Browsing by Subject "MODIS"
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Item Characteristics of Tropical Midlevel Clouds Using A-Train Measurements(2013-07-22) Sutphin, Alisha BrookeMidlevel clouds are observed globally and impact the general circulation through their influence on the radiation budget and their precipitation production. However, because midlevel clouds occur less frequently than high and low clouds they are relatively understudied. Satellite observations from the MODIS, CALIPSO, and CloudSat instruments onboard the A-Train are combined to study midlevel cloud characteristics in the Tropical Western Pacific (TWP) between January 2007 and December 2010. Characteristic cloud and microphysical properties including cloud top height (CTH), geometric thickness, optical depth, effective radius, and liquid or ice water path (LWP or IWP), and environmental properties, including temperature and specific humidity profiles, are determined for precipitating and non-precipitating midlevel clouds. In the study region, approximately 14% of all cloudy scenes are classified as midlevel clouds (4 km < CTH < 8 km). These are concentrated in areas of deeper convection associated with the Pacific warm pool, ITCZ, and SPCZ. Non-precipitating clouds dominate the region, accounting for approximately 70% of all single and two-layer midlevel clouds scenes. Midlevel clouds occur most frequently in three different scenarios: high over midlevel clouds (~65%), single-layer (~25%), and midlevel over mid- or low-level clouds (~10%). Environmental moisture appears to play a larger role than temperature in determining midlevel cloud distributions due to large variations in moisture between the different cloud scenarios. In all scenes, a trimodal distribution in CTH frequency is found within the midlevel. Two of these peaks have been identified in previous studies; however a third midlevel mode is recognized here. CTHs occur most frequently in peaks between 5-6 km, 6-6.25 km, and 6.5-7.5 km. Although the past studies have only noted two midlevel peaks, this third mode is a robust feature in this dataset. Two types of clouds dominate these peaks: non-precipitating altostratus or altocumulus-like clouds less than 1 km thick and geometrically thick precipitating cumulus congestus clouds. Environmental temperature stable layers and dry maxima are found at each one of these peak frequency heights. Again, moisture seems to play a more dominant role in determining the height of the midlevel clouds due to larger variances between the moisture gradients associated with each peak. Microphysical properties (optical depth, effective radius, and LWP or IWP) are characterized for single-layer clouds. Approximately 30% of all single-layer midlevel clouds are precipitating and these clouds tend to occur on the edges of the deep tropics. In general, precipitating clouds have greater optical depths, effective radii, and water path. This research implies that some past studies at single point locations can be representative of the broader tropics, whereas others are not.Item Fractional Snow-Cover Mapping Through Artificial Neural Network Analysis of MODIS Surface Reflectance.(2010-07-14) Dobreva, Iliyana D.Accurate areal measurements of snow-cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is approximated to either snow-covered or snow-free. Fractional snow cover (FSC) mapping achieves a more precise estimate of areal snow-cover extent by determining the fraction of a pixel that is snow-covered. The two most common FSC methods using Moderate Resolution Imaging Spectroradiometer (MODIS) images are linear spectral unmixing and the empirical Normalized Difference Snow Index (NDSI) method. Machine learning is an alternative to these approaches for estimating FSC, as Artificial Neural Networks (ANNs) have been used for estimating the subpixel abundances of other surfaces. The advantages of ANNs over the other approaches are that they can easily incorporate auxiliary information such as land-cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow-cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed an ANN approach to snow-fraction mapping. A feed-forward ANN was trained with backpropagation to estimate FSC from MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with high spatial-resolution FSC derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow-cover maps. ANN achieved best result in terms of extent of snow-covered area over evergreen forests, where the extent of snow cover was slightly overestimated. Scatter plot graphs of the ANN and reference FSC showed that the neural network tended to underestimate snow fraction in high FSC and overestimate it in low FSC. The developed ANN compared favorably to the standard MODIS FSC product with the two methods estimating the same amount of total snow-covered area in the test scenes.Item Quantification of Impurities in Prairie Snowpacks and Evaluation and Assessment of Measuring Snow Parameters from MODIS Images(2012-10-19) Morris, Jennifer NicoleExtensive research on soot in snow and snow grain size has been carried out in the Polar Regions. However, North American prairie snowpacks lack observations of soot in snow on snow albedo which adds uncertainty to the overall global effect that black carbon on snow has on climate. Measurements in freshly fallen prairie snowpacks in Northwestern Iowa and Central Texas were collected from February 25 to March 3, 2007 and April 6, 2007, respectively. Multi-day monitoring locations and a frozen lake were study sites at which snow samples were collected to measure soot in snow concentrations. Ancillary measurements were collected at a subset of the sample sites that included: temperature, density, depth, and grain size. At some locations snow reflectance and snow radiance was collected with an Analytical Spectral Device visible/near infra-red spectroradiometer (350 ? 1500 nm). Snow impurity, consisting of light-absorbing particulate matter, was measured by filtering meltwater through a nucleopore 0.4 micrometer filter. Filters were examined using a photometer to measure mass impurity concentration. Soot observations indicate prairie snowpack concentrations ranging from 1 ng C gm^-1 to 115 ng C gm^-1 with an average of 34.9 ng C gm^-1. These measurements are within range of previously published values and can lower snow albedo. As expected, spectral albedo was found to decrease with increasing impurities. Additionally, as grain size increased impurity concentration increased. Differences in soot concentration were observed between the two Iowa snowfall events. The Texas event had higher soot concentrations than both Iowa snowfalls. Validation of an ADEOS-II snow product algorithm that compares simulated radiances to measured sensor radiances for retrieval of snow grain size and mass fraction of soot in snow was attempted using satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was unable to uniquely identify a particular snow grain size and soot concentration that would lead to a converging radiance solution in the two spectral bands measured and compared by the algorithm. The in situ data at the validation site fell within published ranges for freshly fallen snow for both snow grain size and soot concentration; however; the closest algorithm retrievals were considerably higher than in situ measurements for both grain size and impurity concentrations.Item Suspended Sediment Dynamics of Texas EstuariesReisinger, Anthony ShermanItem Use of airs and modis thermal infrared channels to retrieve ice cloud properties(Texas A&M University, 2007-04-25) Yost, Christopher RogersIn this study, we use thermal infrared channels to retrieve the optical thickness and effective particle radius of ice clouds. A physical model is used in conjunction with Atmospheric Infrared Sounder (AIRS) temperature and water vapor profiles to simulate the top-of-atmosphere (TOA) brightness temperatures (BTs) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) for channels located at 8.5, 11.0, and 12.0 ????m (1176, 909, and 833 cm-1). The model is initially validated by comparing simulated clear-sky BTs to MODIS-observed clear-sky BTs. We also investigate the effect of introducing a +3 K bias in the temperature profile, a +3 K bias in the surface temperature, and a +20% bias in the water vapor profile in order to test the sensitivity of the model to these inputs. For clear-sky cases, the simulated TOA BTs agree with MODIS to within 2-3 K. The model is then extended to simulate thermal infrared BTs for cloudy skies, and we infer the optical thickness and effective radius of ice clouds by matching MODIS-observed BTs to calculations. The optical thickness retrieval is reasonably consistent with the MODIS Collection 5 operational retrieval for optically thin clouds but tends to retrieve smaller particle sizes than MODIS.Item Using LiDAR and normalized difference vegetation index to remotely determine LAI and percent canopy cover at varying scales(2009-05-15) Griffin, Alicia Marie RutledgeThe use of airborne LiDAR (Light Detection and Ranging) as a direct method to evaluate forest canopy parameters is vital in addressing both forest management and ecological concerns. The overall goal of this study was to develop the use of airborne LiDAR in evaluating canopy parameters such as percent canopy cover (PCC) and leaf area index (LAI) for mixed pine and hardwood forests (primarily loblolly pine, Pinus taeda, forests) of the southeastern United States. More specific objectives were to: (1) Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI over both hardwood and coniferous forests; (2) investigate whether a LiDAR and normalized difference vegetation index (NDVI) data fusion through linear regression improve estimates of these forest canopy characteristics; (3) generate maps of PCC and LAI for the study region, and (4) compare local scale LiDAR-derived PCC and regional scale MODIS-based PCC and investigate the relationship. Scanning LiDAR data was used to derive local scale PCC estimates, and TreeVaW, a LiDAR software application, was used to locate individual trees to derive an estimate of plot-level PCC. A canopy height model (CHM) was created from the LiDAR dataset and used to determine tree heights per plot. QuickBird multispectral imagery was used to calculate the NDVI for the study area. LiDAR- and NDVI-derived estimates of plot-level PCC and LAI were compared to field observations for 53 plots over 47 square kilometers. Linear regression analysis resulted in models explaining 84% and 78% of the variability associated with PCC and LAI, respectively. For these models to be of use in future studies, LiDAR point density must be 2.5 m. The relationship between regional scale PCC and local scale PCC was investigated by resizing the local scale LiDAR-derived PCC map to lower resolution levels, then determining a regression model relating MODIS data to the local values of PCC. The results from this comparison showed that MODIS PCC data is not very accurate at local scales. The methods discussed in this paper show great potential for improving the speed and accuracy of ecological studies and forest management.Item Validation of Current Moderate Resolution Imaging Spectroradiometer (MODIS) Daily Snow Albedo Product and Spatial Analysis Based on Multiple Sensors(2012-07-16) Zhao, PanshuSnow albedo is one of the most important factors for atmosphere-surface energy exchange in high latitude areas. Remote sensing provides continual observations of snow albedo. However, the reliability of snow albedos obtained from remotely sensed images can be problematic, especially when acquired over heterogeneous land surfaces. This research examines spatial variations in snow albedo observed under different conditions in order to assess how accurate an individual in situ observation of snow albedo is when compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow albedo product (MOD10A1) and its relationship with land surface types. In addition to the field observations, albedos retrieved from two SURFRAD stations are also examined. The overall Root Mean Square Error (RMSE) between the in situ and MODIS albedos is 8%. Semivariogram analysis of Landsat ETM+ snow albedo retrievals on January 26th, 2010 over an ice and snow covered lake indicates spatial autocorrelation lengths of approximately 260 m, suggesting limited in situ observation can be considered fairly representative of albedos retrieved from MODIS images. To further reveal what parameters could influence the spatial representativeness, this research examined landscape metrics based on seven binary snow maps created from Landsat images for three areas of differing roughness and for different snow cover conditions. There are two Landscape metrics, Mean Shape Index (MSI) and Area Weighted Shape Index (AWMSI), were found to be correlated the spatial autocorrelation lengths of snow albedo as measured from the range distance of the modeled semivariograms. In addition, this research also introduced a method of using multi-angle mast to measure the surface bidirectional reflectance distribution function (BRDF). This method could be used for further research to build the BRDF library of the snow-covered canopies.Item Vertical Distribution of Cloud Liquid Water and Ice: A Comparison of MODIS Satellite Observations and the GISS Global Climate Model(2015-02-09) Pitts, Katherine LClouds continue to be a large source of uncertainty within global climate models. While satellites provide the only global datasets for comparison with these models, satellite retrievals provide inferences of cloud properties, rather than direct measurements. Therefore, comparisons between climate model simulations and satellite retrievals require careful construction of globally-gridded and time-averaged (Level 3) satellite datasets. For some types of comparisons, existing Level 3 datasets may not be sufficient, necessitating the generation of gridded datasets directly from Level 2 products. The current study uses a filtering and gridding algorithm to create a customized globally-gridded (i.e., Level 3) dataset based on Aqua MODIS Level 2 cloud top pressure and cloud optical property retrievals. With the recent release of MODIS Collection 6, we utilize this algorithm to examine the differences between cloud parameters in the MODIS Collection 5 and Collection 6 datasets, and then compare these satellite measurements to the GISS-E2-H model-simulated cloud parameters that were provided for the Coupled Model Intercomparison Project - Phase 5 (CMIP5). This comparison study focuses on the vertical distribution of cloud liquid water and ice, especially in the mid-troposphere where mixed-phase clouds are most likely to occur. Results show that the cloud retrieval algorithm improvements with MODIS Collection 6 lead to an overall decrease in uncertainty in cloud water path retrievals, as well as a change in the vertical distribution of clouds (high clouds higher, low clouds lower) and the resulting vertical distribution of cloud water path (increased mid-level cloud water path). When MODIS Collection 6 data are compared with GISS-E2-H climate model simulations, it is clear that the model greatly overestimates ice water path within a double ITCZ (intertropical convergence zone) in the high cloud height regime, but underestimates ice water path in higher latitudes. The model also overestimates low level liquid water path over land, especially over mountainous regions. The filtering and gridding algorithm used in this study is a convenient tool for building custom gridded datasets to address research questions that the official Level 3 datasets were not designed for.