Moderate Resolution Imaging Spectroradiometer Collection Research Articles

An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 2: Application and validation in Asia

Since small aerosol particles are mostly anthropogenic, the fine-mode aerosol optical thickness (fAOT) can be used to infer PM2.5 amounts. However, satellite-based fAOT products such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS) are highly uncertain over land. An improved fAOT retrieval method called the look-up table–spectral deconvolution algorithm (LUT-SDA) was tested and improved using data from Asia. The improvement is achieved by accounting for seasonal changes instead of using constant annual mean values of the aerosol parameters used in the LUT-SDA derived from the Aerosol Robotic Network (AERONET) data from 2010 to 2014. Compared with the previous version of the LUT-SDA developed for Beijing, Hong Kong, and Osaka, the updated LUT-SDA generates more accurate fine-mode fractions (FMFs) with the total mean root-mean-square error (RMSE) decreasing from 0.24 to 0.18. The updated LUT-SDA was then applied to retrieve fAOT and was validated by retrievals from 45 AERONET sites over the period 2015 to 2016. A good accuracy was achieved by this method with 31% of the validation sites having >50% of retrievals falling within the estimated error (EE) envelope ± (0.05 + 0.15 × AERONET fAOT) and 42% of the validation sites having 40–50% of retrievals falling within the EE envelope. In the total validation and comparison with the MODIS Collection 6 fAOT, the fAOT retrievals from the LUT-SDA agreed more closely with AERONET retrievals, showing a low bias. About 48% of the LUT-SDA-based fAOT retrievals fell within the EE envelope (RMSE = 0.29), while ~22% of the MODIS-based fAOT retrievals fell within the EE envelope (RMSE = 0.42). The fAOT was significantly underestimated by the MODIS algorithm in most areas of Asia with many values of zero. This study demonstrates that the refined LUT-SDA method is valid for the large-scale estimation of fAOT from satellite images.

A Review of Ice Cloud Optical Property Models for Passive Satellite Remote Sensing

The current wealth of spaceborne passive and active measurements from ultraviolet to the infrared wavelengths provides an unprecedented opportunity to construct ice cloud bulk optical property models that lead to consistent ice cloud property retrievals across multiple sensors and platforms. To infer the microphysical and radiative properties of ice clouds from these satellite measurements, the general approach is to assume an ice cloud optical property model that implicitly assumes the habit (shape) and size distributions of the ice particles in these clouds. The assumption is that this ice optical property model will be adequate for global retrievals. In this review paper, we first summarize the key optical properties of individual particles and then the bulk radiative properties of their ensemble, followed by a review of the ice cloud models developed for application to satellite remote sensing. We illustrate that the random orientation condition assumed for ice particles is arguably justified for passive remote sensing applications based on radiometric measurements. The focus of the present discussion is on the ice models used by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Clouds and Earth’s Radiant Energy System (CERES) science teams. In addition, we briefly review the ice cloud models adopted by the Polarization and Directionality of the Earth’s Reflectance (POLDER) and the Himawari-8 Advanced Himawari Imager (AHI) for ice cloud retrievals. We find that both the MODIS Collection 6 ice model and the CERES two-habit model result in spectrally consistent retrievals.

Inference of an Optimal Ice Particle Model through Latitudinal Analysis of MISR and MODIS Data

The inference of ice cloud properties from remote sensing data depends on the assumed forward ice particle model, as they are used in the radiative transfer simulations that are part of the retrieval process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (MC6) ice cloud property retrievals are produced in conjunction with a single-habit ice particle model with a fixed degree of ice particle surface roughness (the MC6 model). In this study, we examine the MC6 model and five other ice models with either smoother or rougher surface textures to determine an optimal model to reproduce the angular variation of the radiation field sampled by the Multi-angle Imaging Spectroradiometer (MISR) as a function of latitude. The spherical albedo difference (SAD) method is used to infer an optimal ice particle model. The method is applied to collocated MISR and MODIS data over ocean for clouds with temperatures ≤233 K during December solstice from 2012–2015. The range of solar zenith angles covered by the MISR cameras is broader at the solstices than at other times of the year, with fewer scattering angles associated with sun glint during the December solstice than the June solstice. The results suggest a latitudinal dependence in an optimal ice particle model, and an additional dependence on the solar zenith angle (SZA) at the time of the observations. The MC6 model is one of the most optimal models on the global scale. In further analysis, the results are filtered by a cloud heterogeneity index to investigate cloudy scenarios that are less susceptible to potential 3D effects. Compared to results for global data, the consistency between measurements and a given model can be distinguished in both the tropics and extra-tropics. The SAD analysis suggests that the optimal model for thick homogeneous clouds corresponds to more roughened ice particles in the tropics than in the extra-tropics. While the MC6 model is one of the models most consistent with the global data, it may not be the most optimal model for the tropics.

Using the Gaofen-4 geostationary satellite to retrieve aerosols with high spatiotemporal resolution

GaoFen-4 (GF-4) is China’s first optical remote sensing geostationary satellite, which observes the Earth’s surface every 20 s. GF-4 has a gaze camera with a ground resolution of 50 m in the visible spectral bands that are sensitive to aerosol optical depth (AOD). AOD retrieval was completed using initial prior surface reflectance in ground-atmosphere decoupling. Surface reflectance was then updated using satellite measurements. The algorithm used in this study was based on two time-adjacent images of GF-4 (which have a constant viewing angle in the same area) and on the following two assumptions: (1) AOD varies quickly with time, but slowly with location and (2) surface reflectance varies quickly with location, but slowly with time. AOD retrieval was then accomplished using a lookup table strategy. The data from June 2016, in the North China Plain were selected for the AOD retrieval test. GF-4-derived AOD was validated using ground measurements from the aerosol robotic network and Sun–Sky radiometer network with a correlation coefficient of R = 0.794. The derived results agreed reasonably well with the moderate resolution imaging spectroradiometer collection 6.0 aerosol product, with a correlation coefficient of R = 0.893. The atmospheric distribution and changes in some parts of the North China Plain were analyzed using the aerosol products of GF-4. The results showed that it is feasible to use the time-series imaging method to conduct high spatiotemporal resolution aerosol inversion using GF-4’s data, which can be used for detecting changes in air pollution.

The Collection 6 MODIS burned area mapping algorithm and product

The two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board NASA's Terra and Aqua satellites have provided nearly two decades of global fire data. Here, we describe refinements made to the 500-m global burned area mapping algorithm that were implemented in late 2016 as part of the MODIS Collection 6 (C6) land-product reprocessing. The updated algorithm improves upon the heritage Collection 5.1 (C5.1) MCD64A1 and MCD45A1 algorithms by offering significantly better detection of small burns, a modest reduction in burn-date temporal uncertainty, and a large reduction in the extent of unmapped areas. Comparison of the C6 and C5.1 MCD64A1 products for fifteen years (2002–2016) on a regional basis shows that the C6 product detects considerably more burned area globally (26%) and in almost every region considered. The sole exception was in Boreal North America, where the mean annual area burned was 6% lower for C6, primarily as a result of a large increase in the number of small lakes mapped (and subsequently masked) at high latitudes in the upstream C6 input data. With respect to temporal reporting accuracy, 44% of the C6 MCD64A1 burned grid cells were detected on the same day as an active fire, and 68% within 2 days, which represents a substantial reduction in temporal uncertainty compared to the C5.1 MCD64A1 and MCD45A1 products. In addition, an areal accuracy assessment of the C6 burned area product undertaken using high resolution burned area reference maps derived from 108 Landsat image pairs is reported.

The impacts of recent drought on fire, forest loss, and regional smoke emissions in lowland Bolivia

Abstract. In the southern Amazon relationships have been established among drought, human activities that cause forest loss, fire, and smoke emissions. We explore the impacts of recent drought on fire, forest loss, and atmospheric visibility in lowland Bolivia. To assess human influence on fire, we consider climate, fire, and vegetation dynamics in an area largely excluded from human activities since 1979, Noel Kempff Mercado National Park (NK) in northeastern Bolivia. We use data from five sources: the Moderate Resolution Imaging Spectroradiometer Collection 6 active fire product (2001–2015) (MODIS C6), Global Fire WEather Database (GFWED) data (1982–2015), MODIS land cover data (2001–2010), MODIS forest loss data (2000–2012), and the regional extinction coefficient for the southwestern Amazon (i.e., Bext), which is derived from horizontal visibility data from surface stations at the World Meteorological Organization (WMO) level (1973–2015). The Bext is affected by smoke and acts as a proxy for visibility and regional fire emissions. In lowland Bolivia from 2001 to 2015, interannual Drought Code (DC) variability was linked to fire activity, while from 1982 to 2015, interannual DC variability was linked to Bext. From 2001 to 2015, the Bext and MODIS C6 active fire data for lowland Bolivia captured fire seasonality, and covaried between low- and high-fire years. Consistent with previous studies, our results suggest Bext can be used as a longer-term proxy of regional fire emissions in southwestern Amazonia. Overall, our study found drought conditions were the dominant control on interannual fire variability in lowland Bolivia, and fires within NK were limited to the Cerrado and seasonally inundated wetland biomes. Our results suggest lowland Bolivian tropical forests were susceptible to human activities that may have amplified fire during drought. Human activities and drought need to be considered in future projections of southern Amazonian fire, in regard to carbon emissions and global climate.

Neural network cloud top pressure and height for MODIS

Abstract. Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors. For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2 km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12 µm show at least 32 % (or 623 m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 430 m) reduction of MAE.

Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China

The Himawari-8 geostationary weather satellite, which is an Earth observing satellite launched in October 2014, has been applied in climate, environment, and air quality studies. Using hourly observation data from the Advanced Himawari Imager (AHI) on board Himawari-8, a new dark target algorithm was proposed to retrieve the aerosol optical depth (AOD) at 1 km and 5 km resolutions over mainland China. Because of the short satellite operation time and lack of AErosol RObotic NETwork (AERONET) sites across China, we cannot derive robust and representative surface reflectance relationships for the visible to near-infrared channels by atmospheric correction. Therefore, we inherited the empirical reflectance relationship from the Moderate Resolution Imaging Spectroradiometer (MODIS) and we used the AHI and MODIS spectral response functions to make the relationship more suitable for AHI. Ultimately, our AOD products can better reflect the regional characteristics with the AHI sensor. Seasonal averages showed that our product is more similar to MODIS Collection 6 (C6) Dark Target (DT) AOD than the Japan Aerospace Exploration Agency (JAXA) AHI AOD, but the difference is largest in winter. In addition, we evaluated several satellite retrieval products (our AHI AOD, JAXA AHI AOD, the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD, MODIS DT AOD, and MODIS DB AOD) against AERONET AOD from July 2016 to June 2017. The results showed that our AHI measurements demonstrate good agreement with, but exhibit a little overestimation, as compared to ground-based AERONET measurements with a correlation coefficient of 0.83 and an root-mean-square error (RMSE) of 0.112. The hourly validation also showed stable statistical results. A time series comparison with ground-based observations from two AERONET sites (Beijing-CAMS and XiangHe) showed that our AHI AOD products have trends as those in MODIS DB AOD, but that the bias in Beijing-CAMS is positive and higher than that in XiangHe. This error and the slight overestimation may be caused by the single continental aerosol model assumption and not considering the Normalized Difference Vegetation Index (NDVI).

Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba

Abstract. In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua (AODa) satellites and those measured using a sun photometer (AODSP) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa, AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AODta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data compared to AODSP, (f). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AODta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AODSP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS–sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.

Simulation of the brightness temperatures observed by the visible infrared imaging radiometer suite instrument (Erratum)

Clouds play a large role in the Earth’s global energy budget, but the impact of cirrus clouds is still widely questioned and researched. Cirrus clouds reside high in the atmosphere and due to cold temperatures are comprised of ice crystals. Gaining a better understanding of ice cloud optical properties and the distribution of cirrus clouds provides an explanation for the contribution of cirrus clouds to the global energy budget. Using radiative transfer models (RTMs), accurate simulations of cirrus clouds can enhance the understanding of the global energy budget as well as improve the use of global climate models. A newer, faster RTM such as the visible infrared imaging radiometer suite (VIIRS) fast radiative transfer model (VFRTM) is compared to a rigorous RTM such as the line-by-line radiative transfer model plus the discrete ordinates radiative transfer program. By comparing brightness temperature (BT) simulations from both models, the accuracy of the VFRTM can be obtained. This study shows root-mean-square error <0.2 K for BT difference using reanalysis data for atmospheric profiles and updated ice particle habit information from the moderate-resolution imaging spectroradiometer collection 6. At a higher resolution, the simulated results of the VFRTM are compared to the observations of VIIRS resulting in a <1.5 % error from the VFRTM for all cases. The VFRTM is validated and is an appropriate RTM to use for global cloud retrievals.

Simulation of the brightness temperatures observed by the visible infrared imaging radiometer suite instrument

Clouds play a large role in the Earth’s global energy budget, but the impact of cirrus clouds is still widely questioned and researched. Cirrus clouds reside high in the atmosphere and due to cold temperatures are comprised of ice crystals. Gaining a better understanding of ice cloud optical properties and the distribution of cirrus clouds provides an explanation for the contribution of cirrus clouds to the global energy budget. Using radiative transfer models (RTMs), accurate simulations of cirrus clouds can enhance the understanding of the global energy budget as well as improve the use of global climate models. A newer, faster RTM such as the visible infrared imaging radiometer suite (VIIRS) fast radiative transfer model (VFRTM) is compared to a rigorous RTM such as the line-by-line radiative transfer model plus the discrete ordinates radiative transfer program. By comparing brightness temperature (BT) simulations from both models, the accuracy of the VFRTM can be obtained. This study shows root-mean-square error <0.2 K for BT difference using reanalysis data for atmospheric profiles and updated ice particle habit information from the moderate-resolution imaging spectroradiometer collection 6. At a higher resolution, the simulated results of the VFRTM are compared to the observations of VIIRS resulting in a <1.5 % error from the VFRTM for all cases. The VFRTM is validated and is an appropriate RTM to use for global cloud retrievals.

Results From the Deep Convective Clouds-Based Response Versus Scan-Angle Characterization for the MODIS Reflective Solar Bands

The Terra and Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) scan mirror reflectance is a function of the angle of incidence (AOI) and was characterized prior to launch by the instrument vendor. The relative change of the prelaunch response versus scan angle (RVS) is tracked and linearly scaled on-orbit using observations at two AOIs of 11.2° and 50.2° corresponding to the moon view and solar diffuser, respectively. As the missions continue to operate well beyond their design life of six years, the assumption of linear scaling between the two AOIs is known to be inadequate in accurately characterizing the RVS, particularly at short wavelengths. Consequently, an enhanced approach of supplementing the on-board measurements with response trends from desert pseudoinvariant calibration sites (PICS) was formulated in MODIS Collection 6 (C6). An underlying assumption for the continued effectiveness of this approach is the long-term (multiyear) and short-term (month to month) stability of the PICS. Previous work has shown that the deep convective clouds (DCC) can also be used to monitor the on-orbit RVS performance with less trend uncertainties compared with desert sites. In this paper, the raw sensor response to the DCC is used to characterize the on-orbit RVS on a band and mirror-side basis. These DCC-based RVS results are compared with those of C6 PICS-based RVS, showing an agreement within 2% observed in most cases. The pros and cons of using a DCC-based RVS approach are also discussed in this paper. Although this reaffirms the efficacy of the C6 PICS-based RVS, the DCC-based RVS approach presents itself as an effective alternative for future considerations. Potential applications of this approach to other instruments, such as Suomi National Polar-orbiting Partnership, Joint Polar Satellite Systems, and Visible Infrared Imaging Radiometer Suite, are also discussed.

Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya

The study elucidate upon the evaluation of satellite retrievals with ground based aerosol optical depth (AOD) measurements, their utilization in LiDAR ratio (LR) estimation, boundary layer (BL) height determination and the case studies on aerosol transport over Himalayan region. The AOD retrievals from the latest level-2 data collections (C5.1 and C6.0) of MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites and Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) versions (4.10 and 3) are subjected for quantitative analysis to assess the level of agreement with the quality assured level-2 ground based AErosol RObotic NETwork (AERONET) measurements over Manora peak (29.36° N, 79.46°E), a high altitude site in the Himalayas. Analysis revealed that the AOD from the latest MODIS Terra C6.0 deep blue (DB) 30km×30km and CALIPSO ver. 4.10 (overpass within ~100km distance) are in a very good agreement (R≥0.9) with that from coincident AERONET measurements averaged over the span of ±30min. About 77% of the AOD retrieved using MODIS and ~87% from CALIPSO were found to be within the expected error (EE) limits. The AOD comparison between MODIS Terra C6.0DB and CALIPSO ver. 4.10, suggested their synergic use for aerosol characterization over Himalayas. In comparison to the ver. 3, CALIPSO ver. 4.10 is found to have undergone substantial changes, and their long term inter-comparison in the grid 28.86°-29.86° N and 78.96°–79.96° E revealed that their vertical feature and aerosol sub-types are in agreement of ~94.6% and ~68.6%, respectively. Utilizing the AOD retrievals from AERONET and MODIS collections, the iteratively computed LR for three LiDAR systems was found to be lower (<16) during winter and higher (>43) during summer. Study on the BL height estimations suggested that the wavelet covariance transform (WCT) method for CALIPSO could be the best choice as compared to the threshold method, and complements well with the specific humidity gradient method used with the radiosonde observation. Case studies on the continental transport of smoke plumes emanating from crop-residue burning in post-monsoon, and long range transport of aerosols and dust over the region in summer are also discussed using the collocated measurements from ground-based AERONET and LiDAR, in conjunction with MODIS, CALIPSO, reanalysis data and trajectory modeling.

Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau

Vegetation phenology is considered a sensitive indicator of climate change, which controls carbon, nitrogen, and water cycles within terrestrial ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) is an important moderate resolution remote sensing data for monitoring vegetation phenology. However, Terra MODIS Collection 5 (C5) vegetation index products were identified to be affected by sensor degradation, which has been addressed in the recently released MODIS Collection 6 (C6) vegetation index products. In order to compare the difference between MODIS C5 and C6 NDVI in monitoring vegetation phenology, the start and end of growing season (SOS and EOS) of the alpine grassland on the Tibetan Plateau (TP) were extracted using four common methods. Then, the C5 and C6 NDVI-derived SOS (SOSC5 and SOSC6) and EOS (EOSC5 and EOSC6) were compared with ground-observed phenology data. Results showed that the multi-year average growing season NDVIs of C6 were lower than those of C5 in most areas, while the inter-annual variation patterns of regional average SOSC5 and SOSC6 (EOSC5 and EOSC6) were consistent. However, large spatial differences in phenological trends were found between C5 and C6 NDVI products. From C5 to C6, pixels with a SOS (EOS) trend shifting from significant to insignificant or from insignificant to significant accounted for at least 14.58% (9.07%) of the total pixels. SOSC5 was more consistent than SOSC6 with the ground-observed green-up dates. C5 NDVI may be more appropriate for monitoring SOS than C6 NDVI in the study region, but more ground-observed phenology records are needed to confirm it due to only four observational sites in this study. However, large differences and poor correlations existed between EOSC5 (EOSC6) and the ground-observed beginning of leaf coloring. To further evaluate the uncertainty of MODIS C5 and C6 NDVI in monitoring vegetation phenology, higher resolution near-surface remote sensing data and corresponding validation methods should be applied.

An Evaluation of Four MODIS Collection 6 Aerosol Products in a Humid Subtropical Region

Moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) products have been widely used to characterize the temporal variations and spatial distributions of atmospheric aerosols. In the present study, we evaluate the performance of four Terra and Aqua MODIS Collection 6 (C6) quality assured AOD products in the Pearl River Delta (PRD) region, a humid subtropical region. The 10 km AOD products retrieved by the Dark Target (DT) and Deep Blue (DB) algorithms, the merged DT/DB (DTDB) 10 km product, and the DT 3 km AOD product were obtained for 2006–2015. These products were compared with Aerosol Robotic Network (AERONET) observations, and with each other. The Terra- and Aqua-derived AODs are quantitatively similar. However, there are significant differences among the four AOD products. The DT 10 km product correlates more closely with AERONET AOD observations than does the DB 10 km product. The latter tends to underestimate the AOD, whereas the former typically overestimates it for highly urbanized areas. The DTDB 10 km product is mainly derived from the DT 10 km product; it does not provide a gap-filled data set, because valid DB 10 km retrievals are not included in the merged product even when DT 10 km retrievals are unavailable. Therefore, the DT/DB merging protocol should be improved. The DT 3 km AOD product closely mimics the DT 10 km product; however, it contains fewer data than the DT 10 km product over water-contaminated areas. In addition, although the quality assured AOD products are recommended for use in quantitative applications by the MODIS aerosol science team, the sampling frequency of these products is generally lower than 25%. Thus, the sampling issues of these products should be considered in humid subtropical areas.

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

An assessment of thin cloud detection by applying bidirectional reflectance distribution function model‐based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea

AbstractIn this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model‐based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land surface area. Unlike cloud detection over the ocean, the detection of cloud over land surfaces is difficult due to the complicated surface scattering characteristics, which vary among land surface types. Furthermore, in the case of thin clouds, in which the surface and cloud radiation are mixed, it is difficult to detect the clouds in both land and atmospheric fields. Therefore, to interpret background surface reflectance, especially underneath cloud, the semiempirical BRDF model was used to simulate surface reflectance by reflecting solar angle‐dependent geostationary sensor geometry. For quantitative validation, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were used to make a comparison with the proposed cloud masking result. As a result, the new cloud masking scheme resulted in a high probability of detection (POD = 0.82) compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) (POD = 0.808) for all cloud cases. In particular, the agreement between the CALIPSO cloud product and new GOCI cloud mask was over 94% when detecting thin cloud (e.g., altostratus and cirrus) from January 2014 to June 2015. This result is relatively high in comparison with the result from the MODIS Collection 6 cloud mask product (MYD35).

Evaluation of satellite remote sensing albedo retrievals over the ablation area of the southwestern Greenland ice sheet

MODerate resolution Imaging Spectroradiometer (MODIS) albedo products have been validated over spatially uniform, snow-covered areas of the Greenland ice sheet (GrIS) using the so-called single ‘point-to-pixel’ method. This study expands on this methodology by applying a ‘multiple-point-to-pixel’ method and examination of spatial autocorrelation (here using semivariogram analysis) by using in situ observations, high-resolution WorldView-2 (WV-2) surface reflectances, and MODIS Collection V006 daily blue-sky albedo over a spatially heterogeneous surfaces in the lower ablation zone in southwest Greenland. Our results using 232 ground-based samples within two MODIS pixels, one being more spatial heterogeneous than the other, show little difference in accuracy among narrow and broad band albedos (except for Band 2). Within the more homogenous pixel area, in situ and MODIS albedos were very close (error varied from −4% to +7%) and within the range of ASD standard errors. The semivariogram analysis revealed that the minimum observational footprint needed for a spatially representative sample is 30m. In contrast, over the more spatially heterogeneous surface pixel, a minimum footprint size was not quantifiable due to spatial autocorrelation, and far exceeds the effective resolution of the MODIS retrievals. Over the high spatial heterogeneity surface pixel, MODIS is lower than ground measurements by 4–7%, partly due to a known in situ undersampling of darker surfaces that often are impassable by foot (e.g., meltwater features and shadowing effects over crevasses). Despite the sampling issue, our analysis errors are very close to the stated general accuracy of the MODIS product of 5%. Thus, our study suggests that the MODIS albedo product performs well in a very heterogeneous, low-albedo, area of the ice sheet ablation zone.Furthermore, we demonstrate that single ‘point-to-pixel’ methods alone are insufficient in characterizing and validating the variation of surface albedo displayed in the lower ablation area. This is true because the distribution of in situ data deviations from MODIS albedo show a substantial range, with the average values for the 10th and 90th percentiles being −0.30 and 0.43 across all bands. Thus, if only single point is taken for ground validation, and is randomly selected from either distribution tails, the error would appear to be considerable. Given the need for multiple in-situ points, concurrent albedo measurements derived from existing AWSs, (low-flying vehicles (airborne or unmanned) and high-resolution imagery (WV-2)) are needed to resolve high sub-pixel variability in the ablation zone, and thus, further improve our characterization of Greenland's surface albedo.

An Analysis of the Discrepancies between MODIS and INSAT-3D LSTs in High Temperatures

In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high spatial resolution can be used as references for validating geostationary LST products. This study attempted to investigate the discrepancies between a Moderate Resolution Imaging Spectro-radiometer (MODIS) and Indian National Satellite (INSAT-3D) LSTs for high temperatures, focusing on six deserts with sand dune land cover in the Middle East from 3 March 2015 to 24 August 2016. Firstly, the variability of LSTs in the deserts of the study area was analyzed by comparing the mean, Standard Deviation (STD), skewness, minimum, and maximum criteria for each observation time. The mean value of the LST observations indicated that the MYD-D observation times are closer to those of diurnal maximum and minimum LSTs. At all times, the LST observations exhibited a negative skewness and the STD indicated higher variability during times of MOD-D. The observed maximum LSTs from MODIS collection 6 showed higher values in comparison with the last versions of LSTs for hot spot regions around the world. After the temporal, spatial, and geometrical matching of LST products, the mean of the MODIS—INSAT LST differences was calculated for the study area. The results demonstrated that discrepancies increased with temperature up to +15.5 K. The slopes of the mean differences were relatively similar for all deserts except for An Nafud, suggesting an effect of View Zenith Angle (VZA). For modeling the discrepancies between two sensors in continuous space, the Diurnal Temperature Cycles (DTC) of both sensors were constructed and compared. The sample DTC models approved the results from discrete LST subtractions and proposed the uncertainties within MODIS DTCs. The authors proposed that the observed LST discrepancies in high temperatures could be the result of inherent differences in LST retrieval algorithms.

An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: Algorithm development

The fine-mode fraction (FMF) can be a useful tool to separate natural aerosols from man-made aerosols and to assist in estimating surface concentrations of particulate matter with a diameter<2.5μm. A LookUp Table-based Spectral Deconvolution Algorithm (LUT-SDA) was developed here for satellite-based applications using data such as MODerate resolution Imaging Spectroradiometer (MODIS) measurements. This method was validated against ground-based FMF retrievals from the Aerosol Robotic Network (AERONET). The LUT-SDA was then applied to two MODIS-retrieved aerosol optical thickness (AOT) products for the period of December 2013 to July 2015: the MODIS Collection 6 (C6) Dark Target (DT) AOT product and the simplified high-resolution MODIS Aerosol Retrieval Algorithm (SARA) AOT product. In comparison with the MODIS C6 FMF product in three study areas (Beijing, Hong Kong, and Osaka), FMFs estimated by the LUT-SDA agreed more closely with those retrieved from the AERONET with a very low bias. Eighty percent of the FMF values fell within the expected error range of ±0.4. The root mean square error (RMSE) was 0.168 with few anomalous values, whereas the RMSE for the MODIS FMF was 0.340 with more anomalous values. The LUT-SDA FMF estimated using SARA AOT data conveys more detailed information on urban pollution than that from MODIS C6 DT AOT data. As a demonstration, the seasonally-averaged spatial distribution of the FMF in Beijing was obtained from the LUT-SDA applied to SARA AOT data and compared with that of the AERONET-retrieved FMF. Their seasonal trends agreed well.