MODIS Airborne Simulator Research Articles

Improvements of on-orbit characterization of Terra MODIS short-wave infrared spectral bands out-of-band responses

The short-wave infrared (SWIR) bands (5 to 7, 26) of Terra Moderate Resolution Imaging Spectroradiometer (MODIS), which are colocated with the midwave infrared bands (20 to 25) on the short- and mid-wave infrared focal plane assembly, have a known issue related to 5.3-μm out-of-band (OOB) thermal leak and electronic crosstalk that was identified prelaunch. As a result, a crosstalk correction algorithm was designed and implemented in the MODIS level 1B (L1B) calibration. Shortly after the Terra launch, extensive efforts were undertaken to help characterize and mitigate the impact due to the OOB response and crosstalk on the SWIR on-orbit calibration and, consequently, the associated L1B data products. In addition, special night-time-day-mode operations have been regularly scheduled to derive the crosstalk correction coefficients. Since MODIS does not have a spectral band centered at 5.3 μm, its band 28 (7.325 μm) was chosen as the surrogate sending band to simulate the OOB radiances at 5.3 μm. This was largely based on the measurements from the MODIS Airborne Simulator spectrometer field campaigns in the early months after the Terra launch. In the case of Aqua MODIS, the magnitude of the SWIR crosstalk was much smaller and band 25 (4.52 μm) was found to be more effective as the sending band for the crosstalk correction. In recent years, the Terra MODIS photovoltaic (PV) long-wave infrared (LWIR) bands (27 to 30) electronic crosstalk has increased considerably, especially after the spacecraft safe-mode event occurred in February 2016. This accentuated degradation in the PV LWIR performance has also impacted the performance of the SWIR crosstalk correction and thus its calibration and data quality. We examine the use of band 25 as the sending band for the Terra MODIS SWIR crosstalk correction and compare its performance with that based on band 28 as the sending band. Results indicate an improvement of on-orbit gain stability for the SWIR calibration and reduced detector to detector and subframe to subframe striping in the calibrated L1B imagery, especially during the period when the PV LWIR electronic crosstalk has become more severe. This approach has been implemented in the forward production of Terra MODIS Collection 6 and Collection 6.1, starting from July 2019, and is planned to be used for the future reprocessing of MODIS L1B and to help improve the mission-long reflectance calibration and trending stability of the SWIR bands.

Influence of Satellite Sensor Pixel Size and Overpass Time on Undercounting of Cerrado/Savannah Landscape-Scale Fire Radiative Power (FRP): An Assessment Using the MODIS Airborne Simulator

The fire radiative power (FRP) of active fires (AFs) is routinely assessed with spaceborne sensors. MODIS is commonly used, and its 1 km nadir pixel size provides a minimum per-pixel FRP detection limit of ~5–8 MW, leading to undercounting of AF pixels with FRPs of less than around 10 MW. Since most biomes show increasing AF pixel frequencies with decreasing FRP, this results in MODIS failing to detect many fires burning when it overpasses. However, the exact magnitude of the landscape-scale FRP underestimation induced by this type of AF undercounting remains poorly understood, as does its sensitivity to sensor pixel size and overpass time. We investigate these issues using both 1 km spaceborne MODIS data and 50 m MODIS Airborne Simulator (MAS) observations of the Brazilian cerrado, a savannah-like environment covering 2 million km2 (>20%) of Brazil where fires are a frequent occurrence. The MAS data were collected during the 1995 SCAR-B experiment, and are able to be spatially degraded to simulate data from sensors with a wide variety of pixel sizes. We explore multiple versions of these MAS data to deliver recommendations for future satellite sensor design, aiming to discover the most effective sensor characteristics that provide negligible pixel-area related FRP underestimation whilst keeping pixels large enough to deliver relatively wide swath widths. We confirm earlier analyses showing 1 km MODIS-type observations fail to detect a very significant number of active fires, and find the degree of undercounting gets worse away from the early afternoon diurnal fire cycle peak (~ 15:00 local time). However, the effect of these undetected fires on the assessment of total landscape-scale FRP is far less significant, since they are mostly low FRP fires. Using two different approaches we estimate that the MODIS-type 1 km data underestimates landscape scale FRP by around a third, and that whilst the degree of underestimation worsens away from the diurnal fire cycle peak the effect of this maybe less important since there are far fewer fires present. MAS data degraded to a 200 m spatial resolution provides landscape-scale FRP totals almost indistinguishable from those calculated with the original 50 m MAS observations, and still provides a pixel size consistent with a wide swath imaging instrument. Our work provides a potentially useful guide for future mission developers aiming at active fire and FRP applications, and we conclude that such missions need operate at spatial resolutions no higher than 200 m if they rely on cooled, low-noise IR detectors. Further work confirming this for fire-affected biomes beyond the savannah-type environments studied here is recommended.

Detecting opaque and nonopaque tropical upper tropospheric ice clouds: A trispectral technique based on the MODIS 8–12 μm window bands

A trispectral technique is developed for detecting tropical upper tropospheric opaque (τ > 6) and nonopaque (τ < 6) ice clouds over ocean based on the brightness temperature differences between the MODIS 8.5 and 11 μm bands and between the 11 and 12 μm bands together with the MODIS detected cloud thermodynamic phase. The brightness temperature differences provide robust information for classifying ice clouds, as illustrated by the observations made by a lidar, a radar, and the MODIS Airborne Simulator over tropical ice anvil systems during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers‐Florida Area Cirrus Experiment. The trispectral technique for detecting tropical upper tropospheric opaque and nonopaque ice clouds is developed based on the analysis of 1 year of data, including MODIS infrared brightness temperatures at 8.5, 11, and 12 μm bands, MODIS‐derived ice cloud optical thicknesses, and cloud top heights from CALIPSO and CloudSat over a region (140°E–180°E, 0°N–20°N) in the Western Pacific Warm Pool. The accuracy of the present trispectral technique is above 80%. A 27 July 2007 MODIS granule over the chosen region is used to verify the trispectral technique. It is found that the classification from the trispectral technique is consistent with a classification based directly on the MODIS ice cloud optical thicknesses. The effects of the variations in the MODIS viewing zenith angle on the detection are found to be negligible. The CALIPSO and CloudSat observations used to develop the classification are more sensitive than MODIS to the height and presence of optically thin cirrus. These differences in cloud heights were found to have a negligible impact on the final detection results.

Retrieval of aerosol optical depth in vicinity of broken clouds from reflectance ratios: case study

Abstract. A recently developed reflectance ratio (RR) method for the retrieval of aerosol optical depth (AOD) is evaluated using extensive airborne and ground-based data sets collected during the Cloud and Land Surface Interaction Campaign (CLASIC) and the Cumulus Humilis Aerosol Processing Study (CHAPS), which took place in June 2007 over the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains site. A detailed case study is performed for a field of single-layer shallow cumuli observed on 12 June 2007. The RR method is applied to retrieve the spectral values of AOD from the reflectance ratios measured by the MODIS Airborne Simulator (MAS) for two pairs of wavelengths (660 and 470 nm, 870 and 470 nm) collected at a spatial resolution of 0.05 km. The retrieval is compared with an independent AOD estimate from three ground-based Multi-filter Rotating Shadowband Radiometers (MFRSRs). The interpolation algorithm that is used to project MFRSR point measurements onto the aircraft flight tracks is tested using AOD derived from NASA Langley High Spectral Resolution Lidar (HSRL). The RR AOD estimates are in a good agreement (within 5%) with the MFRSR-derived AOD values for the 660-nm wavelength. The AODs obtained from MAS reflectance ratios overestimate those derived from MFRSR measurements by 15–30% for the 470-nm wavelength and underestimate the 870-nm AOD by the same amount.

Combining visible and infrared radiometry and lidar data to test simulations in clear and ice cloud conditions

Abstract. Measurements taken during the 2003 Pacific THORPEX Observing System Test (P-TOST) by the MODIS Airborne Simulator (MAS), the Scanning High-resolution Interferometer Sounder (S-HIS) and the Cloud Physics Lidar (CPL) are compared to simulations performed with a line-by-line and multiple scattering modeling methodology (LBLMS). Formerly used for infrared hyper-spectral data analysis, LBLMS has been extended to the visible and near infrared with the inclusion of surface bi-directional reflectance properties. A number of scenes are evaluated: two clear scenes, one with nadir geometry and one cross-track encompassing sun glint, and three cloudy scenes, all with nadir geometry. CPL data is used to estimate the particulate optical depth at 532 nm for the clear and cloudy scenes and cloud upper and lower boundaries. Cloud optical depth is retrieved from S-HIS infrared window radiances, and it agrees with CPL values, to within natural variability. MAS data are simulated convolving high resolution radiances. The paper discusses the results of the comparisons for the clear and cloudy cases. LBLMS clear simulations agree with MAS data to within 20% in the shortwave (SW) and near infrared (NIR) spectrum and within 2 K in the infrared (IR) range. It is shown that cloudy sky simulations using cloud parameters retrieved from IR radiances systematically underestimate the measured radiance in the SW and NIR by nearly 50%, although the IR retrieved optical thickness agree with same measured by CPL. MODIS radiances measured from Terra are also compared to LBLMS simulations in cloudy conditions, using retrieved cloud optical depth and effective radius from MODIS, to understand the origin for the observed discrepancies. It is shown that the simulations agree, to within natural variability, with measurements in selected MODIS SW bands. The impact of the assumed particles size distribution and vertical profile of ice content on results is evaluated. Sensitivity is much smaller than differences between measured and simulated radiances in the SW and NIR. The paper dwells on a possible explanation of these contradictory results, involving the phase function of ice particles in the shortwave.

Remote sensing of radiative and microphysical properties of clouds during TC4: Results from MAS, MASTER, MODIS, and MISR

The Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) and MODIS/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Airborne Simulator (MASTER) were used to obtain measurements of the bidirectional reflectance and brightness temperature of clouds at 50 discrete wavelengths between 0.47 and 14.2 μm (12.9 μm for MASTER). These observations were obtained from the NASA ER‐2 aircraft as part of the Tropical Composition, Cloud and Climate Coupling (TC4) experiment conducted over Central America and surrounding Pacific and Atlantic Oceans between 17 July and 8 August 2007. Multispectral images in eleven distinct bands were used to derive a confidence in clear sky (or alternatively the probability of cloud) over land and ocean ecosystems. Based on the results of individual tests run as part of the cloud mask, an algorithm was developed to estimate the phase of the clouds (liquid water, ice, or undetermined phase). The cloud optical thickness and effective radius were derived for both liquid water and ice clouds that were detected during each flight, using a nearly identical algorithm to that implemented operationally to process MODIS cloud data from the Aqua and Terra satellites (Collection 5). This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS and MASTER data in TC4, is quite capable of distinguishing both liquid water and ice clouds during daytime conditions over both land and ocean. The cloud optical thickness and effective radius retrievals use five distinct bands of the MAS (or MASTER), and these results were compared with nearly simultaneous retrievals of marine liquid water clouds from MODIS on the Terra spacecraft. Finally, this MODIS‐based algorithm was adapted to Multiangle Imaging SpectroRadiometer (MISR) data to infer the cloud optical thickness of liquid water clouds from MISR. Results of this analysis are compared and contrasted.

Comparing irradiance fields derived from Moderate Resolution Imaging Spectroradiometer airborne simulator cirrus cloud retrievals with solar spectral flux radiometer measurements

During the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment, the Moderate Resolution Imaging Spectroradiometer (MODIS) airborne simulator (MAS) and the solar spectral flux radiometer (SSFR) operated on the same aircraft, the NASA ER‐2. While MAS provided two‐dimensional horizontal fields of cloud optical thickness and effective ice particle radius, the SSFR measured spectral irradiance in the visible to near‐infrared wavelength range (0.3–1.7 μm). The MAS retrievals, along with vertical profiles from a combined radar/lidar system on board the same aircraft were used to construct three‐dimensional cloud fields, which were input into Monte Carlo radiative transfer models. The simulated field of spectral albedo (ratio of reflected upwelling to incident downwelling irradiance) was compared with the SSFR measurements. For two cases, the relative importance of spatial cloud heterogeneities, various approximations of the single scattering parameters, vertical structure, cirrus optical thickness, and ice crystal effective radius was studied.

ADEOS-II/GLI snow/ice products — Part I: Scientific basis

Algorithms for retrieval of aerosol and snow/ice properties in polar and mid-latitude regions are described. These include (i) a cloud mask, (ii) a snow/sea ice discriminator, (iii) algorithms for retrieval of snow properties (snow grain size and impurities) and (iv) surface temperature of snow/ice and open ocean water. The cloud mask and snow/ice discriminator are used for cloud clearing as well as surface classification. For cloud-free pixels that are determined to be snow-covered an algorithm for retrieval of snow grain size and impurity concentration has been constructed. Radiative transfer simulations of the coupled atmosphere-surface system are used to construct lookup tables that are used in the retrieval process which proceeds by comparing measured top-of-the-atmosphere (TOA) radiances with synthetic values obtained from the lookup tables. These algorithms are designed to be applicable to polar and mid-latitude regions, but can easily be extended for use in other regions with seasonal and permanent snow-cover. The surface temperature retrieval algorithm consists of two parts: one for retrieval of the snow/ice surface temperature (IST), and another one for the retrieval of open-ocean surface temperature (SST). A multilinear regression technique is used to determine the empirical coefficients in the expression for the surface temperature, and Arctic radiosonde data are used in conjunction with radiative transfer models to simulate the sensor-measured radiances. The algorithms have been extensively tested against synthetic data, Moderate Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS) and GLobal Imager (GLI) data as well as other data sources.

Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice-Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE

Abstract This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earth’s Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.

Estimation of combustion completeness based on fire‐induced spectral reflectance changes in a dambo grassland (Western Province, Zambia)

An experimental burn was performed in a dambo grassland, in the Western Province of Zambia, during the SAFARI 2000 Third Intensive Field Campaign. The main goal of this study was to analyse the possibility of estimating combustion completeness based on fire‐induced spectral reflectance changes in surface. Inverse, nonlinear relationships were obtained between combustion completeness and pre‐fire to post‐fire spectral reflectance changes, in the green, red, and near‐infrared spectral domains (equivalent to Landsat 7 ETM+ channels 2, 3, and 4). The coefficient of determination (R 2) varied from 0.50 for channel 4, to 0.57 for channel 3, and all the regressions were significant at the 95% confidence level. Thus, it may be feasible to treat combustion completeness as a variable whose values can be remotely estimated. However, its relationship with fire‐induced spectral reflectance changes is expected to exhibit some dependence on vegetation structure. The experimental burn was performed simultaneously with overpasses from the Terra satellite, and from the NASA ER‐2 research airplane carrying the 50‐channel MODIS Airborne Simulator (MAS) image spectrometer. Our results may be used in conjunction with imagery from these sensors, to support the development of operational approaches for combustion completeness estimation from remotely sensed data.

Using the “blue spike” to characterize biomass‐burning sites during Southern African Regional Science Initiative (SAFARI) 2000

During several flights of the ER‐2 while participating in the Southern African Regional Science Initiative (SAFARI 2000), the University of Wisconsin–Madison's Scanning High Resolution Interferometer Sounder (S‐HIS) obtained spectra containing isolated fires within its field of view (FOV). These fire‐laden FOVs contain a spectral feature caused by rotational hot band transitions of CO2 near 2400 cm−1. Because of its location on the blue side of the 4.3 μm band of CO2, this feature is commonly referred to as the “blue spike.” Using this feature, we detected fires on four flights: 24 and 27 August and 6 and 7 September 2000. Fire locations are further verified by the ER‐2 pilot's flight logs and elevated brightness temperatures in the thermal detectors of the MODIS Airborne Simulator (MAS) also on board the ER‐2. Using line‐by‐line radiative transfer calculations (Genln2) with corrections for a fire's extreme high temperatures (HiTemp), we model S‐HIS spectra for various scenes: background (cool surface and cool atmosphere), smoldering (warm surface and cool atmosphere), hot gas layer (cool surface and warm atmosphere), and fire (hot surface and hot atmosphere) cases. Using the controlled burn in the Timbavati Game Reserve on 7 September 2000 as a test case, we spectrally modeled the blue spike feature seen in the spectra obtained by S‐HIS while the ER‐2 flew over the fire. For this case, we found that ∼4.12 ± 0.05% of the FOV contained the hot gas layer while ∼0.23 ± 0.05% was actively burning. Originally viewed as a straightforward task of using the blue spike to characterize the fire temperature and size (fraction of S‐HIS FOV), our analysis shows that numerous variables, including amount of carbon dioxide, amount of water vapor, and the temperature near the fire, play significant roles in the blue spike's shape and spectral position.

Simultaneous retrieval of aerosol and thin cirrus optical depths using MODIS airborne simulator data during CRYSTAL‐FACE and CLAMS

We have developed a new methodology to simultaneously retrieve the optical depths of aerosols and thin cirrus clouds over the oceans by using the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) data. This methodology takes advantage of the fact that the visible (or a near‐infrared window) reflectance from cirrus can be characterized by its correlation to the reflectance from a near‐infrared band located in a strong water vapor absorption region. Once the reflectance due to cirrus has been removed, the residual reflectance in these bands is used to retrieve aerosol optical depth. Cirrus cloud properties are subsequently obtained by minimizing the difference between observed and calculated reflectance values stored in look‐up tables that are constructed a priori and correlated to the retrieved aerosol optical depth. Examination of the data gathered from the recent Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL‐FACE) and Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaigns demonstrates that our retrievals are consistent with both in‐situ and ground based measurements.

Remote Sensing of Liquid Water and Ice Cloud Optical Thickness and Effective Radius in the Arctic: Application of Airborne Multispectral MAS Data

A multispectral scanning spectrometer was used to obtain measurements of the bidirectional reflectance and brightness temperature of clouds, sea ice, snow, and tundra surfaces at 50 discrete wavelengths between 0.47 and 14.0 μm. These observations were obtained from the NASA ER-2 aircraft as part of the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) Arctic Clouds Experiment, conducted over a 1600 km × 500 km region of the north slope of Alaska and surrounding Beaufort and Chukchi Seas between 18 May and 6 June 1998. Multispectral images in eight distinct bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) were used to derive a confidence in clear sky (or alternatively the probability of cloud) over five different ecosystems. Based on the results of individual tests run as part of this cloud mask, an algorithm was developed to estimate the phase of the clouds (liquid water, ice, or undetermined phase). Finally, the cloud optical thickness and effective radius were derived for both water and ice clouds that were detected during one flight line on 4 June. This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS data in Alaska, is quite capable of distinguishing clouds from bright sea ice surfaces during daytime conditions in the high Arctic. Results of individual tests, however, make it difficult to distinguish ice clouds over snow and sea ice surfaces, so additional tests were added to enhance the confidence in the thermodynamic phase of clouds over the Chukchi Sea. The cloud optical thickness and effective radius retrievals used three distinct bands of the MAS, with a recently developed 1.62- and 2.13-μm-band algorithm being used quite successfully over snow and sea ice surfaces. These results are contrasted with a MODIS-based algorithm that relies on spectral reflectance at 0.87 and 2.13 μm.

Evaluation of MODIS thermal IR band L1B radiances during SAFARI 2000

On 11 September 2000, a NASA ER‐2 aircraft underflew the Terra spacecraft during the Southern Africa Regional Science Initiative (SAFARI) 2000 field experiment. The Scanning High Resolution Interferometer Sounder (S‐HIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS), and Cloud Physics Lidar (CPL) instruments onboard the ER‐2 have provided a data set for a first look at the accuracy of the Collect 3 (L1B Version 3.X) Terra MODIS L1B radiances for the thermal infrared (TIR) bands. Based on comparisons between MODIS and S‐HIS nadir viewing data and an estimation of the existing uncertainties, the radiances of most MODIS TIR bands are found to be very near or within specification. There does not appear to be a significant influence by electronic cross talk on the midwave IR (MWIR) bands for the low reflectance, flat thermal scenes used in the evaluation. Longwave IR (LWIR) split window band residuals are within 0.1 K with an estimated uncertainty of ±0.13 K, raising confidence in their accuracy. Midtropospheric water vapor band residuals are also within specification, despite known detector striping in these bands. Residuals in the LWIR upper tropospheric CO2 bands 34–36 exceed specification. However, these are not considered strong indications of L1B performance issues due to possible undocumented uncertainty in the altitude correction for the unmeasured atmosphere above the ER‐2 aircraft level.

On the retrieval of columnar aerosol mass and CCN concentration by MODIS

A parameterization is introduced for the derivation of columnar aerosol mass concentration (AMC) and cloud condensation nucleus concentration (CCNC) from the primary aerosol products of Moderate‐Resolution Imaging Spectroradiometer (MODIS). The method relies on the scaling between AMC and optical depth with a proportionality constant dependent on MODIS‐derived reff, η (contribution of the accumulation mode radiance to the total radiance), ambient RH, and an assumed constant aerosol composition. The CCNC is derived from a parameterization of concentration as a function of the retrieved aerosol volume. By comparing with in situ data, it is shown that retrievals in dry ambient conditions (dust) are improved when using a proportionality constant dependent on reff and η obtained in the same pixel. In high‐humidity environments, the improvement in the new method is inconclusive because of the difficulty in accounting for the uneven vertical distribution of relative humidity. Additionally, two detailed comparisons of AMC and CCNC retrieved by the MODIS Airborne Simulator (MAS) algorithm and the new method are shown. The new method and MAS retrievals of AMC are within the same order of magnitude with respect to the in situ measurements of aerosol mass. However, the proposed method is closer to the in situ measurements than the MODIS retrievals. The retrievals of CCNC are also within the same order of magnitude for both methods. Finally, the new method is applied to an actual MODIS retrieval. Although no in situ data is available for comparison, it is shown that the proposed method yields more credible values than the MODIS retrievals.

Near concurrent MIR, SSM/T-2, and SSM/I observations over snow-covered surfaces

The airborne Millimeter-wave Imaging Radiometer (MIR) and MODIS Airborne Simulator (MAS) measurements over the Arctic region and the Midwest region of the US are used to derive surface emissivities ξ( ν) for three frequencies, ν=89, 150, and 220 GHz, as well as Normalized Difference Snow Index (NDSI) and R87 (0.87-μm reflectance). These derived parameters are compared with parameters estimated from near concurrent measurements made by the SSMI and SSM/T-2 over snow-covered areas. It is shown that the MIR-estimated ξ( ν) values at ν=89 and 150 GHz agree well with those estimated from the SSM/T-2 at ν=91 and 150 GHz, respectively. Low MIR-estimated ξ( ν) values are generally associated with high NDSI and R87 over the snow-covered areas. Over forested areas, more fluctuations in the values of MIR-estimated ξ( ν), NDSI and R87, as well as a reduction in polarization index (PI) at 37 and 85 GHz are observed. Both observations and results from radiative transfer calculations show a change in the difference between brightness temperatures ( T b) at 19 and 37 GHz, as well as PI at 37 and 85 GHz, when measured at satellite altitudes and at the surface. The amplitude of the T b difference and PI is reduced by about 10–15% from surface to high altitudes when integrated water vapor is ≤1.5 g/cm 2. This effect is readily correctable and requires consideration when validating satellite retrieval algorithms based on surface and low-elevation aircraft measurements.

Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data

This paper presents the status of land-surface temperature (LST) standard products retrieved from Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Based on estimates of the channel-dependence error and noise equivalent temperature difference (NEDT) and the calibration accuracy of MODIS thermal–infrared data, the impact of instrument performance on the accuracy of LST is discussed. A double-screen scheme based on the difference between the 5-km LST retrieved by the day/night LST algorithm and the aggregated 1-km LST retrieved by the generalized split-window algorithm, and the difference between daytime and nighttime LSTs, is proposed to remove the LSTs contaminated with cloud effects. The accuracy of daily MODIS LST product at 1-km resolution, which was produced by the generalized split-window algorithm, was validated in 11 clear-sky cases with in situ measurement data collected in field campaigns in 2000 and 2001. The MODIS LST accuracy is better than 1 K in the range from 263 to 300 K over Lake Titicaca in Bolivia, Mono Lake, Bridgeport grassland, and a rice field in Chico, CA, and Walker Lake, Nevada (NV), in the atmospheric column water vapor range from 0.4 to 3.0 cm. It is difficult to validate the daytime LST product over land sites rather than lakes with ground-based measurements alone because of the high spatial variations in the in situ LST measurement data, which was verified by the daytime data of the MODIS Airborne Simulator (MAS) over a grassland in Bridgeport, CA, on October 6, 2000. In six cases over a silt playa in Railroad Valley, NV, the 1-km MODIS LSTs are a few Kelvin degrees lower than the in situ measured LSTs because the surface emissivities inferred from land cover types in the split-window LST method are often overestimated in semi-arid and arid regions. After a correction with the difference between the 5-km LST retrieved by the day/night LST method and the LST aggregated from 1-km LSTs retrieved by the split-window method, the MODIS LSTs agree with in situ measured LSTs within ±1 K in the range 263–322 K for the six cases in Railroad Valley and one case of snowcover in Bridgeport, CA, leading a recommendation for use of the 5-km LST product retrieved by the day/night LST method in bare and sparse vegetated areas.

Evaluating the principles of cloud remote sensing with AVHRR and MAS imagery over SHEBA

A rigorous discrete ordinates radiative transfer formulation has been applied to two Advanced Very High Resolution Radiometer (AVHRR) images extracted from telemetry collected by the CCGS Des Groseilliers satellite tracking system during SHEBA to estimate cloud optical depth and effective radius of the cloud droplet size distribution. The two cases, from 2 and 3 June 1998, were chosen for analysis because (1) the images contained mostly liquid water clouds and (2) contemporaneous MODIS Airborne Simulator (MAS) overflight imagery was available for these AVHRR overpasses. The objective is to apply the same detailed radiative transfer formulation to both the MAS and AVHRR data so that the quality of the retrievals from the latter can be evaluated. Retrievals of cloud optical properties from MAS are assumed to be more reliable, because (1) all MAS channels have direct radiometric calibration, (2) the higher spatial resolution of MAS (50 m nadir versus 1.1 km nadir with AVHRR) should yield smaller uncertainties related to partially cloudy pixels in a given study area, and (3) effective droplet radius can be retrieved directly from the MAS 1.62‐μm channel without additional uncertainties involved with subtracting a thermal radiance component. Examination of the retrievals from both sensors in these two cases reveals considerable spatial variability (more than a factor of 2) in cloud optical depth, on a variety of scales ranging from tens of meters to tens of kilometers, even for relatively uniform liquid water clouds. Retrievals of cloud effective droplet radius from AVHRR are generally consistent with those from MAS, suggesting that AVHRR can be reliably used to estimate this quantity. However, AVHRR‐based retrievals of cloud optical depth are subject to large errors that result from small uncertainties in the absolute radiometric calibration of AVHRR channel 2. Using recalibration coefficients from one of the more robust AVHRR postlaunch calibration efforts, the cloud optical depths that we retrieved from NOAA 14 AVHRR channel 2 are consistently 30–50% larger than those obtained from MAS. The intercomparison of MAS and AVHRR retrievals of cloud optical depth also revealed errors with AVHRR due to partial cloud cover, and these errors are not immediately apparent when examining the AVHRR retrievals alone. If the AVHRR retrievals are averaged to spatial resolutions of order 10–30 km, they appear to become more stable for use in applications such as atmospheric energy budget calculations.

Improvement in thin cirrus retrievals using an emissivity‐adjusted CO2 slicing algorithm

CO2 slicing has been generally accepted as a useful algorithm for determining cloud top pressure (CTP) and effective cloud amount (ECA) for tropospheric clouds above 600 hPa. To date, the technique has assumed that the surface emissivity is that of a blackbody in the long‐wavelength infrared radiances and that the cloud emissivities in spectrally close bands are approximately equal. The modified CO2 slicing algorithm considers adjustments of both surface emissivity and cloud emissivity ratio. Surface emissivity is adjusted according to the surface types. The ratio of cloud emissivities in spectrally close bands is adjusted away from unity according to radiative transfer calculations. The new CO2 slicing algorithm is examined with Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) CO2 band radiance measurements over thin clouds and validated against Cloud Lidar System (CLS) measurements of the same clouds; it is also applied to Geostationary Operational Environmental Satellite (GOES) Sounder data to study the overall impact on cloud property determinations. For high thin clouds an improved product emerges, while for thick and opaque clouds there is little change. For very thin clouds, the CTP increases by about 10–20 hPa and RMS (root mean square bias) difference is approximately 50 hPa; for thin clouds, the CTP increase is about 10 hPa bias and RMS difference is approximately 30 hPa. The new CO2 slicing algorithm places the clouds lower in the troposphere.

Aerosol optical depth retrieval from GOES‐8: Uncertainty study and retrieval validation over South America

An algorithm for aerosol optical depth τ retrieval from the Geostationary Observational Environmental Satellite (GOES) series is described, where the darkest pixels are used to create a spatial composite of surface reflectance. The data are calibrated and corrected for atmospheric extinction to retrieve the surface reflectance which is then used to retrieved τ. Analysis suggests that τ retrieval uncertainty is ±18–34% depending on the certainty of the assumed radiative transfer model parameters. Retrieval uncertainty is less over low surface reflectances and at large scattering angles. The retrieval algorithm is validated against Sun‐sky radiometer τ measurements for aerosols emitted by biomass burning in South America during 1995 and 1998. The relative differences between observed and retrieved τ are within the estimated uncertainty, having correlations ranging from 0.78 to 0.97. Further, the GOES retrievals are compared to τ retrieved using the Moderate‐Resolution Imaging Spectroradiometer (MODIS) airborne simulator (MAS). The average relative difference in this comparison is 11%, thus retrieval validations are again within the estimated algorithm uncertainty. These results suggest that the GOES satellite can be used to monitor aerosols over land, while the agreement between MAS and GOES retrievals suggests the ability to combine the spectral abilities of MODIS with the temporal observations of GOES.