System Moderate Resolution Imaging Spectroradiometer Research Articles

Estimation of Effective Resolution for Daily MODIS Gridded Surface Reflectance Products

Data from wide field-of-view sensors have provided a wealth of information of the Earth's surface for many years. Of the more recent efforts, the National Aeronautics and Space Administration Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has been operational since 2000 for Terra (mid-2002 for Aqua). The wide field of view and high temporal frequency of MODIS provide near daily global coverage. However, this coverage comes at the cost of some level of spatial accuracy due to known effects of pixel size growth at increasing view zenith angles (VZAs). Further complicating the matter of variable viewing geometry is the application of a fixed grid for the geolocation of MODIS observations. An accurate understanding of this process is necessary to characterize the effective spatial resolution of daily MODIS gridded products. In this paper, we estimate the point spread function (PSF) of nominal 250-m MODIS gridded surface reflectance products from sequences of daily images over man-made large-size targets. Our results suggest that, in near optimal locations, the resolution of those MODIS gridded products varies between 344 and 835 m along rows and between 292 and 523 m along columns when the VZA ranges from 0° (nadir view) to 55 °, respectively. We also discuss some implications of the reliance on a global fixed grid, such as the MODIS sinusoidal grid, on the relation between location and spatial resolution.

Assessment of NASA GISS CMIP5 and Post-CMIP5 Simulated Clouds and TOA Radiation Budgets Using Satellite Observations. Part I: Cloud Fraction and Properties

Abstract Although many improvements have been made in phase 5 of the Coupled Model Intercomparison Project (CMIP5), clouds remain a significant source of uncertainty in general circulation models (GCMs) because their structural and optical properties are strongly dependent upon interactions between aerosol/cloud microphysics and dynamics that are unresolved in such models. Recent changes to the planetary boundary layer (PBL) turbulence and moist convection parameterizations in the NASA GISS Model E2 atmospheric GCM (post-CMIP5, hereafter P5) have improved cloud simulations significantly compared to its CMIP5 (hereafter C5) predecessor. A study has been performed to evaluate these changes between the P5 and C5 versions of the GCM, both of which used prescribed sea surface temperatures. P5 and C5 simulated cloud fraction (CF), liquid water path (LWP), ice water path (IWP), cloud water path (CWP), precipitable water vapor (PWV), and relative humidity (RH) have been compared to multiple satellite observations including the Clouds and the Earth’s Radiant Energy System–Moderate Resolution Imaging Spectroradiometer (CERES-MODIS, hereafter CM), CloudSat–Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; hereafter CC), Atmospheric Infrared Sounder (AIRS), and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Although some improvements are observed in the P5 simulation on a global scale, large improvements have been found over the southern midlatitudes (SMLs), where correlations increased and both bias and root-mean-square error (RMSE) significantly decreased, in relation to the previous C5 simulation, when compared to observations. Changes to the PBL scheme have resulted in improved total column CFs, particularly over the SMLs where marine boundary layer (MBL) CFs have increased by nearly 20% relative to the previous C5 simulation. Globally, the P5 simulated CWPs are 25 g m−2 lower than the previous C5 results. The P5 version of the GCM simulates PWV and RH higher than its C5 counterpart and agrees well with the AMSR-E and AIRS observations. The moister atmospheric conditions simulated by P5 are consistent with the CF comparison and provide a strong support for the increase in MBL clouds over the SMLs. Over the tropics, the P5 version of the GCM simulated total column CFs and CWPs are slightly lower than the previous C5 results, primarily as a result of the shallower tropical boundary layer in P5 relative to C5 in regions outside the marine stratocumulus decks.

Preliminary evaluation of S‐NPP VIIRS aerosol optical thickness

AbstractThe Visible Infrared Imaging Radiometer Suite (VIIRS) is the next‐generation polar‐orbiting operational environmental sensor with a capability for global aerosol observations. The VIIRS aerosol Environmental Data Record (EDR) is expected to continue the decade‐long successful multispectral aerosol retrieval from the NASA's Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) for scientific research and applications. Since the launch of the Suomi National Polar‐orbiting Partnership (S‐NPP), the VIIRS aerosol calibration/validation team has been continuously monitoring, evaluating, and improving the performance of VIIRS aerosol retrievals. In this study, the VIIRS aerosol optical thickness (AOT) at 550 nm EDR at current Provisional maturity level is evaluated by comparing it with MODIS retrievals and measurements from the Aerosol Robotic Network (AERONET) and the Maritime Aerosol Network (MAN). The VIIRS global mean AOT at 550 nm differs from that of MODIS by approximately −0.01 over ocean and 0.03 over land (0.00 and −0.01 for the collocated retrievals) but shows larger regional biases. Global validation with AERONET and with MAN measurements shows biases of 0.01 over ocean and −0.01 over land, with about 64% and 71% of retrievals falling within the expected uncertainty range established by MODIS over ocean (±(0.03 + 0.05AOT)) and over land (±(0.05 + 0.15AOT)), respectively. The VIIRS retrievals over land exhibit slight overestimation over vegetated surfaces and underestimation over soil‐dominated surfaces. These results show that the VIIRS AOT at 550 nm product provides a solid global data set for quantitative scientific investigations and environmental monitoring.

A thin cloud removal method from remote sensing image for water body identification

In this paper, a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm. Channels of 0.66 μm, 0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud. Two study cases were selected to validate the thin cloud removal method. One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) data, and the other with the Medium Resolution Spectral Imager (MERSI) and Visible and Infrared Radiometer (VIRR) data from Fengyun-3A (FY-3A). The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky. To the area contaminated by the thin cloud, the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal. The spatial distribution of the water body area could not be extracted before the thin cloud removal, while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud. The thin cloud removal method could improve the image quality and water body extraction precision effectively.

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R 2 = 0.90).

Integrating Remote Sensing Data with WRF for Improved Simulations of Oasis Effects on Local Weather Processes over an Arid Region in Northwestern China

AbstractLand use/cover types derived by satellite remote sensing data from the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) were used to replace the U.S. Geological Survey (USGS) data in the Weather Research and Forecasting Model (WRF). Simulations in this study were further improved by modifying the initial fields of WRF with soil temperature and moisture observations, because these two variables are important to producing “cold–wet island” effects. A series of WRF simulations were performed to describe microclimate characteristics and the local thermal circulation generated by the inhomogeneous surface over the Jinta oasis, which is located in Gansu—a northwestern province of China. Comparison between simulations and observations showed that the WRF results produced with observed soil temperature and moisture initializations agreed well with near-surface measurements of air temperature, relative humidity, and wind direction. Moreover, low temperatures over the oasis were found to coexist with high temperatures over the bare land, further leading to developments of local atmospheric circulation. The simulated winds over the oasis showed airmass divergence over the surface layer, triggering local circulation in the upper level. The integration of the MODIS land use/cover data with WRF and the initialization of WRF’s soil temperature and moisture with in situ observations improved the simulations in air temperature, relative humidity, and heat fluxes. These improvements enabled the WRF to reproduce the observed “cold and wet island” effects of the oasis.

Use of Landsat TM and EOS MODIS imaging technologies for estimation of winter wheat yield in the North China Plain

This study focuses on the methodologies of winter wheat yield prediction based on Land Satellite Thematic Map (TM) and Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS) imaging technologies in the North China Plain. Routine field measurements were initiated during the periods when the Landsat satellite passed over the study region. Five Landsat TM images were acquired. Wheat yields of the experimental sites were recorded after harvest. Spectral vegetation indices were calculated from TM and MODIS images. The correlation analysis among wheat yield and spectral parameters revealed that TM renormalized difference water index (RDWI) and MODIS near-infrared reflectance had the highest correlation with yield at grain-filling stages. The models from the best-fitting method were used to estimate wheat yield based on TM and MODIS data. The average relative error of the root mean square error (RMSE) of the predicted yield was smaller from TM than from MODIS.

A blended global snow product using visible, passive microwave and scatterometer satellite data

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.

Seasonal Variation of the Physical Properties of Marine Boundary Layer Clouds off the California Coast

Abstract Marine boundary layer (MBL) clouds can significantly regulate the sensitivity of climate models, yet they are currently poorly simulated. This study aims to characterize the seasonal variations of physical properties of these clouds and their associated processes by using multisatellite data. Measurements from several independent satellite datasets [International Satellite Cloud Climatology Project (ISCCP), Clouds and the Earth’s Radiant Energy System–Moderate Resolution Imaging Spectroradiometer (CERES–MODIS), Geoscience Laser Altimeter System (GLAS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)], in conjunction with balloon soundings from the mobile facility of the Atmospheric Radiation Measurement (ARM) program at Point Reyes and reanalysis products, are used to characterize the seasonal variations of MBL cloud-top and cloud-base heights, cloud thickness, the degree of decoupling between clouds and MBL, and inversion strength off the California coast. The main results from this study are as follows: (i) MBL clouds over the northeast subtropical Pacific in the summer are more prevalent and associated with a larger in-cloud water path than in winter. The cloud-top and cloud-base heights are lower in the summer than in the winter. (ii) Although the lower-tropospheric stability of the atmosphere is higher in the summer, the MBL inversion strength is only weakly stronger in the summer because of a negative feedback from the cloud-top altitude. Summertime MBL clouds are more homogeneous and are associated with lower surface latent heat flux than those in the winter. (iii) Seasonal variations of low-cloud properties from summer to winter resemble the downstream stratocumulus-to-cumulus transition of MBL clouds in terms of MBL depth, cloud-top and cloud-base heights, inversion strength, and spatial homogeneity. The “deepening–warming” mechanism of Bretherton and Wyant for the stratocumulus-to-trade-cumulus transition downstream of the cold eastern ocean can also explain the seasonal variation of low clouds from the summer to the winter, except that warming of the sea surface temperature needs to be taken as relative to the free-tropospheric air temperature, which occurs in the winter. The observed variation of low clouds from summer to winter is attributed to the much larger seasonal cooling of the free-tropospheric air temperature than that of the sea surface temperature.

Evaluation of Split-Window Land Surface Temperature Algorithms for Generating Climate Data Records

Land surface temperature (LST) is a key indicator of the Earth's surface energy and is used in a range of hydrological, meteorological, and climatological applications. As needed for most modeling and climate analysis applications, LST products that are generated from polar-orbiting meteorological satellite sensors have spatial resolutions from several hundred meters to several kilometers and have (quasi) daily temporal resolution. These sensors include the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (AVHRR), the earth observing system moderate resolution imaging spectroradiometer (MODIS), and the forthcoming visible/infrared imager radiometer suite (VIIRS) series, to be flown onboard the National Polar-Orbiting Operational Environmental Satellite System (VIIRS flights begin approximately 2009). Generally, split-window algorithms are used with these sensors to produce LST products. In this paper, we evaluated nine published LST algorithms (or, in some cases, their slight variants) to determine those that are most suitable for generating a consistent LST climate data record across these satellite sensors and platforms. A consistent set of moderate-resolution atmospheric transmission simulations were used in determining the appropriate coefficients for each algorithm and sensor (AVHRR, MODIS, and VIIRS) combination. Algorithm accuracy was evaluated over different view zenith angles, surface-atmosphere temperature combinations, and emissivity errors. Both simulated and actual remote sensing data were used in the evaluation. We found that the nine heritage algorithms can effectively be collapsed into three groups of highly similar performance. We also demonstrated the efficacy of an atmospheric path-length correction term that is added to the heritage algorithms. We conclude that the algorithms depending on both the mean and difference of band emissivities (Group 1 in our nomenclature) are most accurate and stable over a wide range of conditions, provided that the emissivity can be well estimated a priori . Where the emissivity cannot be well estimated, the Group 3 algorithms (which do not depend on the emissivity difference) modified with the path-length correction term perform better.

Comparison and analysis of two aerosol retrievals over the ocean in the Terra/Clouds and the Earth's Radiant Energy System–Moderate Resolution Imaging Spectroradiometer single scanner footprint data: 1. Global evaluation

Satellite aerosol remote sensing entered a new era with the deployment of advanced satellite imaging instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra and Aqua satellites. These new instruments provide the opportunity to learn more about aerosol properties than was possible using the simpler NOAA Advanced Very High Resolution Radiometer (AVHRR), which has been used to retrieve aerosol optical thickness for more than 20 years. Combining historical AVHRR and the more advanced MODIS aerosol retrievals to form a long‐term aerosol data record is critical for studying aerosol climate forcing. To achieve this objective, it is necessary to build a connection and establish consistency between the two retrievals through a careful evaluation of the two retrieval methods applied to the same data. As a first step in this effort, this paper exploits the potential of the Clouds and the Earth's Radiant Energy System (CERES) Single‐Scanner Footprint (SSF) data set that includes aerosol products derived from Terra MODIS data at the same locations using both the multichannel MODIS and the two‐channel AVHRR aerosol retrieval algorithms. The analysis examines the differences in the results seen over oceans on a global scale. It was found in a global mean sense that advancement in the aerosol retrieval over ocean from the MODIS algorithm relative to the AVHRR method is realized mostly in the improvement of the aerosol size parameter (ASP) rather than in the aerosol optical thickness (AOT). However, regional differences were observed in both AOT and ASP retrieved from the MODIS and AVHRR algorithms. These are examined further in the second part of this two‐part paper. Cloud contamination and surface roughness appear to affect both aerosol retrievals, effects that need further investigation.

Comparison and analysis of two aerosol retrievals over the ocean in the Terra/Clouds and the Earth's Radiant Energy System–Moderate Resolution Imaging Spectroradiometer single scanner footprint data: 2. Regional evaluation

The advanced multichannel Moderate Resolution Imaging Spectroradiometer (MODIS) and simple independent two‐channel Advanced Very High Resolution Radiometer (AVHRR) aerosol retrieval algorithms were compared regionally using the Terra/CERES‐MODIS Single Scanner Footprint (SSF) data. On average, it was found that the two methods tend to overestimate 0.66‐μm aerosol optical thickness (AOT) compared to AERONET surface observations in the original SSF data. If the most cloud‐free data are used, the mean satellite retrievals agree to within ±10% of the AERONET data. The MODIS near‐infrared (1.60‐μm) AOTs are in better agreement with the surface data than the AVHRR‐type retrievals. The satellite‐derived aerosol size parameters are 20–30% smaller than the surface‐based values with the MODIS values closer to the AERONET values than that of AVHRR‐type. The effects of aerosol model assumptions, cloud contamination, and surface roughness on the two aerosol retrievals were analyzed in detail with the careful classification of clear‐sky and surface roughness conditions. For most of the regions examined, the annual mean AOTs from the MODIS retrieval are 0.03 and 0.02 less than their AVHRR‐type counterparts at 0.66 and 1.60 μm, respectively. However, the MODIS values may exceed the AVHRR‐type values in regions where the prevailing aerosol type varies with season or is under an apparent influence of cloud or surface disturbance. Examination of the surface treatments used by the two retrieval methods indicates the need for improvement over very rough ocean surfaces, especially for the AVHRR method. The results indicate that aerosol model assumptions become important for regional retrievals and the dynamic aerosol models used in the MODIS retrieval are better suited for simultaneously measuring the regional variations in aerosol optical properties and their global mean values.

On-orbit performance of the Earth Observing System Moderate Resolution Imaging Spectroradiometer; first year of data

The Moderate Resolution Imaging Spectroradiometer (MODIS) completed the first year of science data acquisition on February 24, 2000. The overall performance of the sensor and the on-board calibration systems for this first year have been very good. Several features of the performance lead to characteristics in the data set that merit special attention. These items are sometimes called data product caveats, and are described here. Uncertainty budgets for the 0.555-μm band, the 1.240-μm band and the 12.000-μm band are presented at several days throughout this first year. The uncertainty is estimated to be decreasing with time during this period, and to be near 1.8% in reflectance factor for 0.555 μm, 1.9% for the reflectance factor product for the 1.240 μm band, and 0.7% for the 12.000-μm band at nadir at the end of the first year. Degradation of the solar diffuser is 2.3% at 0.412 μm, known to an uncertainty of ±0.5%. Solar diffuser degradation for wavelengths longer that 0.5 μm is indistinguishable from the uncertainty in trend determination for the first year. Mirror side degradation at 0.412 μm is 6±0.5%, with a mirror side difference of an additional 3%. The performance present in the data at the end of year 1 provides significant encouragement that many improvements in our understanding of the Earth system performance can and will be based on MODIS data during the coming years.