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81 records · Page 5

Impacts of Cross-Platform Vicarious Calibration on the Deep Blue Aerosol Retrievals for Moderate Resolution Imaging Spectroradiometer Aboard Terra

The retrieval of aerosol properties from spaceborne sensors requires highly accurate and precise radiometric measurements, thus placing stringent requirements on sensor calibration and characterization. For the Terra/Moderate Resolution Imaging Spedroradiometer (MODIS), the characteristics of the detectors of certain bands, particularly band 8 [(B8); 412 nm], have changed significantly over time, leading to increased calibration uncertainty. In this paper, we explore a possibility of utilizing a cross-calibration method developed for characterizing the Terral MODIS detectors in the ocean bands by the National Aeronautics and Space Administration Ocean Biology Processing Group to improve aerosol retrieval over bright land surfaces. We found that the Terra/MODIS B8 reflectance corrected using the cross calibration method resulted in significant improvements for the retrieved aerosol optical thickness when compared with that from the Multi-angle Imaging Spectroradiometer, Aqua/MODIS, and the Aerosol Robotic Network. The method reported in this paper is implemented for the operational processing of the Terra/MODIS Deep Blue aerosol products.

Jeong, Myeong-Jae↗

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends in Washington State, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends from Northern California to British Columbia, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗

Global Validation of Columnar Water Vapor Derived from EOS MODIS-MAIAC Algorithm Against the Ground-Based AERONET Observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from the Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1-kilometer resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000-2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS (Root Mean Square) error lower than 0.250 centimeters. The binned error analysis revealed an underestimation (approximately 10 percent) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 centimeters. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 centimeters. While the accuracy is relatively similar between 1.0 and 5.0 centimeters for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (less than 5.0 centimeters). The expected error was defined as plus or minus 15 percent, with less than 68 percent of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (approximately 0.2 centimeters per decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.

MAIAC↗

Updated MISR Dark Water Research Aerosol Retrieval Algorithm - Part 1: Coupled 1.1 km Ocean Surface Chlorophyll a Retrievals with Empirical Calibration Corrections

As aerosol amount and type are key factors in the 'atmospheric correction' required for remote-sensing chlorophyll alpha concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chl(sub in situ) less than 1.5 mg m(exp -3), the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov- Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p greater than 0.1). We also compare MODIS-Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl less than 1.5 mg m(exp -3), MISR and MODIS show very good agreement: r = 0.96, MAE = 0.09, and RMSE = 0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS-Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.

Limbacher, James A.↗

The application of support vector machines to analysis of global satellite data sets from MlSR

The Multi-angle Imaging Spectro Radiometer (MISR) is one of a suite of five instruments onboard NASA's Terra EOS satellite, launched in December 1999. Typical satellite imagers view the earth from a single direction, but MISR's cameras image the earth simultaneously from nine different directions in four spectral bands. In this way, MISR provides unique multiangle information about solar radiation scattered from clouds, aerosols and other terrestrial surfaces. One of the primary goals of the MISR mission is to improve our understanding of how clouds and aerosols affect the earth's global energy balance.

support vector machines↗

Multi-angle Imaging SpectroRadiometer

MISR views the sunlit Earth simultaneously at nine widely spaced angles and provides ongoing global coverage with high spatial detail. Its imagery is carefully calibrated to provide accurate measures of the brightness, contrast, and color of reflected sunlight. MISR provides new types of information for scientists studying Earth's climate, such as the regional and global distribution of different types of atmospheric particles and aerosols. The change in reflection at different view angles provides the means to distinguish aerosol types, cloud forms, and land surface cover. Combined with stereoscopic techniques, this enables construction of 3-D cloud models and estimation of the total amount of sunlight reflected by Earth's diverse environments. MISR was built for NASA by the Jet Propulsion Laboratory (JPL) in Pasadena, California. It is part of NASA's first Earth Observing System (EOS) spacecraft, the Terra spacecraft, which was launched into polar orbit from Vandenberg Air Force Base on December 18, 1999. MISR has been continuously providing data since February 24, 2000. [Mission Objectives] The MISR instrument acquires systematic multi-angle measurements for global monitoring of top-of-atmosphere and surface albedos and for measuring the shortwave radiative properties of aerosols, clouds, and surface scenes in order to characterize their impact on the Earth's climate. The Earth's climate is constantly changing -- as a consequence of both natural processes and human activities. Scientists care a great deal about even small changes in Earth's climate, since they can affect our comfort and well-being, and possibly our survival. A few years of below-average rainfall, an unusually cold winter, or a change in emissions from a coal-burning power plant, can influence the quality of life of people, plants, and animals in the region involved. The goal of NASA's Earth Observing System (EOS) is to increase our understanding of the climate changes that are occurring on our planet, and the reasons for these changes, so we are better equipped to anticipate and prepare for the future. The MISR instrument is a part of EOS. Its role is to measure the amount of sunlight scattered in different directions under natural conditions. These measurements will help quantify the amount of solar energy that heats the Earth's surface and atmosphere, and the changes that occur in them over the lifetime of the MISR instrument. From the MISR observations, we are also learning more about those components of the Earth's environment that scatter sunlight: particles in the atmosphere, the planet's surface, and clouds. MISR monitors changes in surface reflection properties, in atmospheric aerosol content and composition, and in cloudiness. Scientists use these data to study land use changes, air pollution, volcanic eruptions, as well as processes such as desertification, deforestation, and soil erosion. As part of the EOS program, computer models that predict future climate will be improved by the results of these studies. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

Diner, David J.↗

Mapping Snow Grain Size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Lyapustin, Alexei↗

MISR CMVs and Multiangular Views of Tropical Cyclone Inner-Core Dynamics

Multi-camera stereo imaging of cloud features from the MISR (Multiangle Imaging SpectroRadiometer) instrument on NASA's Terra satellite provides accurate and precise measurements of cloud top heights (CTH) and cloud motion vector (CMV) winds. MISR observes each cloudy scene from nine viewing angles (Nadir, +/-26(sup o), +/-46(sup o), +/-60(sup o), +/-70(sup o)) with approximatel 275-m pixel resolution. This paper provides an update on MISR CMV and CTH algorithm improvements, and explores a high-resolution retrieval of tangential winds inside the eyewall of tropical cyclones (TC). The MISR CMV and CTH retrievals from the updated algorithm are significantly improved in terms of spatial coverage and systematic errors. A new product, the 1.1-km cross-track wind, provides high accuracy and precision in measuring convective outflows. Preliminary results obtained from the 1.1-km tangential wind retrieval inside the TC eyewall show that the inner-core rotation is often faster near the eyewall, and this faster rotation appears to be related linearly to cyclone intensity.

hurricanes↗