surface Reflectance Validation Network (ASRVN) and its data set of spectral surface bidirectional reflectance and albedo based on Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and AQUA data. The ASRVN is an operational data collection and processing system. It receives 50 ?? 50 km 2 ; subsets of MODIS level 1B (L1B) data from MODIS adaptive processing system and AERONET aerosol and water-vapor information. Then, it performs an atmospheric correction (AC) for about 100 AERONET sites based on accurate radiative-transfer theory with complex quality control of the input data. The ASRVN processing software consists of an L1B data gridding algorithm, a new cloud-mask (CM) algorithm based on a time-series analysis, and an AC algorithm using ancillary AERONET aerosol and water-vapor data. The AC is achieved by fitting the MODIS top-of-atmosphere measurements, accumulated for a 16-day interval, with theoretical reflectance parameterized in terms of the coefficients of the Li SparseRoss Thick (LSRT) model of the bidirectional reflectance factor (BRF). The ASRVN takes several steps to ensure high quality of results: 1) the filtering of opaque clouds by a CM algorithm; 2) the development of an aerosol filter to filter residual semitransparent and subpixel clouds, as well as cases with high inhomogeneity of aerosols in the processing area; 3) imposing the requirement of the consistency of the new solution with previously retrieved BRF and albedo; 4) rapid adjustment of the 16-day retrieval to the surface changes using the last day of measurements; and 5) development of a seasonal backup spectral BRF database to increase data coverage. The ASRVN provides a gapless or near-gapless coverage for the processing area. The gaps, caused by clouds, are filled most naturally with the latest solution for a given pixel. The ASRVN products include three parameters of the LSRT model (kL, kG, and kV), surface albedo, normalized BRF (computed for a standard viewing geometry, VZA = 0, SZA = 45??), and instantaneous BRF (or one-angle BRF value derived from the last day of MODIS measurement for specific viewing geometry) for the MODIS 500-m bands 17. The results are produced daily at a resolution of 1 km in gridded format. We also provide a cloud mask, a quality flag, and a browse bitmap image. The ASRVN data set, including 6 years of MODIS TERRA and 1.5 years of MODIS AQUA data, is available now as a standard MODIS product (MODASRVN) which can be accessed through the Level 1 and Atmosphere Archive and Distribution System website ( http://ladsweb.nascom.nasa.gov/data/search.html). It can be used for a wide range of applications including validation analysis and science research. ?? 2006 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2011

Modeling PSInSAR time series without phase unwrapping

In this paper, we propose a least-squares-based method for multitemporal synthetic aperture radar interferometry that allows one to estimate deformations without the need of phase unwrapping. The method utilizes a series of multimaster wrapped differential interferograms with short baselines and focuses on arcs at which there are no phase ambiguities. An outlier detector is used to identify and remove the arcs with phase ambiguities, and a pseudoinverse of the variance-covariance matrix is used as the weight matrix of the correlated observations. The deformation rates at coherent points are estimated with a least squares model constrained by reference points. The proposed approach is verified with a set of simulated data.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2012

Development of the Landsat Data Continuity Mission cloud-cover assessment algorithms

The upcoming launch of the Operational Land Imager (OLI) will start the next era of the Landsat program. However, the Automated Cloud-Cover Assessment (CCA) (ACCA) algorithm used on Landsat 7 requires a thermal band and is thus not suited for OLI. There will be a thermal instrument on the Landsat Data Continuity Mission (LDCM)-the Thermal Infrared Sensor-which may not be available during all OLI collections. This illustrates a need for CCA for LDCM in the absence of thermal data. To research possibilities for full-resolution OLI cloud assessment, a global data set of 207 Landsat 7 scenes with manually generated cloud masks was created. It was used to evaluate the ACCA algorithm, showing that the algorithm correctly classified 79.9% of a standard test subset of 3.95 109 pixels. The data set was also used to develop and validate two successor algorithms for use with OLI data-one derived from an off-the-shelf machine learning package and one based on ACCA but enhanced by a simple neural network. These comprehensive CCA algorithms were shown to correctly classify pixels as cloudy or clear 88.5% and 89.7% of the time, respectively.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2012

Landsat-7 ETM+: 12 years on-orbit reflective-band radiometric performance

The Landsat-7 ETM+ sensor has been operating on orbit for more than 12 years, and characterizations of its performance have been ongoing over this period. In general, the radiometric performance of the instrument has been remarkably stable: 1) noise performance has degraded by 2% or less overall, with a few detectors displaying step changes in noise of 2% or less; 2) coherent noise frequencies and magnitudes have generally been stable, though the within-scan amplitude variation of the 20 kHz noise in bands 1 and 8 disappeared with the failure of the scan line corrector and a new similar frequency noise (now about 18 kHz) has appeared in two detectors in band 5 and increased in magnitude with time; 3) bias stability has been better than 0.25 DN out of a normal value of 15 DN in high gain; 4) relative gains, the differences in response between the detectors in the band, have generally changed by 0.1% or less over the mission, with the exception of a few detectors with a step response change of 1% or less; and 5) gain stability averaged across all detectors in a band, which is related to the stability of the absolute calibration, has been more stable than the techniques used to measure it. Due to the inability to confirm changes in the gain (beyond a few detectors that have been corrected back to the band average), ETM+ reflective band data continues to be calibrated with the prelaunch measured gains. In the worst case, some bands may have changed as much as 2% in uncompensated absolute calibration over the 12 years.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2012

Landsat 4 Thematic Mapper calibration update

The Landsat 4 Thematic Mapper (TM) collected imagery of the Earth's surface from 1982 to 1993. Although largely overshadowed by Landsat 5 which was launched in 1984, Landsat 4 TM imagery extends the TM-based record of the Earth back to 1982 and also substantially supplements the image archive collected by Landsat 5. To provide a consistent calibration record for the TM instruments, Landsat 4 TM was cross-calibrated to Landsat 5 using nearly simultaneous overpass imagery of pseudo-invariant calibration sites (PICS) in the time period of 1988-1990. To determine if the radiometric gain of Landsat 4 had changed over its lifetime, time series from two PICS locations (a Saharan site known as Libya 4 and a site in southwest North America, commonly referred to as the Sonoran Desert site) were developed. The results indicated that Landsat 4 had been very stable over its lifetime, with no discernible degradation in sensor performance in all reflective bands except band 1. In contrast, band 1 exhibited a 12% decay in responsivity over the lifetime of the instrument. Results from this paper have been implemented at USGS EROS, which enables users of Landsat TM data sets to obtain consistently calibrated data from Landsat 4 and 5 TM as well as Landsat 7 ETM+ instruments.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2013

Applications of spectral band adjustment factors (SBAF) for cross-calibration

To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface acquired from multiple spaceborne imaging sensors. However, an integrated global observation framework requires an understanding of how land surface processes are seen differently by various sensors. This is particularly true for sensors acquiring data in spectral bands whose relative spectral responses (RSRs) are not similar and thus may produce different results while observing the same target. The intrinsic offsets between two sensors caused by RSR mismatches can be compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of the two sensors. The motivation of this work comes from the need to compensate the spectral response differences of multispectral sensors in order to provide a more accurate cross-calibration between the sensors. In this paper, radiometric cross-calibration of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors was performed using near-simultaneous observations over the Libya 4 pseudoinvariant calibration site in the visible and near-infrared spectral range. The RSR differences of the analogous ETM+ and MODIS spectral bands provide the opportunity to explore, understand, quantify, and compensate for the measurement differences between these two sensors. The cross-calibration was initially performed by comparing the top-of-atmosphere (TOA) reflectances between the two sensors over their lifetimes. The average percent differences in the long-term trends ranged from $-$5% to $+$6%. The RSR compensated ETM+ TOA reflectance (ETM+$^{ast}$) measurements were then found to agree with MODIS TOA reflectance to within 5% for all bands when Earth Observing-1 Hy- erion hyperspectral data were used to produce the SBAFs. These differences were later reduced to within 1% for all bands (except band 2) by using Environmental Satellite Scanning Imaging Absorption Spectrometer for Atmospheric Cartography hyperspectral data to produce the SBAFs.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2013

Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study

Cross-calibration of satellite sensors permits the quantitative comparison of measurements obtained from different Earth Observing (EO) systems. Cross-calibration studies usually use simultaneous or near-simultaneous observations from several spaceborne sensors to develop band-by-band relationships through regression analysis. The investigation described in this paper focuses on evaluation of the uncertainties inherent in the cross-calibration process, including contributions due to different spectral responses, spectral resolution, spectral filter shift, geometric misregistrations, and spatial resolutions. The hyperspectral data from the Environmental Satellite SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY and the EO-1 Hyperion, along with the relative spectral responses (RSRs) from the Landsat 7 Enhanced Thematic Mapper (TM) Plus and the Terra Moderate Resolution Imaging Spectroradiometer sensors, were used for the spectral uncertainty study. The data from Landsat 5 TM over five representative land cover types (desert, rangeland, grassland, deciduous forest, and coniferous forest) were used for the geometric misregistrations and spatial-resolution study. The spectral resolution uncertainty was found to be within 0.25%, spectral filter shift within 2.5%, geometric misregistrations within 0.35%, and spatial-resolution effects within 0.1% for the Libya 4 site. The one-sigma uncertainties presented in this paper are uncorrelated, and therefore, the uncertainties can be summed orthogonally. Furthermore, an overall total uncertainty was developed. In general, the results suggested that the spectral uncertainty is more dominant compared to other uncertainties presented in this paper. Therefore, the effect of the sensor RSR differences needs to be quantified and compensated to avoid large uncertainties in cross-calibration results.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2013

Assessment of spectral band impact on intercalibration over desert sites using simulation based on EO-1 Hyperion data

Since the beginning of the 1990s, stable desert sites have been used for the calibration monitoring of many different sensors. Many attempts at sensor intercalibration have been also conducted using these stable desert sites. As a result, site characterization techniques and the quality of intercalibration techniques have gradually improved over the years. More recently, the Committee on Earth Observation Satellites has recommended a list of reference pseudo-invariant calibration sites for frequent image acquisition by multiple agencies. In general, intercalibration should use well-known or spectrally flat reference. The reflectance profile of desert sites, however, might not be flat or well characterized (from a fine spectral point of view). The aim of this paper is to assess the expected accuracy that can be reached when using desert sites for intercalibration. In order to have a well-mastered estimation of different errors or error sources, this study is performed with simulated data from a hyperspectral sensor. Earth Observing-1 Hyperion images are chosen to provide the simulation input data. Two different cases of intercalibration are considered, namely, Landsat 7 Enhanced Thematic Mapper Plus with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Environmental Satellite MEdium Resolution Imaging Spectrometer (MERIS) with Aqua MODIS. The simulation results have confirmed that intercalibration accuracy of 1% to 2% can be achieved between sensors, provided there are a sufficient number of available measurements. The simulated intercalibrations allow explaining results obtained during real intercalibration exercises and to establish some recommendations for the use of desert sites for intercalibration.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2013

Evaluation of ISCCP multisatellite radiance calibration for geostationary imager visible channels using the moon

Since 1983, the International Satellite Cloud Climatology Project (ISCCP) has collected Earth radiance data from the succession of geostationary and polar-orbiting meteorological satellites operated by weather agencies worldwide. Meeting the ISCCP goals of global coverage and decade-length time scales requires consistent and stable calibration of the participating satellites. For the geostationary imager visible channels, ISCCP calibration provides regular periodic updates from regressions of radiances measured from coincident and collocated observations taken by Advanced Very High Resolution Radiometer instruments. As an independent check of the temporal stability and intersatellite consistency of ISCCP calibrations, we have applied lunar calibration techniques to geostationary imager visible channels using images of the Moon found in the ISCCP data archive. Lunar calibration enables using the reflected light from the Moon as a stable and consistent radiometric reference. Although the technique has general applicability, limitations of the archived image data have restricted the current study to Geostationary Operational Environmental Satellite and Geostationary Meteorological Satellite series. The results of this lunar analysis confirm that ISCCP calibration exhibits negligible temporal trends in sensor response but have revealed apparent relative biases between the satellites at various levels. However, these biases amount to differences of only a few percent in measured absolute reflectances. Since the lunar analysis examines only the lower end of the radiance range, the results suggest that the ISCCP calibration regression approach does not precisely determine the intercept or the zero-radiance response level. We discuss the impact of these findings on the development of consistent calibration for multisatellite global data sets.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2013

Absolute radiometric calibration of Landsat using a pseudo invariant calibration site

Pseudo invariant calibration sites (PICS) have been used for on-orbit radiometric trending of optical satellite systems for more than 15 years. This approach to vicarious calibration has demonstrated a high degree of reliability and repeatability at the level of 1-3% depending on the site, spectral channel, and imaging geometries. A variety of sensors have used this approach for trending because it is broadly applicable and easy to implement. Models to describe the surface reflectance properties, as well as the intervening atmosphere have also been developed to improve the precision of the method. However, one limiting factor of using PICS is that an absolute calibration capability has not yet been fully developed. Because of this, PICS are primarily limited to providing only long term trending information for individual sensors or cross-calibration opportunities between two sensors. This paper builds an argument that PICS can be used more extensively for absolute calibration. To illustrate this, a simple empirical model is developed for the well-known Libya 4 PICS based on observations by Terra MODIS and EO-1 Hyperion. The model is validated by comparing model predicted top-of-atmosphere reflectance values to actual measurements made by the Landsat ETM+ sensor reflective bands. Following this, an outline is presented to develop a more comprehensive and accurate PICS absolute calibration model that can be Système international d'unités (SI) traceable. These initial concepts suggest that absolute calibration using PICS is possible on a broad scale and can lead to improved on-orbit calibration capabilities for optical satellite sensors.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2014

A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest

Cross comparison of satellite-derived land surface phenology (LSP) and ground measurements is useful to ensure the relevance of detected seasonal vegetation change to the underlying biophysical processes. While standard 16-day and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI)-based springtime LSP has been evaluated in previous studies, it remains unclear whether LSP with enhanced temporal and spatial resolutions can capture additional details of ground phenology. In this paper, we compared LSP derived from 500-m daily MODIS and 30-m MODIS-Landsat fused VI data with landscape phenology (LP) in a northern U.S. mixed forest. LP was previously developed from intensively observed deciduous and coniferous tree phenology using an upscaling approach. Results showed that daily MODIS-based LSP consistently estimated greenup onset dates at the study area (625 m × 625 m) level with 4.48 days of mean absolute error (MAE), slightly better than that of using 16-day standard VI (4.63 days MAE). For the observed study areas, the time series with increased number of observations confirmed that post-bud burst deciduous tree phenology contributes the most to vegetation reflectance change. Moreover, fused VI time series demonstrated closer correspondences with LP at the community level (0.1-20 ha) than using MODIS alone at the study area level (390 ha). The fused LSP captured greenup onset dates for respective forest communities of varied sizes and compositions with four days of the overall MAE. This study supports further use of spatiotemporally enhanced LSP for more precise phenological monitoring.

Wisconsin↗
USGS2016

Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2019

Landsat 1-5 Multispectral Scanner System (MSS) sensors radiometric calibration update

First launched in 1972, the Landsat satellite sensors have provided the longest continuous record of high quality images of the Earth’s surface that are used in both civilian and military applications. The Landsat Multispectral Scanner (MSS) sensor was on-board Landsat-1 through Landsat-5. In fact, the MSS sensors provide the only systematic global multispectral space-based imagery of the Earth’s surface from 1972 to 1982. This paper focuses on the radiometric calibration update of the Landsat 1-5 MSS sensors. The radiometric calibration was performed in both radiance and reflectance-based scales through cross-calibration approach. Simultaneous or a near simultaneous image collections were available for MSS sensor pairs and used for the cross-calibration. The estimated uncertainties for this calibration update exhibit progressively decreasing calibration accuracy, ranging from 5.1% for MSS 5 to 8.8% for MSS-1 in the Green spectral band, for example. Lastly, the new radiometric calibration coefficients were validated through the use of pseudo invariant calibration sites (PICS). The temporal TOA Radiance and TOA Reflectance over the Sonoran Desert were plotted with the purpose of verifying the lifetime radiometric stability of MSS sensors. With the previous calibration, the agreement between the measurements of TOA Reflectance over the Sonoran Desert was around 7.5% for all spectral bands. With the calibration update implemented in this study, the agreement between MSS sensors is 3.6%, 2.9%, 3.5% and 5.9% for the Green, Red, NIR-1 and NIR 2 spectral bands, respectively. This study ties all the Landsat legacy instruments from Landsat-1 MSS through Landsat-8 OLI to a consistent radiometric scale.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2024

Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography

We present a practical approach to intercompare a range of candidate digital elevation models (DEMs) based on predefined criteria and a statistically sound ranking approach. The presented approach integrates the randomized complete block design (RCBD) into a novel framework for DEM comparison. The method presented provides a flexible, statistically sound, and customizable tool for evaluating the quality of any raster—in this case, a DEM—by means of a ranking approach, which takes into account a confidence level and can use both quantitative and qualitative criteria. The users can design their own criteria for the quality evaluation in relation to their specific needs. The application of the RCBD method to rank six 1′′ global DEMs, considering a wide set of study sites, covering different morphological and land cover settings, highlights the potentialities of the approach. We used a suite of criteria relating to the differences in the elevation, slope, and roughness distributions compared to reference DEMs aggregated from 1- to 5-m light detection and ranging (LiDAR)-derived DEMs. Results confirmed the significant superiority of Copernicus DEM (CopDEM) 1′′ and its derivative forests and buildings removed DEM (FABDEM) as the overall best 1′′ global DEMs. They are slightly better than Advanced Land Observing Satellite (ALOS) and clearly outperform NASADEM and SRTM, which are, in turn, much better than advanced spaceborne thermal emission and reflection radiometer (ASTER).

IEEE Transactions on Geoscience and Remote Sensing↗