Aqua Moderate Resolution Imaging Spectroradiometer Data Research Articles

A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data

The ability to infer ocean chlorophyll-a concentrations (Chla) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level-3 remote sensing reflectance (Rrs) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chla thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chla in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing.

Improved cloud detection for the Aura Microwave Limb Sounder (MLS): training an artificial neural network on colocated MLS and Aqua MODIS data

Abstract. An improved cloud detection algorithm for the Aura Microwave Limb Sounder (MLS) is presented. This new algorithm is based on a feedforward artificial neural network and uses as input, for each MLS limb scan, a vector consisting of 1710 brightness temperatures provided by MLS observations from 15 different tangent altitudes and up to 13 spectral channels in each of 10 different MLS bands. The model has been trained on global cloud properties reported by Aqua's Moderate Resolution Imaging Spectroradiometer (MODIS). In total, the colocated MLS–MODIS data set consists of 162 117 combined scenes sampled on 208 d over 2005–2020. A comparison to the current MLS cloudiness flag used in “Level 2” processing reveals a huge improvement in classification performance. For previously unseen data, the algorithm successfully detects > 93 % of profiles affected by clouds, up from ∼ 16 % for the Level 2 flagging. At the same time, false positives reported for actually clear profiles are comparable to the Level 2 results. The classification performance is not dependent on geolocation but slightly decreases over low-cloud-cover regions. The new cloudiness flag is applied to determine average global cloud cover maps over 2015–2019, successfully reproducing the spatial patterns of mid-level to high clouds seen in MODIS data. It is also applied to four example cloud fields to illustrate its reliable performance for different cloud structures with varying degrees of complexity. Training a similar model on MODIS-retrieved cloud top pressure (pCT) yields reliable predictions with correlation coefficients > 0.82. It is shown that the model can correctly identify > 85 % of profiles with pCT < 400 hPa. Similar to the cloud classification model, global maps and example cloud fields are provided, which reveal good agreement with MODIS results. The combination of the cloudiness flag and predicted cloud top pressure provides the means to identify MLS profiles in the presence of high-reaching convection.

Northern Hemisphere contrail properties derived from Terra and Aqua MODIS data for 2006 and 2012

Abstract. Linear contrail coverage, optical property, and radiative forcing data over the Northern Hemisphere (NH) are derived from a year (2012) of Terra and Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) imagery and compared with previously published 2006 results (Duda et al., 2013; Bedka et al., 2013; Spangenberg et al., 2013) using a consistent retrieval methodology. Differences in the observed Terra-minus-Aqua screened contrail coverage and patterns in the 2012 annual-mean air traffic estimated with respect to satellite overpass time suggest that most contrails detected by the contrail detection algorithm (CDA) form approximately 2 h before overpass time. The 2012 screened NH contrail coverage (Mask B) shows a relative 3 % increase compared to 2006 data for Terra and increases by almost 7 % for Aqua, although the differences are not expected to be statistically significant. A new post-processing algorithm added to the contrail mask processing estimated that the total contrail cirrus coverage visible in the MODIS imagery may be 3 to 4 times larger than the linear contrail coverage detected by the CDA. This estimate is similar in magnitude to the spreading factor estimated by Minnis et al. (2013). Contrail property retrievals of the 2012 data indicate that both contrail optical depth and contrail effective diameter decreased approximately 10 % between 2006 and 2012. The decreases may be attributed to better background cloudiness characterization, changes in the waypoint screening, or changes in contrail temperature. The total mean contrail radiative forcings (TCRFs) for all 2012 Terra observations were −6.3, 14.3, and 8.0 mW m−2 for the shortwave (SWCRF), longwave (LWCRF), and net forcings, respectively. These values are approximately 20 % less than the corresponding 2006 Terra estimates. The decline in TCRF results from the decrease in normalized CRF, partially offset by the 3 % increase in overall contrail coverage in 2012. The TCRFs for 2012 Aqua are similar, −6.4, 15.5, and 9.0 mW m−2 for shortwave, longwave, and net radiative forcing. The strong correlation between the relative changes in both total SWCRF and LWCRF between 2006 and 2012 and the corresponding relative changes in screened contrail coverage over each air traffic region suggests that regional changes in TCRF from year to year are dominated by year-to-year changes in contrail coverage over each area.

Development of a new dust index NDLI for Asian dust extraction system based on Aqua MODIS data and monitoring of trans-boundary Asian dust events in Japan

ABSTRACTHeavy Asian dust events occur due to the strong wind in the Gobi deserts and are occasionally carried to Korea, Japan, and North America. They cause problems in human lives, such as respiratory diseases, transportation disturbances due to reduce visibility, and other disruptions in social activities. Remote sensing technology is useful for detecting and monitoring such airborne dust and understanding the distributions and movements of dust. To understand the Asian dust events, in this study, a new dust index is developed for the efficient detection of airborne Asian dust, which is a composite of two Moderate Resolution Imaging Spectroradiometer (MODIS) indices: Brightness Temperature Difference (BTD) and Normalized Difference Dust Index (NDDI). Our proposed Normalized Dust Layer Index (NDLI) detects dust more efficiently. To identify the characteristics of annual Asian dust events in Japan, a statistical time-series analysis of data from the years 2010, 2013 and 2014 is performed, and it is found that the dust events in 2014 were relatively calmer than those in 2013. An evaluation that was based on ground observations over different sites in Japan indicated that the proposed method performed well. Finally, we integrated our NDLI product into the trans-boundary air pollution satellite image database (TAPSIDB) system for monitoring Asian dust events.

Estimation of the Near-Surface Air Temperature during the Day and Nighttime from MODIS in Berlin, Germany

Air temperature (Tair or T2m) is an important climatological variable for forest biosphere processes and climate change research. Due to the low density and the uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Tair. In this study, Tair in Berlin is estimated during the day and night time over six land cover/land use (LC/LU) types by satellite remote sensing data over a large domain and a relatively long period (7 years). Aqua and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the period from 2007 to 2013 were collected to estimate Tair. Twelve environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emissivity31, Emissivity32, altitude, albedo, wind speed, wind direction and air pressure) were selected as predictors. Moreover, a comparison between LST from MODIS Terra and Aqua with daytime and night time air temperatures (Tday, Tnight) was done respectively and in addition, the spatial variability of LST and Tair relationship by applying a varying window size on the MODIS LST grid was examined. An analysis of the relationship between the observed Tair and the spatially averaged remotely sensed LST, indicated that 3 × 3 and 1 × 1 pixel size was the optimal window size for the statistical model estimating Tair from MODIS data during the day and night time, respectively. Three supervised learning methods (Adaptive Neuro Fuzzy Inference system (ANFIS), Artificial Neural Network (ANN) and Support vector machine (SVR)) were used to estimate Tair during the day and night time, and their performances were validated by cross-validation for each LC/LU. Moreover, tuning the hyper parameters of some models like SVR and ANN were investigated. For tuning the hyper parameters of SVR, Simulated Annealing (SA) was applied (SA-SVR model) and a multiple-layer feed-forward (MLF) neural networks with three layers and different nodes in hidden layers are used with Levenber-Marquardt back-propagation (LM-BP), in order to achieve higher accuracy in the estimation of Tair. Results indicated that the ANN model achieved better accuracy (RMSE= 2.16°C, MAE = 1.69°C, R2 = 0.95) than SA_SVR model (RMSE= 2.50°C, MAE = 1.92°C, R2 = 0.91) and ANFIS model (RMSE= 2.88°C, MAE= 2.2°C, R2 = 0.89) over six LC/LU during the day and night time. The Q-Q diagram of SA-SVR, ANFIS and NN show that all three models slightly tended to underestimate and overestimate the extreme and low temperatures for all LC/LU classes during the day and night time. The weak performance in the extreme and low temperatures are a consequence of the small number of data in these temperatures. These satisfactory results indicate that this approach is proper for estimating air temperature and spatial window size is an important factor that should be considered in the estimation of air temperature.

Development of a MODIS Data–Based Algorithm for Retrieving Gage Height in Nearshore Waters along the Louisiana Gulf Coast

ABSTRACT Wang, J. and Deng, Z., 2018. Development of a MODIS data–based algorithm for retrieving gage height in nearshore waters along the Louisiana Gulf Coast. Gage height is one of most important physical parameters commonly used for the description of daily sea levels and generally monitored at sparsely scattered tidal stations. This paper presents a novel remote sensing algorithm for the retrieval of spatially distributed gage height data in coastal waters with emphasis on nearshore waters using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The algorithm was trained using the Artificial Neural Networks toolbox in the MATLAB program and over 4 years (2007–11) of cloud-free MODIS Aqua data for water-leaving reflectance as well as ground truth measurements collected daily from U.S. Geological Survey stations along the Louisiana Gulf Coast. The algorithm was validated using 3 additional years of independent data sets, which were not used in the algorithm training and collected ...

Fundamental Research on Spring Season Daytime Sea Fog Detection Using MODIS in the Yellow Sea

For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel (STDIR) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

Simultaneously inferring above‐cloud absorbing aerosol optical thickness and underlying liquid phase cloud optical and microphysical properties using MODIS

AbstractThe regional haze over the southeast (SE) Atlantic Ocean induced by biomass burning in southern Africa can be problematic for passive imager‐based retrievals of the underlying quasi‐permanent marine boundary layer (MBL) clouds and for estimates of top‐of‐atmosphere (TOA) aerosol direct radiative effect (DRE). Here an algorithm is introduced to simultaneously retrieve above‐cloud aerosol optical thickness (AOT), the cloud optical thickness (COT), and cloud effective particle radius (CER) of the underlying MBL clouds while also providing pixel‐level estimates of retrieval uncertainty. This approach utilizes reflectance measurements at six Moderate Resolution Imaging Spectroradiometer (MODIS) channels from the visible to the shortwave infrared. Retrievals are run under two aerosol model assumptions on 8 years (2006–2013) of June–October Aqua MODIS data over the SE Atlantic, from which a regional cloud and above‐cloud aerosol climatology is produced. The cloud retrieval methodology is shown to yield COT and CER consistent with those from the MODIS operational cloud product (MOD06) when forcing AOT to zero, while the full COT‐CER‐AOT retrievals that account for the above‐cloud aerosol attenuation increase regional monthly mean COT and CER by up to 9% and 2%, respectively. Retrieved AOT is roughly 3 to 5 times larger than the collocated 532 nm Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals, though closer agreement is observed with the CALIOP 1064 nm retrievals, a result consistent with previous case study analyses. Regional cloudy‐sky above‐cloud aerosol DRE calculations are also performed that illustrate the importance of the aerosol model assumption and underlying cloud retrievals.

MODIS Collection 6 aerosol products: Comparison between Aqua's e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations

AbstractThe Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheres data product suite includes three algorithms applied to retrieve midvisible aerosol optical depth (AOD): the Enhanced Deep Blue (DB) and Dark Target (DT) algorithms over land, and a DT over‐water algorithm. All three have been refined in the recent “Collection 6” (C6) MODIS reprocessing. In particular, DB has been expanded to cover vegetated land surfaces as well as brighter desert/urban areas. Additionally, a new “merged” data set which draws from all three algorithms is included in the C6 products. This study is intended to act as a point of reference for new and experienced MODIS data users with which to understand the global and regional characteristics of the C6 DB, DT, and merged data sets, based on MODIS Aqua data. This includes validation against Aerosol Robotic Network (AERONET) observations at 111 sites, focused toward regional and categorical (surface/aerosol type) analysis. Neither algorithm consistently outperforms the other, although in many cases the retrieved AOD and the level of its agreement with AERONET are very similar. In many regions the DB, DT, and merged data sets are all suitable for quantitative applications, bearing in mind that they cannot be considered independent, while in other cases one algorithm does consistently outperform the other. Usage recommendations and caveats are thus somewhat complicated and regionally dependent.

Effects of crop residue burning on aerosol properties, plume characteristics, and long‐range transport over northern India

AbstractAerosol emissions from biomass burning are of specific interest over the globe due to their strong radiative impacts and climate implications. The present study examines the impact of paddy crop residue burning over northern India during the postmonsoon (October–November) season of 2012 on modification of aerosol properties, as well as the long‐range transport of smoke plumes, altitude characteristics, and affected areas via the synergy of ground‐based measurements and satellite observations. During this period, Moderate Resolution Imaging Spectroradiometer (MODIS) images show a thick smoke/hazy aerosol layer below 2–2.5 km in the atmosphere covering nearly the whole Indo‐Gangetic Plains (IGP). The air mass trajectories originating from the biomass‐burning source region over Punjab at 500 m reveal a potential aerosol transport pathway along the Ganges valley from west to east, resulting in a strong aerosol optical depth (AOD) gradient. Sometimes, depending upon the wind direction and meteorological conditions, the plumes also influence central India, the Arabian Sea, and the Bay of Bengal, thus contributing to Asian pollution outflow. The increased number of fire counts (Terra and Aqua MODIS data) is associated with severe aerosol‐laden atmospheres (AOD500 nm > 1.0) over six IGP locations, high values of Ångström exponent (>1.2), high particulate mass 2.5 (PM2.5) concentrations (>100–150 µgm−3), and enhanced Ozone Monitoring Instrument Aerosol Index gradient (~2.5) and NO2 concentrations (~6 × 1015 mol/cm2), indicating the dominance of smoke aerosols from agricultural crop residue burning. The aerosol size distribution is shifted toward the fine‐mode fraction, also exhibiting an increase in the radius of fine aerosols due to coagulation processes in a highly turbid environment. The spectral variation of the single‐scattering albedo reveals enhanced dominance of moderately absorbing aerosols, while the aerosol properties, modification, and mixing atmospheric processes differentiate along the IGP sites depending on the distance from the aerosol source, urban influence, and local characteristics.

Estimation of Daily Sunshine Duration from Terra and Aqua MODIS Data

Some studies have shown that the estimation of global sunshine duration can be done with the help of geostationary satellites because they can record several images of the same location in a day. In this paper, images obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors of polar orbiting satellites Aqua and Terra were used to estimate daily global sunshine duration for any region in Turkey. A new quadratic correlation between daily mean cloud cover index and relative sunshine duration was also introduced and compared with the linear correlation. Results have shown that polar orbiting satellites can be used for the estimation of sunshine duration. The quadratic model introduced here works better than the linear model especially for the winter months in which very low sunshine duration values were recorded at the ground stations for many days.

Exploration of sensor comparability: a case study of composite MODIS Aqua and Terra data

Comparability across sensors is a common goal of remotely sensed data products; however, sensor calibration and atmospheric conditions often limit implementation. Recently, a slight degradation in the blue band sensor on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite was reported (Wang et al. 2012), and it was suggested at the 2011 fall NASA Carbon Cycle and Ecosystems/Biodiversity and Ecological Forecasting Team meeting that users switch to using MODIS Aqua data until the corrected collection 6 Terra data are released. Following this recommendation, a switch from Terra to Aqua data inputs used in the Tsetse Ecological Distribution (TED) model was implemented; however, significant variability between model outputs for Kenya was found. This letter considers the possible sources of incongruity between the MODIS Aqua and Terra sensors that might result in the observed differences between model outputs, including sensor degradation, temporal overlap, daily acquisition and data quality. While for data of Kenya it appears that differences in daily acquisition times and data quality result in differences between composite products, ultimately the selection of what data product and sensor to use should be based on reported data quality and biophysical conditions of the region under study.

Comparing Ship-Track Droplet Sizes Inferred from Terra and Aqua MODIS Data

AbstractIn this study of ship tracks, Moderate Resolution Imaging Spectroradiometer (MODIS) measurements from late-morning (Terra) and early-afternoon (Aqua) Earth Observing System platforms are analyzed in five separate geographically distributed cases to compare estimates of the sizes (and their changes in time) of droplets associated with ship exhaust. Ship tracks are readily detected in near-infrared imagery as bright features, especially in 2.13-μm observations. The Terra “MOD06” and Aqua “MYD06” cloud products are used to determine the effective radius of the ship-track droplets; droplet age (time in the atmosphere) is estimated as a function of the distance from the ship. Terra and Aqua MODIS estimates of droplet sizes in ship-track plumes are found to be in agreement, with a correlation greater than 0.90; for the cases studied, droplet sizes in the ship plumes are between 6 and 18 μm. Moreover, the droplets’ size growth rates inferred from the length of the ship track were found to average between 0.5 and 1.0 μm h−1.

Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

Abstract. A novel approach for the joint retrieval of aerosol optical depth (AOD) and aerosol type, using Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a Time Series (TS) technique, which makes use of the two visible bands at 0.6 μm and 0.8 μm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmosphere, two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the AOD. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor. We assume one aerosol type for a grid of 1°×1° and the surface reflectance changes little between two subsequent observations. A k-ratio approach is used in the inversion to find the best solution of atmospheric properties and surface reflectance. The k-ratio approach assumes that the surface reflectance is little influenced by aerosol scattering at 1.6 μm and therefore the ratio of surface reflectances in the solar band for two subsequent observations can be well-approximated by the ratio of the reflectances at 1.6 μm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets. A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that >75% of the MSG-retrieved AOD at 0.6 and 0.8 μm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15 τ and a high correlation coefficient (R>0.86). The AOD datasets derived using the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially also useful for surface reflectance retrieval using SEVIRI observations. The current paper focuses on AOD retrieval and analysis, and the analysis and validation of reflectance will be given in a following paper.

Retrieval of aerosol optical depth over bright land surfaces by coupling bidirectional reflectance distribution function model and aerosol retrieval model

A novel aerosol optical depth (AOD) retrieval algorithm is developed by integrating a kernel-driven bidirectional reflectance distribution function (BRDF) model and the multi-satellite AOD retrieval model for Moderate Resolution Imaging Spectroradiometer (MODIS) data. As there is close coupling of AOD and surface reflectance in satellite signal, one will be traditionally assuming a prior in order to solve another one. However, we build a group of equations to solve both AOD and surface reflectance at the same time in our algorithm. Applying this new algorithm to Terra and Aqua MODIS data in Beijing, China, allows AOD and surface reflectance of this region to be retrieved. Results indicate that the MODIS AOD derived from the new method is consistent with AErosol RObotic NETwork (AERONET), with correlation coefficient (R 2) of 0.812 and root mean square error (RMSE) of 0.04 at 0.55 μm.

Validation and analysis of aerosol optical thickness retrieval over land

Aerosol optical thickness (AOT) retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS) data has been well established over oceans, but this is not the case over land. In this article, the AOT data sets retrieved by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM) over land are validated with ground-based measurements from Aerosol Robotic Network (AERONET) data, as well as from the National Aeronautics and Space Administration (NASA) AOT products, amended with a DeepBlue algorithm in Asian (15–60° N and 35–150° E) and American areas (30–40° N and 100–120° W). Overall, AOT retrieval errors of around 10–20% against AERONET data are found at both 1 and 10 km resolutions. The spectral and spatial sensitivities of the AOT correlation are explicitly addressed at both 1 and 10 km resolutions. Three window sizes, 1 × 1, 3 × 3 and 5 × 5, are tested for SYNTAM to evaluate the effect of window size on parameter statistics, and it is found that the accuracy of the SYNTAM method decreases with increasing window size. The validations at three spectral bands of 0.47, 0.55 and 0.66 μm show that the accuracies of different bands are 80–90% similar, and that the band at 0.47 μm has the highest accuracy most of the time. Comparisons between AOT data sets derived from the SYNTAM and AOT products from the NASA Dark Dense Vegetation (DDV) and the DeepBlue algorithms are also conducted using data from the USA. More pixels with AOT values for the area could be retrieved using the SYNTAM method with the NASA DeepBlue algorithm. The AOT values of more than 90% of pixels derived by both methods are very close. This clearly shows that AOT data from SYNTAM are very close to the AOT data set from the NASA DeepBlue algorithm in cloud-free areas. The synergic use of both the SYNTAM and DeepBlue algorithms could produce AOT values over much greater land areas.

CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part II: Examples of Average Results and Comparisons With Other Data

Cloud properties were retrieved by applying the Clouds and Earth's Radiant Energy System (CERES) project Edition-2 algorithms to 3.5 years of Tropical Rainfall Measuring Mission Visible and Infrared Scanner data and 5.5 and 8 years of MODerate Resolution Imaging Spectroradiometer (MODIS) data from Aqua and Terra, respectively. The cloud products are consistent quantitatively from all three imagers; the greatest discrepancies occur over ice-covered surfaces. The retrieved cloud cover (~59%) is divided equally between liquid and ice clouds. Global mean cloud effective heights, optical depth, effective particle sizes, and water paths are 2.5 km, 9.9, 12.9 μm , and 80 g·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , respectively, for liquid clouds and 8.3 km, 12.7, 52.2 μm, and 230 g·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for ice clouds. Cloud droplet effective radius is greater over ocean than land and has a pronounced seasonal cycle over southern oceans. Comparisons with independent measurements from surface sites, the Ice Cloud and Land Elevation Satellite, and the Aqua Advanced Microwave Scanning Radiometer-Earth Observing System are used to evaluate the results. The mean CERES and MODIS Atmosphere Science Team cloud properties have many similarities but exhibit large discrepancies in certain parameters due to differences in the algorithms and the number of unretrieved cloud pixels. Problem areas in the CERES algorithms are identified and discussed.

Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model

Images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua exhibit strong detector striping. This artifact is common to most pushbroom scanners and affects both visual interpretation and radiometric integrity of remotely sensed data. A considerable effort has been made to remove stripe noise and reduce its impact on high-level products. Despite the variety of destriping algorithms proposed in the literature, complete removal of stripes without signal distortion is yet to be overcome. In this paper, we tackle the striping issue from a variational angle. Basic statistical assumptions used in previous techniques are replaced by a much realistic geometrical consideration on the striping unidirectional variations. The resulting algorithm is tested on Aqua and Terra MODIS data contaminated with severe stripes and is shown to provide optimal qualitative and quantitative results.

An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data

Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼40km resolution passive microwave-derived soil moisture, a methodology based on 1km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼40km resolution microwave pixels and verify disaggregation results at 4km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012vol./vol.

Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data

Over a 1-year period, field and satellite measurements of surface water turbidity were combined in order to study the dynamics of the turbidity maximum zone (TM) in a macrotidal estuary (the Gironde, France). Four fixed platforms equipped with turbidity sensors calibrated to give the suspended particulate matter (SPM) concentration provided continuous information in the upper estuary. Full resolution data recorded by the moderate resolution imaging spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellite platforms provided information in the central and lower estuary twice a day (depending on cloud cover). Field data were used to validate a recently developed SPM quantification algorithm applied to the MODIS ‘surface reflectance’ product. The algorithm is based on a relationship between the SPM concentration and a reflectance ratio of MODIS bands 2 (near-infrared) and 1 (red). Based on 62 and 75 match-ups identified in 2005 with MODIS Terra and Aqua data, the relative uncertainty of the algorithm applied to these sensors was found to be 22 and 18%, respectively. Field measurements showed the tidal variations of turbidity in the upper estuary, while monthly-averaged MODIS satellite data complemented by field data allowed observing the monthly movements of the TM in the whole estuary. The trapping of fine sediments occurred in the upper estuary during the period of low river flow. This resulted in the formation of a highly concentrated TM during a 4-month period. With increasing river flow, the TM moved rapidly to the central estuary. A part of the TM detached, moved progressively in the lower estuary and was finally either massively exported to the ocean during peak floods or temporary trapped (settled) on intertidal mudflats. The massive export to the ocean was apparently the result of combined favorable environmental conditions: presence of fluid mud near the mouth, high river flow, high tides and limited wind speeds. The mean SPM concentration within surface waters of the whole estuary showed strong seasonal variations but remained almost unchanged on a 1-year-basis. These observations suggest that the masses of suspended sediments exported toward the ocean and supplied by the rivers were almost equivalent during the year investigated (2005). Results show the usefulness of information extracted from combined field and current ocean color satellite data in order to monitor the transport of suspended particles in coastal and estuarine waters.