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At least 289 records · Page 16Linked to original sources
USGS2004

Estimating suspended solids concentrations from backscatter intensity measured by acoustic Doppler current profiler in San Francisco Bay, California

The estimation of mass concentration of suspended solids is one of the properties needed to understand the characteristics of sediment transport in bays and estuaries. However, useful measurements or estimates of this property are often problematic when employing the usual methods of determination from collected water samples or optical sensors. Analysis of water samples tends to undersample the highly variable character of suspended solids, and optical sensors often become useless from biological fouling in highly productive regions. Acoustic sensors, such as acoustic Doppler current profilers that are now routinely used to measure water velocity, have been shown to hold promise as a means of quantitatively estimating suspended solids from acoustic backscatter intensity, a parameter used in velocity measurement. To further evaluate application of this technique using commercially available instruments, profiles of suspended solids concentrations are estimated from acoustic backscatter intensity recorded by 1200- and 2400-kHz broadband acoustic Doppler current profilers located at two sites in San Francisco Bay, California. ADCP backscatter intensity is calibrated using optical backscatterance data from an instrument located at a depth close to the ADCP transducers. In addition to losses from spherical spreading and water absorption, calculations of acoustic transmission losses account for attenuation from suspended sediment and correction for nonspherical spreading in the near field of the acoustic transducer. Acoustic estimates of suspended solids consisting of cohesive and noncohesive sediments are found to agree within about 8-10% (of the total range of concentration) to those values estimated by a second optical backscatterance sensor located at a depth further from the ADCP transducers. The success of this approach using commercially available Doppler profilers provides promise that this technique might be appropriate and useful under certain conditions in spite of some theoretical limitations of the method. ?? 2004 Elsevier B.V. All rights reserved.

Marine Geology↗
USGS2004

Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: initial results from Tampa Bay, FL

Using Tampa Bay, FL as an example, we explored the potential for using MODIS medium-resolution bands (250- and 500-m data at 469-, 555-, and 645-nm) for estuarine monitoring. Field surveys during 21–22 October 2003 showed that Tampa Bay has Case-II waters, in that for the salinity range of 24–32 psu, (a) chlorophyll concentration (11 to 23 mg m −3 ), (b) colored dissolved organic matter (CDOM) absorption coefficient at 400 nm (0.9 to 2.5 m −1 ), and (c) total suspended sediment concentration (TSS: 2 to 11 mg L −1 ) often do not co-vary. CDOM is the only constituent that showed a linear, inverse relationship with surface salinity, although the slope of the relationship changed with location within the bay. The MODIS medium-resolution bands, although designed for land use, are 4–5 times more sensitive than Landsat-7/ETM+ data and are comparable to or higher than those of CZCS. Several approaches were used to derive synoptic maps of water constituents from concurrent MODIS medium-resolution data. We found that application of various atmospheric-correction algorithms yielded no significant differences, due primarily to uncertainties in the sensor radiometric calibration and other sensor artifacts. However, where each scene could be groundtruthed, simple regressions between in situ observations of constituents and at-sensor radiances provided reasonable synoptic maps. We address the need for improvements of sensor calibration/characterization, atmospheric correction, and bio-optical algorithms to make operational and quantitative use of these medium-resolution bands.

Florida↗
USGS2010

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km 2 study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000–2009.

Alaska↗
USGS1997

A theoretically based determination of bowen-ratio fetch requirements

Determination of fetch requirements for accurate Bowen-ratio measurements of latent- and sensible-heat fluxes is more involved than for eddy-correlation measurements because Bowen-ratio sensors are located at two heights, rather than just one. A simple solution to the diffusion equation is used to derive an expression for Bowen-ratio fetch requirements, downwind of a step change in surface fluxes. These requirements are then compared to eddy-correlation fetch requirements based on the same diffusion equation solution. When the eddy-correlation and upper Bowen-ratio sensor heights are equal, and the available energy upwind and downwind of the step change is constant, the Bowen-ratio method requires less fetch than does eddy correlation. Differences in fetch requirements between the two methods are greatest over relatively smooth surfaces. Bowen-ratio fetch can be reduced significantly by lowering the lower sensor, as well as the upper sensor. The Bowen-ratio fetch model was tested using data from a field experiment where multiple Bowen-ratio systems were deployed simultaneously at various fetches and heights above a field of bermudagrass. Initial comparisons were poor, but improved greatly when the model was modified (and operated numerically) to account for the large roughness of the upwind cotton field.

Boundary-Layer Meteorology↗
USGS2010

Interpretation of surface flux measurements in heterogeneous terrain during the Monsoon '90 experiment

A network of 9-m-tall surface flux measurement stations were deployed at eight sparsely vegetated sites during the Monsoon '90 experiment to measure net radiation, Q , soil heat flux, G , sensible heat flux, H (using eddy correlation), and latent heat flux, λ E (using the energy balance equation). At four of these sites, 2-m-tall eddy correlation systems were used to measure all four fluxes directly. Also a 2-m-tall Bowen ratio system was deployed at one site. Magnitudes of the energy balance closure ( Q + G + H + λ E ) increased as the complexity of terrain increased. The daytime Bowen ratio decreased from about 10 before the monsoon season to about 0.3 during the monsoons. Source areas of the measurements are developed and compared to scales of heterogeneity arising from the sparse vegetation and the topography. There was very good agreement among simultaneous measurements of Q with the same model sensor at different heights (representing different source areas), but poor agreement among different brands of sensors. Comparisons of simultaneous measurements of G suggest that because of the extremely small source area, extreme care in sensor deployment is necessary for accurate measurement in sparse canopies. A recently published model to estimate fetch is used to interpret measurements of H at the 2 m and 9 m heights. Three sites were characterized by undulating topography, with ridgetops separated by about 200–600 m. At these sites, sensors were located on ridgetops, and the 9-m fetch included the adjacent valley, whereas the 2-m fetch was limited to the immediate ridgetop and hillside. Before the monsoons began, vegetation was mostly dormant, the watershed was uniformly hot and dry, and the two measurements of H were in close agreement. After the monsoons began and vegetation fully matured, the 2-m measurements of H were significantly greater than the 9-m measurements, presumably because the vegetation in the valleys was denser and cooler than on the ridgetops and hillsides. At one lowland site with little topographic relief, the vegetation was more uniform, and the two measurements of H were in close agreement during peak vegetation. Values of λ E could only be compared at two sites, but the 9-m values were greater than the 2-m values, suggesting λ E from the dense vegetation in the valleys was greater than elsewhere.

Water Environment Research↗
USGS2010

Prospects for quantifying structure, floristic composition and species richness of tropical forests

Airborne spectral and light detection and ranging (lidar) sensors have been used to quantify biophysical characteristics of tropical forests. Lidar sensors have provided high-resolution data on forest height, canopy topography, volume, and gap size; and provided estimates on number of strata in a forest, successional status of forests, and above-ground biomass. Spectral sensors have provided data on vegetation types, foliar biochemistry content of forest canopies, tree and canopy phenology, and spectral signatures for selected tree species. A number of advances are theoretically possible with individual and combined spectral and lidar sensors for the study of forest structure, floristic composition and species richness. Delineating individual canopies of over-storey trees with small footprint lidar and discrimination of tree architectural types with waveform distributions is possible and would provide scientists with a new method to study tropical forest structure. Combined spectral and lidar data can be used to identify selected tree species and identify the successional status of tropical forest fragments in order to rank forest patches by levels of species richness. It should be possible in the near future to quantify selected patterns of tropical forests at a higher resolution than can currently be undertaken in the field or from space.

International Journal of Remote Sensing↗
USGS2009

Remote sensing of snow and ice

Monitoring of snow and ice on the Earth's surface will require increasing use of satellite remote sensing techniques. These techniques are evolving rapidly. Active and passive sensors operating in the visible, near infrared, thermal infrared, and microwave wavelengths are described in regard to general applications and in regard to specific USA or USSR satellites. Meteorological satellites (frequent images of relatively crude resolution) and Earth resources satellites such as Landsat (less frequent images of higher resolution) have been used to monitor the areal extent of seasonal snow, but problems exist with cloud cover or dense forest canopies. Snow mass (water equivalent) can be measured from a low-flying aircraft using natural radioactivity, but cannot yet be measured from satellite altitudes. A combination of active and passive microwave sensors may permit this kind of measurement, but not until more is known about radiation scattering in snow. Satellite observations are very useful in glacier inventories, correcting maps of glacier extent, estimating certain mass balance parameters, and monitoring calving or surging glaciers. Ground ice is virtually impossible to monitor from satellites; ice on rivers and lakes can be monitored only with very high-resolution sensors. Microwave sensors, due to their all-weather capability (the ability to see through clouds) provide exciting data on sea ice distribution. Analysis of digital tapes of satellite data requires the archiving and scanning of huge amounts of data. Simple methods for extracting quantitative data from satellite images are described.

Hydrological Sciences Bulletin↗
USGS2008

Monitoring on-orbit stability of Terra MODIS and Landsat 7 ETM+ reflective solar bands using Railroad Valley Playa, Nevada (RVPN) test site

The Moderate Resolution Imaging Spectroradiometer (MODIS) Proto-Flight Model (PFM), launched on December 18, 1999, aboard NASA's Earth Observing System (EOS) Terra satellite has 20 reflective solar bands (RSB) with wavelengths ranging from 0.41 to 2.1 mum over a wide field of view (plusmn55deg). The Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) sensor was launched on April 15, 1999, and has six spectral bands located in the visible and shortwave infrared (SWIR) part of the electromagnetic spectrum (0.4 - 2.5 mum). The ETM+ belongs to the family of Thematic Mapper sensors flown on previous Landsat missions. In this study, over 75 cloud-free nadir near-simultaneous images over Railroad Valley Playa, Nevada (RVPN) were chosen covering entire missions of both sensors. RVPN (38.5degN and 115.7degW), located between the cities of Ely and Tonopah, Nevada, USA, is a high reflectance site with very high spatial, spectral, and temporal uniformity. It is referenced to the Worldwide Reference System-2 (WRS-2) with path 40 and row 33. Homogeneous regions of interest (ROI) were chosen and cross-calibration was performed using an image statistics approach to monitor the long-term stability of the two sensors.

Nevada↗
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↗
USGS2016

Using diurnal temperature signals to infer vertical groundwater-surface water exchange

Heat is a powerful tracer to quantify fluid exchange between surface water and groundwater. Temperature time series can be used to estimate pore water fluid flux, and techniques can be employed to extend these estimates to produce detailed plan-view flux maps. Key advantages of heat tracing include cost-effective sensors and ease of data collection and interpretation, without the need for expensive and time-consuming laboratory analyses or induced tracers. While the collection of temperature data in saturated sediments is relatively straightforward, several factors influence the reliability of flux estimates that are based on time series analysis (diurnal signals) of recorded temperatures. Sensor resolution and deployment are particularly important in obtaining robust flux estimates in upwelling conditions. Also, processing temperature time series data involves a sequence of complex steps, including filtering temperature signals, selection of appropriate thermal parameters, and selection of the optimal analytical solution for modeling. This review provides a synthesis of heat tracing using diurnal temperature oscillations, including details on optimal sensor selection and deployment, data processing, model parameterization, and an overview of computing tools available. Recent advances in diurnal temperature methods also provide the opportunity to determine local saturated thermal diffusivity, which can improve the accuracy of fluid flux modeling and sensor spacing, which is related to streambed scour and deposition. These parameters can also be used to determine the reliability of flux estimates from the use of heat as a tracer.

Groundwater↗
USGS2018

Full-range, solar-reflected hyperspectral microscopy to support earth remote sensing research

Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the characterization of healthy and diseased tissue and of biomolecular indicators at the cellular level. Here, we describe the development of a hyperspectral imaging (HSI) microscope that is aimed at material characterization to complement traditional stand-off, earth remote sensing with hyperspectral sensors. We combine commercial off the shelf technology to build an HSI microscope to collect spectral data with illumination provided by a tunable laser. Hyperspectral imaging microscopy (HIM) facilitates detailed examination of target materials at the subcentimeter spatial scale. The custom-built, laser illumination HSI microscope covers the NIR to shortwave infrared (NIR/SWIR; 900 to 2500 nm) solar-reflected spectral range. It is combined with a separate VNIR sensor (400 to 900 nm) that utilizes quartz–tungsten–halogen lamps for illumination. The combined sensors provide a means to collect <10,000 s of spectra in the full VNIR/SWIR spectral range from both pure substances and precisely engineered linear and nonlinear mixtures. The large abundance of spectra allows for a more detailed understanding of the variability and multivariate probability distributions of spectral signatures. This additional information aids in understanding the variability observed in ground truth spectra collected from portable spectrometers, and it greatly enhances sample description and metadata content. In addition, HIM data cubes can serve as proxies, as “microscenes,” for systems engineering applications such as trade studies for HSI acquired by air- and space-borne sensors.

Journal of Applied Remote Sensing↗
USGS2017

Radiometric characterization of Landsat Collection 1 products

Landsat data in the U.S. Geological Survey (USGS) archive are being reprocessed to generate a tiered collection of consistently geolocated and radiometrically calibrated products that are suitable for time series analyses. With the implementation of the collection management, no major updates will be made to calibration of the Landsat sensors within a collection. Only calibration parameters needed to maintain the established calibration trends without an effect on derived environmental records will be regularly updated, while all other changes will be deferred to a new collection. This first collection, Collection 1, incorporates various radiometric calibration updates to all Landsat sensors including absolute and relative gains for Landsat 8 Operational Land Imager (OLI), stray light correction for Landsat 8 Thermal Infrared Sensor (TIRS), absolute gains for Landsat 4 and 5 Thematic Mappers (TM), recalibration of Landsat 1-5 Multispectral Scanners (MSS) to ensure radiometric consistency among different formats of archived MSS data, and a transfer of Landsat 8 OLI reflectance based calibration to all previous Landsat sensors. While all OLI/TIRS, ETM+ and majority of TM data have already been reprocessed to Collection 1, a completion of MSS and remaining TM data reprocessing is expected by the end of this year. It is important to note that, although still available for download from the USGS web pages, the products generated using the Pre-Collection processing do not benefit from the latest radiometric calibration updates. In this paper, we are assessing radiometry of solar reflective bands in Landsat Collection 1 products through analysis of trends in on-board calibrator and pseudo invariant site (PICS) responses.

Conference Paper↗
USGS2021

Radiometric performance of Landsat 8 Collection 2 products

The U.S. Geological Survey (USGS) archive of Earth images acquired by Landsat 1-8 sensors is organized in collections of consistently calibrated, geolocated, and processed data products. Such an organization ensures consistent quality of the archived data within a collection over time and across all instruments within the Landsat mission. In December 2020, the USGS completed reprocessing of the archived Landsat data and released a new collection, Collection 2, which introduced surface reflectance and surface temperature Level-2 products, implemented improved ground control and elevation datasets, and brought several geometric and radiometric calibration enhancements. Radiometric enhancements include absolute and relative gain updates for both imaging sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), aboard Landsat 8 and a change in calculation of bias for OLI. In this paper we present the analysis of Collection 2 Landsat 8 products demonstrating stability over the mission lifetime, the improvement in OLI signal to noise ratio and along-track striping performance resulting from enhanced bias correction, as well as reduction of cross-track striping in TIRS data as a result of relative gain updates. In addition, we discuss effects of two safehold events that Landsat 8 experienced in November 2020 on the radiometric calibration of both sensors and product performance.

Conference Paper↗
USGS2005

Improvement in absolute calibration accuracy of Landsat-5 TM with Landsat-7 ETM+ data

The ability to detect and quantify changes in the Earth's environment depends on satellites sensors that can provide calibrated, consistent measurements of Earth's surface features through time. A critical step in this process is to put image data from subsequent generations of sensors onto a common radiometric scale. To evaluate Landsat-5 (L5) Thematic Mapper's (TM) utility in this role, image pairs from the L5 TM and Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) sensors were compared. This approach involves comparison of surface observations based on image statistics from large common areas observed eight days apart by the two sensors. The results indicate a significant improvement in the consistency of L5 TM data with respect to L7 ETM+ data, achieved using a revised Look-Up-Table (LUT) procedure as opposed to the historical Internal Calibrator (IC) procedure previously used in the L5 TM product generation system. The average percent difference in reflectance estimates obtained from the L5 TM agree with those from the L7 ETM+ in the Visible and Near Infrared (VNIR) bands to within four percent and in the Short Wave Infrared (SWIR) bands to within six percent.

Conference Paper↗
USGS2010

Preliminary assessment of several parameters to measure and compare usefulness of the CEOS reference pseudo-invariant calibration sites

Test sites are central to any future quality assurance and quality control (QA/QC) strategy. The Committee on Earth Observation Satellites (CEOS) Working Group for Calibration and Validation (WGCV) Infrared Visible Optical Sensors (IVOS) worked with collaborators around the world to establish a core set of CEOS-endorsed, globally distributed, reference standard test sites (both instrumented and pseudo-invariant) for the post-launch calibration of space-based optical imaging sensors. The pseudo-invariant calibration sites (PICS) have high reflectance and are usually made up of sand dunes with low aerosol loading and practically no vegetation. The goal of this paper is to provide preliminary assessment of "several parameters" than can be used on an operational basis to compare and measure usefulness of reference sites all over the world. The data from Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Earth Observing-1 (EO-1) Hyperion sensors over the CEOS PICS were used to perform a preliminary assessment of several parameters, such as usable area, data availability, top-of-atmosphere (TOA) reflectance, at-sensor brightness temperature, spatial uniformity, temporal stability, spectral stability, and typical spectrum observed over the sites.

Conference Paper↗
USGS2011

Landsat 8 on-orbit characterization and calibration system

The Landsat Data Continuity Mission (LDCM) is planning to launch the Landsat 8 satellite in December 2012, which continues an uninterrupted record of consistently calibrated globally acquired multispectral images of the Earth started in 1972. The satellite will carry two imaging sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The OLI will provide visible, near-infrared and short-wave infrared data in nine spectral bands while the TIRS will acquire thermal infrared data in two bands. Both sensors have a pushbroom design and consequently, each has a large number of detectors to be characterized. Image and calibration data downlinked from the satellite will be processed by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center using the Landsat 8 Image Assessment System (IAS), a component of the Ground System. In addition to extracting statistics from all Earth images acquired, the IAS will process and trend results from analysis of special calibration acquisitions, such as solar diffuser, lunar, shutter, night, lamp and blackbody data, and preselected calibration sites. The trended data will be systematically processed and analyzed, and calibration and characterization parameters will be updated using both automatic and customized manual tools. This paper describes the analysis tools and the system developed to monitor and characterize on-orbit performance and calibrate the Landsat 8 sensors and image data products.

Conference Paper↗
USGS2021

Hyperspectral narrowband data propel gigantic leap in the earth remote sensing

Hyperspectral narrowbands (HNBs) capture data as nearly continuous “spectral signatures” rather than a “few spectral data points” along the electromagnetic spectrum as with multispectral broadbands (MBBs). Almost all of satellite remote sensing of the Earth in the twentieth century was conducted using MBB data from sensors such as the Landsat-series, Advanced Very High-Resolution Radiometer (AVHRR), SPOT (Système Pour l’Observation de la Terre), and the Indian Remote Sensing (IRS) satellites. These systems typically provide 4 to 9 broad spectral wavebands spread from 400 to 2500 nm, often with one or two additional bands in the thermal range. Significant advances in the study of the Earth have been made based on these data [Thenkabail et al., 2018a,b,c,d; Thenkabail et al., 2015a,b,c]. Possibilities of great advances that can be made using HNB data over MBB data are well established based on studies conducted using hyperspectral sensors such as the hand-held spectroradiometers, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and spaceborne Earth Observing -1 (EO-1) Hyperion [Thenkabail 2018a,b,c,d]. The twenty-first century is already seeing the dawn of hyperspectral imaging data from sensors such as the German Aerospace Center’s (DLR’s) DESIS (DLR Earth Sensing Imaging Spectrometer) onboard the MUSES (Multi-User System for Earth Sensing) platform on the International Space Station (ISS), the polar-orbiting Italian Space Agency’s (ASI) PRISMA (PRecursore IperSpettrale della Missione Applicativa), and many other upcoming sensors such as the NASA Surface Biology and Geology (SBG) [Thenkabail et al., 2018a,b,c,d]. These satellites acquire data in hundreds of narrow spectral bands of 1 to 10 nm width, typically between 400 to 2500 nm; also future planned missions will be extending HNBs to the thermal (9,000 to 14,000 nm) electromagnetic spectrum. This expansion creates a quantum leap in new data, new information, and myriad possible new applications in the study of the Earth in addition to great advances in existing applications. Given the above, the objective of this article is to provide insights on the gigantic leap in our understanding, modeling, mapping, and monitoring of the Earth that can be made using HNB relative to MBB by focusing on agricultural and vegetation applications. We will address this in four aspects: 1. Comparison between HNB and MBB data; 2. Spectral libraries of agricultural crops; 3. HNB data analysis in general; and 4. HNB analysis using machine learning (ML) and cloud computing.

Photogrammetric Engineering and Remote Sensing.↗