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At least 631 records · Page 35Linked to original sources
USGS2013

CEOS visualization environment (COVE) tool for intercalibration of satellite instruments

Increasingly, data from multiple instruments are used to gain a more complete understanding of land surface processes at a variety of scales. Intercalibration, comparison, and coordination of satellite instrument coverage areas is a critical effort of international and domestic space agencies and organizations. The Committee on Earth Observation Satellites Visualization Environment (COVE) is a suite of browser-based applications that leverage Google Earth to display past, present, and future satellite instrument coverage areas and coincident calibration opportunities. This forecasting and ground coverage analysis and visualization capability greatly benefits the remote sensing calibration community in preparation for multisatellite ground calibration campaigns or individual satellite calibration studies. COVE has been developed for use by a broad international community to improve the efficiency and efficacy of such calibration planning efforts, whether those efforts require past, present, or future predictions. This paper provides a brief overview of the COVE tool, its validation, accuracies, and limitations with emphasis on the applicability of this visualization tool for supporting ground field campaigns and intercalibration of satellite instruments.

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↗
USGS2015

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

IEEE Transactions on Geoscience and Remote Sensing↗
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↗
USGS2022

Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( R r s ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to R r s spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.

IEEE Transactions in Geoscience and Remote Sensing↗
USGS2022

Snow depth retrieval with an autonomous UAV-mounted software-defined radar

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600-2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. We image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. We present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as ρ = 0.9 demonstrated. Finally, we compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit with in situ measured snow dielectric profiles and demonstrate imaging of snow stratigraphy.

Colorado↗
USGS2022

Acquisition of Moon measurements by Earth orbiting sensors for lunar calibration

The reflected light from the Moon can be utilized as a reference for radiometric calibration by employing a model to generate reference values corresponding to the Moon observations made by instruments. Using a calibration target that is outside the atmosphere provides a distinct advantage for space-based instruments; however, the lunar irradiance sensed by satellite instruments naturally changes as the host spacecraft traverses its orbit. This article presents a study of the potential impact on lunar radiometric measurements due to their acquisition from an orbiting platform. A simulation of a Sun-synchronous orbit was coupled to the U.S. Geological Survey (USGS) lunar model to generate predicted irradiances for points along orbit passes through several lunations. These irradiance values exhibit variations tied to the spacecraft motion, arising primarily from changes in the Moon-sensor distance and the phase angle. The two effects are similar in overall magnitude, but their respective contributions depend on the time of month and the orbit. Relative changes in irradiance mostly fall within an envelope of ±0.006% per second, except at the smallest phase angles. These studies enable planning space-based Moon observations to minimize the change in the target irradiance, an important consideration for measurements acquired for radiometric characterization of the Moon.

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↗
USGS2024

Ecological dissimilarity matters more than geographical distance when predicting land surface indicators using machine learning

Supervised training techniques, such as those used in machine learning, use generally large sets of in situ data to train models that can, in turn, be used to make predictions (or prediction maps) about the Earth’s surface in times or places where no in situ data exist. The purpose of the present study is to investigate, using a very large set of in situ data from across the western United States (U.S.), the conditions under which training data from a different geographic region where predictions are desired may be substituted. To do this, we train models using in situ data from level IV ecoregions and test how well these models predict surface conditions in different ecoregions. We characterize the difference between the possible pairs of ecoregion in terms of geographical (centroid-to-centroid) distance and “ecological dissimilarity.” Ecological dissimilarity between pairs of ecoregions is defined in two ways: 1) as the Euclidean distance in multivariate space defined by in situ indicators designed for monitoring purposes and 2) in terms of the difference in temporal behavior from model- and remote sensing-derived datasets. Although, overall, prediction error increases with geographical distance between training and testing ecoregions, our results indicate that ecological dissimilarity can be used to predict the error expected from a model trained with data from one ecoregion when applied in a different ecoregion.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2024

A global assessment of SAOCOM-1 L-band stripmap data for InSAR characterization of volcanic, tectonic, cryospheric, and anthropogenic deformation

SAOCOM-1 is an L-band (23.5 cm) synthetic aperture radar (SAR) constellation made up of two satellites launched in 2018 and 2020 by Comisión Nacional de Actividades Espaciales (CONAE, Argentina). In this contribution, we present a global summary of interferometric SAR (InSAR) observations of ground deformation with SAOCOM-1 stripmap data for tracking volcanic, tectonic, glacier, and anthropogenic deformation. These examples include: 1) episodes of unrest at volcanoes in the Aleutian Islands, Southern Andes, and Italy, with line-of-sight (LOS) deformation from 4 cm/yr in InSAR time series to ~70 cm in interferograms; 2) dike intrusions in Hawai’i; 3) earthquakes in the Andean fold and thrust belt and the East Anatolian fault; 4) ice flow of the Southern Patagonia icefield; and 5) subsidence due to lithium brine extraction in the Salar de Atacama basin (northern Chile). Comparisons between SAOCOM-1, ALOS-2 SM3, Sentinel-1, and TerraSAR-X/ TanDEM-X/PAZ (TSX/TDX/PAZ) mean velocities from InSAR time series show a 1:1 ± 3% correlation in the LOS velocity, which highlights the high accuracy of SAOCOM-1 data. The minimum deformation that we measured in individual interferograms is 4 ± 0.6 cm. One limitation of SAOCOM-1 is the lack of a global acquisition program, which reduces its global and broader applications. Considering the repeat periods, background observation program, and lack of a controlled orbital tube, the best suited targets for SAOCOM-1 InSAR are two. First, volcanoes that deform with secular rates located in vegetated regions in mid- and high-latitudes, and/or that undergo transient episodes of fast deformation in which C-band coherence is lost quickly. Second, glaciers where coherence can be sustained during the repeat period of eight days.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2026

Across-track tilt concept for sun glint mitigation in future Earth-observing missions

Sun glint is a major limitation for spaceborne optical remote sensing of aquatic environments, particularly in tropical and subtropical regions. By dominating the at-sensor radiance, sun glint complicates atmospheric correction and reduces the quality of aquatic reflectance products derived across Earth observation missions. This study presents a machine learning framework to quantify sun glint probability and evaluates the across-track tilt observational concept as a potential sun glint mitigation approach for satellite remote sensing missions. More than 14,000 cloud-free Landsat 8 and 9 scenes were analyzed to quantify glint occurrence as a function of sun-sensor geometry, latitude, and season. Logistic regression and machine learning models identified solar zenith angle (SZA), wind speed, and viewing zenith angle (VZA) as the primary predictors of sun glint probability, achieving >98% classification accuracy. Sun glint contamination was found to be negligible for SZA > 40° but frequent at SZA <35°, particularly in forward-scattering geometries. Introducing a 3° across-track westward tilt reduces the proportion of glint-contaminated pixels by ~21% in tropical and subtropical regions, while larger tilts of 6°, 9°, and 12° yield reductions of 43%, 59%, and 74%, respectively. These findings underscore the value of incorporating glint mitigation strategies in future Earth observing mission designs. A modest across-track tilt, combined with post-acquisition glint detection and correction, offers a practical pathway to increasing the availability of high-quality aquatic observations for monitoring inland and coastal water quality and ecosystems.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2022

Reserve growth in oil fields of the North Sea

The assessment of petroleum resources of the North Sea, as well as other areas of the world, requires a viable means to forecast the amount of growth of reserve estimates (reserve growth) for discovered fields and to predict the potential fully developed sizes of undiscovered fields. This study investigates the utility of North Sea oil field data to construct reserve-growth models. Oil fields of the North Sea provide an excellent dataset in which to examine the mechanisms, characteristics, rates and quantities of reserve growth because of the high level of capital investments, implementation of sophisticated technologies and careful data collection. Additionally, these field data are well reported and available publicly. Increases in successive annual estimat es of recoverable crude oil volumes indicate that oil fields in the North Sea, collectively and in each country, experience reserve growth. Specific patterns of reserve growth are observed among countries and primary producing reservoir-rock types. Since 1985, Norwegian oil fields had the greatest volume increase; Danish oil fields increased by the greatest percentage relative to 1985 estimates; and British oil fields experienced an increase in recoverable oil estimates for the first ten years since 1985, followed by a slight reduction. Fields producing primarily from clastic reservoirs account for the majority of the estimated recoverable oil and, therefore, these fields had the largest volumetric increase. Fields producing primarily from chalk (limestone) reservoirs increased by a greater percentage relative to 1985 estimates than did fields producing primarily from clastic reservoirs. Additionally, the largest oil fields had the greatest volumetric increases. Although different reserve-growth patterns are observed among oil fields located in different countries, the small number of fields in Denmark precludes construction of reserve-growth models for that country. However, differences in reserve-growth patterns among oil fields that produce from primarily clastic and primarily chalk reservoirs, in addition to a greater number of fields in each of the two categories, allow separate reserve-growth models to be constructed based on reservoir-rock type. Reserve-growth models referenced to the date of discovery and to the date of first production may be constructed from North Sea field data. Years since discovery or years since first production are used as surrogates for, or measures of, field-development effort that is applied to promote reserve growth. Better estimates of recoverable oil are made as fields are developed. Because much of the field development occurs some time later than the field discovery date, reserve-growth models referenced to the date of first production may provide a more appropriate measure of development than does date of discovery. ?? 2005 EAGE/Geological Society of London.

Petroleum Geoscience↗
USGS2022

Infiltration of late Palaeozoic evaporative brines in the reelfoot rift: A possible salt source for Illinois Basin formation waters and MVT mineralizing fluids

Salinities and homogenization temperatures of fluid inclusions in Mississippi Valley-type (MVT) deposits provide important insights into the regional hydrology of the Illinois basin/Reelfoot rift system in late Palaeozoic time. Although the thermal regime of this basin system has been plausibly explained, the origin of high salinities in the basin fluids remains enigmatic. Topographically driven flow appears to have been essential in forming these MVT districts, as well as many other districts worldwide. However, this type of flow is recharged by fresh water making it difficult to account for the high salinities of the mineralizing fluids over extended time periods. Results of numerical experiments carried out in this study provide a possible solution to the salinity problem presented by the MVT zinc-lead and fluorite districts at the margins of the basin system. Evaporative concentration of surface water and subsequent infiltration into the subsurface are proposed to account for large volumes of brine that are ultimately responsible for mineralization of these districts. This study demonstrates that under a range of geologically reasonable conditions, brine infiltration into an aquifer in the deep subsurface can coexist with topographically driven flow. Infiltration combined with regional flow and local magmatic heat sources in the Reelfoot rift explain the brine concentrations as well as the temperatures observed in the Southern Illinois and Upper Mississippi Valley districts.

Petroleum Geoscience↗
USGS2021

Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah

Geological carbon sequestration (GCS) is necessary to help meet emissions reduction goals, but groundwater contamination may occur if CO 2 and/or brine were to leak out of deep storage formations into the shallow subsurface. For this study, a natural analogue was investigated: in the Virgin River Basin of southwest Utah, water with moderate salinity and high CO 2 concentrations is leaking upward into shallow aquifers that contain heavy metal-bearing concretions. The aquifer system is comprised of the Navajo and Kayenta formations, which are pervasive across southern Utah and have been considered as a potential GCS injection unit where they are sufficiently deep. Numerical models of the site were constructed based on measured water chemistry and head distributions from previous studies. Simulations were used to improve understanding of the rate and distribution of the upwelling flow into the aquifers, and to assess the reactive transport processes that may occur if the upwelling fluids were to interact with a zone of iron oxide and other heavy metals, representing the concretions that are common in the area. Various mineralogies were tested, including one in which Pb +2 was adsorbed onto ferrihydrite, and another in which it was bound within a solid mixture of litharge (PbO) and hematite (Fe 2 O 3 ). Results indicate that metal mobilization depends strongly on the source zone composition and that Pb +2 transport can be naturally attenuated by gas phase formation and carbonate mineral precipitation. These findings could be used to improve risk assessment and mitigation strategies at geological carbon sequestration sites.

Utah↗