Standard Moderate Resolution Imaging Spectroradiometer Research Articles

Sensitivity of Multispectral Imager Liquid Water Cloud Microphysical Retrievals to the Index of Refraction

A cloud property retrieved from multispectral imagers having spectral channels in the shortwave infrared (SWIR) and/or midwave infrared (MWIR) is the cloud effective particle radius (CER), a radiatively relevant weighting of the cloud particle size distribution. The physical basis of the CER retrieval is the dependence of SWIR/MWIR cloud reflectance on the cloud particle single scattering albedo, which in turn depends on the complex index of refraction of bulk liquid water (or ice) in addition to the cloud particle size. There is a general consistency in the choice of the liquid water index of refraction by the cloud remote sensing community, largely due to the few available independent datasets and compilations. Here we examine the sensitivity of CER retrievals to the available laboratory index of refraction datasets in the SWIR and MWIR using the retrieval software package that produces NASA’s standard Moderate Resolution Imaging Spectroradiometer (MODIS)/Visible Infrared Imaging Radiometer suite (VIIRS) continuity cloud products. The sensitivity study incorporates two laboratory index of refraction datasets that include measurements at supercooled water temperatures, one in the SWIR and one in the MWIR. Neither has been broadly utilized in the cloud remote sensing community. It is shown that these two new datasets can significantly change CER retrievals (e.g., 1–2 µm) relative to common datasets used by the community. Further, index of refraction data for a 265 K water temperature gives more consistent retrievals between the two spectrally distinct 2.2 µm atmospheric window channels on MODIS and VIIRS. As a result, 265 K values from the SWIR and MWIR index of refraction datasets were adopted for use in the production version of the continuity cloud product. The results indicate the need to better understand temperature-dependent bulk water absorption and uncertainties in these spectral regions.

Comprehensive accuracy assessment of MODIS daily snow cover products and gap filling methods

The accuracy of the standard Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products (Collection 5) and several of the most common and computationally frugal gap-filling methods were validated using 12 years of daily observations from over 800 Snow Telemetry (SNOTEL) stations. While several factors affect snow cover accuracy to some extent, the largest controls are associated with land cover type; accuracy fell from the maximum in croplands (94.9%) to the minimum for observations with a land cover type of water (79.5%). The second largest impact on accuracy is attributed to the changes in solar zenith angle, where accuracy decreased from a maximum of ∼96% at 42° to a minimum of ∼84% at 58°. Based on the results of this work, the highest accuracy binary daily snow cover dataset can be achieved by reclassifying the fractional snow band using a no-snow/snow threshold of 10 from the MODIS Terra sensor. Gap-filling is best accomplished by a temporal filter on the same dataset, and the window length depends on the desired completeness of the final dataset and the amount of missing values in the area of interest. Decreases in accuracy caused by this gap-filling method taper off after 7 days. Therefore, if a larger temporal window is needed to adequately fill in a time series it is best to extend the window to the largest gap present.

A Multilayer Surface Temperature, Surface Albedo and Water Vapor Product of Greenland from MODIS.

A multilayer, daily ice-surface temperature (IST)-albedo-water vapor product of Greenland, extending from March 2000 through December 2016, has been developed using standard MODerate-resolution Imaging Spectroradiometer (MODIS) data products from the Terra satellite. To meet the needs of the ice sheet modeling community, this new Earth Science Data Record (ESDR) is provided in a polar stereographic projection in NetCDF format, and includes the existing standard MODIS Collection-6.1 IST and derived melt maps, and Collection 6 snow albedo and water vapor maps, along with ancillary data, and is provided at a spatial resolution of ~0.78 km. This ESDR enables relationships between IST, surface melt, albedo and water vapor to be evaluated easily. We show examples of the components of the ESDR and describe some uses of the ESDR such as for comparison with skin temperature, albedo and water vapor output from Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2). Additionally we show validation of the MODIS IST using in situ and aircraft data, and validation of MERRA-2 skin temperature maps using MODIS IST and in situ data. The ESDR has been assigned a DOI and will be available through the National Snow and Ice Data Center by the summer of 2018.

Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

Abstract. Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years, offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European water bodies in or near the Alps based on the extensive AVHRR 1 km data record (1989–2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and MetOp-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with ERA-interim reanalysis data from the European Centre for Medium-range Weather Forecasts. The resulting LSWTs were extensively compared with in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of −0.5 to 0.6 K and 1.0 to 1.6 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. An inter-comparison with the standard Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature product exhibits RMSEs and biases in the range of 0.6 to 0.9 and −0.5 to 0.2 K, respectively. The cross-platform consistency of the retrieval was found to be within ~ 0.3 K. For one lake, the satellite-derived trend was compared with the trend of in situ measurements and both were found to be similar. Thus, orbital drift is not causing artificial temperature trends in the data set. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT for all of Europe to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.

The Intercomparison of Satellite-Derived and In Situ Profiles of Droplet Effective Radii in Marine Stratocumulus Clouds

It was found that the satellite spectral measurements can be used not only for the determination of cloud top effective radii but also for retrieving the effective radii vertical profile in some specific cases (e.g., for low marine stratocumulus clouds with negligible effects of horizontal variability). The inversion algorithm was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) radiances (at the wavelengths of 0.865, 1.24, 1.6, and 2.1 μm), and the results were validated against in situ airborne measurements of cloud vertical profiles of the effective radius. We found that the results were more reliable for the cases with large vertical gradients of droplet sizes. The new retrieval method, based on the optimal estimation approach, enabled a higher accuracy of the liquid water path estimate than that produced with the standard MODIS vertically homogeneous algorithm.

Evaluation of a cloud-gap-filled MODIS daily snow cover product over the Pacific Northwest USA

Summary The cloud-gap-filled (CGF) method developed by Hall et al. (2010) is a novel method for efficiently mitigating cloud obscuration using the most recently available cloud-free observations from prior days at each pixel. In this paper, we extend this method not only using prior observations but also using subsequent observations. Four CGF using 3 or 5 days prior or subsequent observations are applied to the standard MODerate resolution Imaging Spectroradiometer (MODIS) snow cover product and are evaluated against 3-years, in situ observations at 244 SNOwpack TELemetry (SNOTEL) stations. Results indicate that daily CGF snow cover maps using prior or subsequent observations are obviously different due to daily cloud shifting. However, the monthly and annual cloud reductions are similar. Although the overall accuracies under all-sky conditions of CGF using prior or subsequent observations are very similar, CGF snow cover maps using subsequent images have less underestimation errors, except during the snow melting period, and higher snow accuracies. When the observation interval is expanded from 3 to 5 days, this method is able to fill more cloud obscurations and increase the overall accuracies by ∼30% and ∼37%, respectively; however, they introduce slightly more uncertainty. Monthly and regional analyses indicate that this method is more efficient during the snow-covered period or in high-elevation zones.

Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China

To improve the accuracy of the moderate resolution imaging spectroradiometer (MODIS) gross primary production (GPP) algorithm, it is critical to evaluate MODIS GPP production for different land cover types using ground-based measurements. In this paper the MODIS primary production products (MOD17) is evaluated by using site-specific input parameters to the algorithm and compared to the results of eddy covariance measurements over an old-growth forest. Direct comparisons suggest that 8-day GPP predicted by the standard MODIS algorithm was highly correlated with flux tower based GPP (r2 = 0.77, P<0.001), while with an average underestimation of 39%. The difference is substantial in magnitude mainly because the inputs of underestimated MODIS biome-specific parameters, maximum light use efficiency emax and MODIS derived fraction of photosynthetically active radiation. The modified MODIS algorithm GPP calculated with site-specific input parameters compares favorably with ground flux tower observations (r2 = 0.92, relative error = 7%). These results suggest that the MODIS GPP production is most likely underpredicted in forest sites with high primary production, and site-specific input parameters could help to improve the accuracy of MODIS GPP algorithm.

AOD–PM 2.5 Association: Paciorek and Liu Respond

AOD–PM _2.5 Association: Paciorek and Liu Respond

A comparative analysis of three different MODIS NDVI datasets for Alaska and adjacent Canada

Moderate Resolution Imaging Spectroradiometer (MODIS) data offer great potential for monitoring vegetation dynamics in Alaska. However, certain MODIS image quality issues, such as geometric distortion, have been analyzed and documented in high latitudes and regions distant from the Greenwich Meridian. To improve MODIS data usability, the Canada Centre for Remote Sensing (CCRS) developed a seven-band reflectance dataset (10 day composite) at 250 m resolution for Canada and North America. More recently, the US Geological Survey Earth Resources Observation and Science Center produced an eMODIS dataset that includes a 7 day composite of normalized difference vegetation index (NDVI) and surface reflectance data at 250 m, 500 m, and 1 km resolutions for the conterminous United States and Alaska. Although these two datasets are based on the same MODIS level 1B data as those of the standard MODIS products, they are processed to improve their use in high-latitude regions. In this study, we conducted a comparative analysis of the standard MODIS, CCRS MODIS, and eMODIS Alaska 250 m NDVI products using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images as a reference source. CCRS MODIS and eMODIS Alaska NDVI images have significantly improved geometric features over those of the standard MODIS product. Pixel-by-pixel comparisons of the MODIS datasets indicated that all retained the original MODIS radiometric characteristics, but considerable mismatches at the pixel level were found due to geometric distortions caused by resampling. All three MODIS datasets agreed well as images were degraded to 5 or 10 km resolution.

Case studies of aerosol remote sensing in the vicinity of clouds

Studying the spatial variability of aerosol properties in the vicinity of clouds is essential to our ability to determine aerosol direct and indirect effects on climate. In this paper, we describe aerosol observations collected near cloud edges by an airborne Sun photometer over dark ocean waters. Focusing on case studies of aerosol measurements near eight cloud edges within a dissipating stratiform cloud deck, we compare the airborne Sun photometer observations to retrievals of aerosol properties using the standard Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol algorithm applied to 500‐m‐resolution MODIS spectral reflectances. We find a persistent, spectrally neutral increase in the Sun photometer–derived aerosol optical depth (AOD) of up to 10% (0.015) in the 2‐km distances closest to the edges of several distinct clouds. At midvisible wavelengths, the MODIS AOD retrievals show similar increases toward cloud edges, although a larger increase in AOD is found in the MODIS along‐scan direction. At short‐wave infrared (SWIR) wavelengths (1240–2130 nm), the MODIS‐derived AOD increases near cloud edges are of the order of 0.03 and as such three times as large as the Sun photometer–derived values. Hence, in contrast to recently discussed “bluing” of aerosols near cloud edges, i.e., a preferential apparent increase in the visible reflectances of clear‐sky pixels due to 3‐D radiative transfer effects in the vicinity of clouds, we find a “reddening” of aerosols in the MODIS 500‐m‐resolution aerosol retrievals near clouds. This “reddening” in our study can be traced to larger absolute increases in SWIR reflectances when compared to visible reflectances near clouds, which in turn seem to stem from larger electronic cross talk in the MODIS SWIR bands (5–7). We note that the lack of “bluing” in our MODIS observations is likely due to the small geometric and optical thicknesses of the clouds considered.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Evaluation of SeaWiFS and MODIS chlorophyll‐a products in the Argentinean Patagonian Continental Shelf (38° S–55° S)

Field measurements of surface chlorophyll‐a concentration were used to evaluate for the first time the performance of the standard Moderate Resolution Imaging Spectroradiometer (MODIS) and both standard and regional Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) ocean colour algorithms in the Patagonian Continental Shelf (PCS) between 38° S and 55° S. The results showed that the regional algorithms did not significantly improve the global algorithm estimates. Moreover, the SeaWiFS OC4v4 algorithm, National Aeronautics and Space Administration (NASA) standard chlorophyll product, showed the best performance among all the algorithms examined. Nonetheless, all the global and local algorithms analysed showed uncertainties dependent on chlorophyll concentration. Low chlorophyll‐a concentration values tended to be overestimated and high values tended to be underestimated. A regional analysis within the PCS showed that higher uncertainties are found in the homogeneous side of the tidal fronts present in the PCS, in areas suggested to be optically complex case 2 waters, while a better result (less bias) was obtained in the southern mid‐shelf region. We discuss the probable reasons and provide possible explanations of the regional differences in the performance of the algorithms.

Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach

Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP model for the conterminous U.S. In the following four-step process, we first trained the SVM to predict flux-based GPP from 33 AmeriFlux sites between 2000 and 2003 using three remotely-sensed variables (land surface temperature, enhanced vegetation index (EVI), and land cover) and one ground-measured variable (incident shortwave radiation). Second, we evaluated model performance by predicting GPP for 24 available AmeriFlux sites in 2004. In this independent evaluation, the SVM predicted GPP with a root mean squared error (RMSE) of 1.87 gC/m 2/day and an R 2 of 0.71. Based on annual total GPP at 15 AmeriFlux sites for which the number of 8-day averages in 2004 was no less than 67% (30 out of a possible 45), annual SVM GPP prediction error was 32.1% for non-forest ecosystems and 22.2% for forest ecosystems, while the standard Moderate Resolution Imaging Spectroradiometer GPP product (MOD17) had an error of 50.3% for non-forest ecosystems and 21.5% for forest ecosystems, suggesting that the regionally tuned SVM performed better than the standard global MOD17 GPP for non-forest ecosystems but had similar performance for forest ecosystems. The most important explanatory factor for GPP prediction was EVI, removal of which increased GPP RMSE by 0.85 gC/m 2/day in a cross-validation experiment. Third, using the SVM driven by remote sensing data including incident shortwave radiation, we predicted 2004 conterminous U.S. GPP and found that results were consistent with expected spatial and temporal patterns. Finally, as an illustration of SVM GPP for ecological applications, we estimated maximum light use efficiency ( e max), one of the most important factors for standard light use efficiency models, for the conterminous U.S. by integrating the 2004 SVM GPP with the MOD17 GPP algorithm. We found that e max varied from ∼ 0.86 gC/MJ in grasslands to ∼ 1.56 gC/MJ in deciduous forests, while MOD17 e max was 0.68 gC/MJ for grasslands and 1.16 gC/MJ for deciduous forests, suggesting that refinements of MOD17 e max may be beneficial.

Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol‐free Vegetation Index (AFRI) on MODIS images

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R 2 = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

An extensive bio-optical data set from field measurements was used to evaluate the performance of standard Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color (in-water) algorithms in the Baltic Sea, which represents an example of optically complex Case 2 waters with high concentration of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities, chlorophyll a concentration (Chl a), and absorption coefficient of CDOM, which were taken on 25 cruises between 1993 and 2001. The data cover a wide range of variability with Chl a in surface waters from about 0.3 to 100 mg m −3. All the MODIS pigment algorithms examined as well as the SeaWiFS OC4v4 algorithm showed a systematic and large overestimation in chlorophyll retrievals. The mean systematic and random errors based on our entire data set exceeded 150% or even 200% in some cases, making these standard algorithms inadequate for pigment determinations in the Baltic. Although new parameterization of the standard pigment algorithms based on our field measurements in the Baltic resulted in a significant reduction of errors, the overall performance of such regionally tuned algorithms remained unsatisfactory. For example, the mean normalized bias (MNB) for the regionally tuned MODIS chlor_a_2 algorithm was reduced to 26% (from over 200% for the standard algorithm), but the root mean square (RMS) error was still large (>100%). The MODIS K_490 algorithm for estimating the diffuse attenuation coefficient of downwelling irradiance showed the best performance among all the algorithms examined. With the new coefficients based on our field data, the regional version of this algorithm showed an acceptable level of errors, MNB=4% and RMS=30%. In addition to the apparent problems of the standard in-water bio-optical algorithms, we found that the atmospheric correction currently in use for MODIS and SeaWiFS imagery usually fails to retrieve upwelling radiances emerging from the Baltic Sea. The match-up comparisons of the coincident in situ and satellite determinations of normalized water-leaving radiances showed generally poor agreement, especially in the blue spectral region. It appears that new approaches for ocean color algorithms are required in the Baltic Sea.

Invariant object recognition with the adaptive resonance (ART) network

The cloud-gap-filled (CGF) method developed by Hall et al. (2010) is a novel method for efficiently mitigating cloud obscuration using the most recently available cloud-free observations from prior days at each pixel. In this paper, we extend this method not only using prior observations but also using subsequent observations. Four CGF using 3 or 5 days prior or subsequent observations are applied to the standard MODerate resolution Imaging Spectroradiometer (MODIS) snow cover product and are evaluated against 3-years, in situ observations at 244 SNOwpack TELemetry (SNOTEL) stations. Results indicate that daily CGF snow cover maps using prior or subsequent observations are obviously different due to daily cloud shifting. However, the monthly and annual cloud reductions are similar. Although the overall accuracies under all-sky conditions of CGF using prior or subsequent observations are very similar, CGF snow cover maps using subsequent images have less underestimation errors, except during the snow melting period, and higher snow accuracies. When the observation interval is expanded from 3 to 5 days, this method is able to fill more cloud obscurations and increase the overall accuracies by ∼30% and ∼37%, respectively; however, they introduce slightly more uncertainty. Monthly and regional analyses indicate that this method is more efficient during the snow-covered period or in high-elevation zones.