The Experts below are selected from a list of 63549 Experts worldwide ranked by ideXlab platform
Jeffrey G Masek - One of the best experts on this subject based on the ideXlab platform.
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quality assessment of landsat Surface Reflectance products using modis data
Computers & Geosciences, 2012Co-Authors: Min Feng, Jeffrey G Masek, Chengquan Huang, Saurabh Channan, E Vermote, J R G TownshendAbstract:Surface Reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level Surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of Surface Reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The Surface Reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat Surface Reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat Surface Reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary Surface Reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As Surface Reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.
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on the blending of the landsat and modis Surface Reflectance predicting daily landsat Surface Reflectance
IEEE Transactions on Geoscience and Remote Sensing, 2006Co-Authors: Jeffrey G Masek, M Schwaller, F G HallAbstract:The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive Reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS Surface Reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m Surface Reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m Surface Reflectance are used to produce a synthetic "daily" Surface Reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts Surface Reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes
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a landsat Surface Reflectance dataset for north america 1990 2000
IEEE Geoscience and Remote Sensing Letters, 2006Co-Authors: Jeffrey G Masek, F G Hall, Eric Vermote, Nazmi Saleous, Robert E Wolfe, K F Huemmrich, J KutlerAbstract:The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Surface Reflectance scenes, providing 30-m resolution wall-to-wall Reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can support decadal assessments of environmental and land-cover change, production of Reflectance-based biophysical products, and applications that merge Reflectance data from multiple sensors [e.g., the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Multiangle Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer (MODIS)]. The raw imagery was obtained from the orthorectified Landsat GeoCover dataset, purchased by NASA from the Earth Satellite Corporation. Through the LEDAPS project, these data were calibrated, converted to top-of-atmosphere Reflectance, and then atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat Surface Reflectance compared to the standard MODIS Reflectance product (the greater of 0.5% absolute Reflectance or 5% of the recorded Reflectance value). The rapid automated nature of the processing stream also paves the way for routine high-level products from future Landsat sensors.
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On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat
2006Co-Authors: Jeffrey G Masek, M SchwallerAbstract:The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiome- ter (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive Reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS sur- face Reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m sur- face Reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m Surface Reflectance are used to produce a synthetic "daily" Surface Reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts Surface Reflectance at an effective resolution close to that of the ETM+. However, the per- formance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes.
Daniel Schläpfer - One of the best experts on this subject based on the ideXlab platform.
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Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne
2008Co-Authors: Rudolf Richter, Daniel SchläpferAbstract:The retrievals of atmospheric water vapor column and Surface Reflectance from air- or spaceborne hyperspectral imagery require accurate spectroradiometric calibration and a radiative transfer (RT) code. Since RT codes are too time con- suming to be run on a per-pixel basis, a common technique employs the offline compilation of an atmospheric database and its subsequent use for the atmospheric correction of the image cube. The challenge is to design the size of the database as small as possible for a requested retrieval accuracy. We present a method- ology to compile the database for a specified retrieval accuracy in water vapor and Surface Reflectance for a given set of input Surface Reflectance spectra and a chosen RT algorithm. The method is applied as a case study conducted for the planned German imaging spectrometer EnMAP. Some tradeoff considera- tions are also discussed. For the specified range of columnar water vapor (0.5-4.5 cm), results demonstrate that five water vapor grid points in the database are sufficient to achieve the requested relative root-mean-square retrieval accuracies of 2% and 3% in water vapor and Surface Reflectance, respectively. It should be pointed out that this is not intended as a general claim of retrieval accuracy achievable under typical remote sensing conditions, but these figures apply only to the theoretical conditions of the calcu- lation, i.e., assuming the same conditions for forward simulation and retrieval. Nevertheless, these figures are indispensable for the design of a database, which is an important step for the atmospheric correction of imaging spectrometer data and the sole topic of this paper.
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Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: Rudolf Richter, Daniel SchläpferAbstract:The retrievals of atmospheric water vapor column and Surface Reflectance from air- or spaceborne hyperspectral imagery require accurate spectroradiometric calibration and a radiative transfer (RT) code. Since RT codes are too time consuming to be run on a per-pixel basis, a common technique employs the offline compilation of an atmospheric database and its subsequent use for the atmospheric correction of the image cube. The challenge is to design the size of the database as small as possible for a requested retrieval accuracy. We present a methodology to compile the database for a specified retrieval accuracy in water vapor and Surface Reflectance for a given set of input Surface Reflectance spectra and a chosen RT algorithm. The method is applied as a case study conducted for the planned German imaging spectrometer EnMAP. Some tradeoff considerations are also discussed. For the specified range of columnar water vapor (0.5-4.5 cm), results demonstrate that five water vapor grid points in the database are sufficient to achieve the requested relative root-mean-square retrieval accuracies of 2% and 3% in water vapor and Surface Reflectance, respectively. It should be pointed out that this is not intended as a general claim of retrieval accuracy achievable under typical remote sensing conditions, but these figures apply only to the theoretical conditions of the calculation, i.e., assuming the same conditions for forward simulation and retrieval. Nevertheless, these figures are indispensable for the design of a database, which is an important step for the atmospheric correction of imaging spectrometer data and the sole topic of this paper.
F G Hall - One of the best experts on this subject based on the ideXlab platform.
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on the blending of the landsat and modis Surface Reflectance predicting daily landsat Surface Reflectance
IEEE Transactions on Geoscience and Remote Sensing, 2006Co-Authors: Jeffrey G Masek, M Schwaller, F G HallAbstract:The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive Reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS Surface Reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m Surface Reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m Surface Reflectance are used to produce a synthetic "daily" Surface Reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts Surface Reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes
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a landsat Surface Reflectance dataset for north america 1990 2000
IEEE Geoscience and Remote Sensing Letters, 2006Co-Authors: Jeffrey G Masek, F G Hall, Eric Vermote, Nazmi Saleous, Robert E Wolfe, K F Huemmrich, J KutlerAbstract:The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus Surface Reflectance scenes, providing 30-m resolution wall-to-wall Reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can support decadal assessments of environmental and land-cover change, production of Reflectance-based biophysical products, and applications that merge Reflectance data from multiple sensors [e.g., the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Multiangle Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer (MODIS)]. The raw imagery was obtained from the orthorectified Landsat GeoCover dataset, purchased by NASA from the Earth Satellite Corporation. Through the LEDAPS project, these data were calibrated, converted to top-of-atmosphere Reflectance, and then atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat Surface Reflectance compared to the standard MODIS Reflectance product (the greater of 0.5% absolute Reflectance or 5% of the recorded Reflectance value). The rapid automated nature of the processing stream also paves the way for routine high-level products from future Landsat sensors.
Belen Franch - One of the best experts on this subject based on the ideXlab platform.
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preliminary analysis of the performance of the landsat 8 oli land Surface Reflectance product
Remote Sensing of Environment, 2016Co-Authors: Eric Vermote, C O Justice, Martin Claverie, Belen FranchAbstract:Abstract The Surface Reflectance, i.e., satellite derived top of atmosphere (TOA) Reflectance corrected for the temporally, spatially and spectrally varying scattering and absorbing effects of atmospheric gases and aerosols, is needed to monitor the land Surface reliably. For this reason, the Surface Reflectance, and not TOA Reflectance, is used to generate the greater majority of global land products, for example, from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Even if atmospheric effects are minimized by sensor design, atmospheric effects are still challenging to correct. In particular, the strong impact of aerosols in the visible and near infrared spectral range can be difficult to correct, because they can be highly discrete in space and time (e.g., smoke plumes) and because of the complex scattering and absorbing properties of aerosols that vary spectrally and with aerosol size, shape, chemistry and density. This paper presents the Landsat 8 Operational Land Imager (OLI) atmospheric correction algorithm that has been developed using the Second Simulation of the Satellite Signal in the Solar Spectrum Vectorial (6SV) model, refined to take advantage of the narrow OLI spectral bands (compared to Thematic Mapper/Enhanced Thematic Mapper (TM/ETM +)), improved radiometric resolution and signal-to-noise. In addition, the algorithm uses the new OLI Coastal aerosol band (0.433–0.450 μm), which is particularly helpful for retrieving aerosol properties, as it covers shorter wavelengths than the conventional Landsat, TM and ETM + blue bands. A cloud and cloud shadow mask has also been developed using the “cirrus” band (1.360–1.390 μm) available on OLI, and the thermal infrared bands from the Thermal Infrared Sensor (TIRS) instrument. The performance of the Surface Reflectance product from OLI is analyzed over the Aerosol Robotic Network (AERONET) sites using accurate atmospheric correction (based on in situ measurements of the atmospheric properties), by comparison with the MODIS Bidirectional Reflectance Distribution Function (BRDF) adjusted Surface Reflectance product and by comparison of OLI derived broadband albedo from United States Surface Radiation Budget Network (US SURFRAD) measurements. The results presented clearly show an improvement of Landsat 8 Surface Reflectance product over the ad-hoc Landsat 5/7 LEDAPS product.
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preliminary analysis of the performance of the landsat 8 oli land Surface Reflectance product
Remote Sensing of Environment, 2016Co-Authors: Eric Vermote, C O Justice, Martin Claverie, Belen FranchAbstract:The Surface Reflectance, i.e., satellite derived top of atmosphere (TOA) Reflectance corrected for the temporally, spatially and spectrally varying scattering and absorbing effects of atmospheric gases and aerosols, is needed to monitor the land Surface reliably. For this reason, the Surface Reflectance, and not TOA Reflectance, is used to generate the greater majority of global land products, for example, from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Even if atmospheric effects are minimized by sensor design, atmospheric effects are still challenging to correct. In particular, the strong impact of aerosols in the Visible and Near Infrared spectral range can be difficult to correct, because they can be highly discrete in space and time (e.g., smoke plumes) and because of the complex scattering and absorbing properties of aerosols that vary spectrally and with aerosol size, shape, chemistry and density. This paper presents the Landsat 8 Operational Land Imager (OLI) atmospheric correction algorithm that has been developed using the Second Simulation of the Satellite Signal in the Solar Spectrum Vectorial (6SV) model, refined to take advantage of the narrow OLI spectral bands (compared to Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+)), improved radiometric resolution and signal-to-noise. In addition, the algorithm uses the new OLI Coastal aerosol band (0.433-0.450μm), which is particularly helpful for retrieving aerosol properties, as it covers shorter wavelengths than the conventional Landsat, TM and ETM+ blue bands. A cloud and cloud shadow mask has also been developed using the "cirrus" band (1.360-1.390 μm) available on OLI, and the thermal infrared bands from the Thermal Infrared Sensor (TIRS) instrument. The performance of the Surface Reflectance product from OLI is analyzed over the Aerosol Robotic Network (AERONET) sites using accurate atmospheric correction (based on in situ measurements of the atmospheric properties), by comparison with the MODIS Bidirectional Reflectance Distribution Function (BRDF) adjusted Surface Reflectance product and by comparison of OLI derived broadband albedo from United States Surface Radiation Budget Network (US SURFRAD) measurements.
Ming Liu - One of the best experts on this subject based on the ideXlab platform.
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a fast smoothing algorithm for post processing of Surface Reflectance spectra retrieved from airborne imaging spectrometer data
Sensors, 2013Co-Authors: Bocai Gao, Ming LiuAbstract:Surface Reflectance spectra retrieved from remotely sensed hyperspectral imaging data using radiative transfer models often contain residual atmospheric absorption and scattering effects. The Reflectance spectra may also contain minor artifacts due to errors in radiometric and spectral calibrations. We have developed a fast smoothing technique for post-processing of retrieved Surface Reflectance spectra. In the present spectral smoothing technique, model-derived Reflectance spectra are first fit using moving filters derived with a cubic spline smoothing algorithm. A common gain curve, which contains minor artifacts in the model-derived Reflectance spectra, is then derived. This gain curve is finally applied to all of the Reflectance spectra in a scene to obtain the spectrally smoothed Surface Reflectance spectra. Results from analysis of hyperspectral imaging data collected with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data are given. Comparisons between the smoothed spectra and those derived with the empirical line method are also presented.
