1,166 research outputs found

    Linear and nonlinear aspects of the tropical 30-60 day oscillation: A modeling study

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    The scientific problem focused on study of the tropical 30-60 day oscillation and explanation for this phenomenon is discussed. The following subject areas are covered: the scientific problem (the importance of low frequency oscillations; suggested mechanisms for developing the tropical 30-60 day oscillation); proposed research and its objective; basic approach to research; and results (satellite data analysis and retrieval development; thermodynamic model of the oscillation; the 5-level GCM)

    Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

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    Atmospherically corrected surface reflectance (SR) products are used for reliable monitoring of land surfaces and are the standard products of Landsat sensors. Due to increased demand for SR products, a need exists to verify that the L2C2 (Level-2 Collection-2) SR products are precise and accurate. The Level-2 Collection 2 (L2C2) SR Product is processed satellite imagery data that corrects for atmospheric effects such as absorption and scattering, providing a more accurate representation of Earth\u27s surface. The validation of SR products using ground truth measurement is essential. This study aims to develop and evaluate a validation methodology for satellite SR products. Thus, the Empirical Line Method (ELM) is used here for atmospheric validation of remotely sensed data. Validation is performed using the SR derived from ELM tied to ground truth measurement. Absolute surface reflectance models of Algodones Dunes and the Salton Sea located in North America Sonoran Desert are developed to extend the temporally limited ground truth measurements. This model can give ground truth reflectance in any time frame independent of time constraints. The result of the absolute surface reflectance model of Algodones Dunes indicates that the model predicts the response of Algodones Dunes with an average accuracy of 0.0041 and precision of 0.0063 and gives ground measurements across all multispectral between 350-2500nm. For the Salton Sea the model predicts the response of the Salton Sea with mean absolute error (MAE) of 0.0035 and gives ground measurements across all multispectral between 350-2500nm. The ELM generates atmospheric coefficients (gain and bias) which are applied to an image to obtain SR. Validation results indicated for L9-OLI-2, L8-OLI, and L5-TM-SR products give the RMSE range of 0.0019 to 0.0106, 0.0019 to 0.0148 and 0.0026 to 0.0045 reflectance unit, respectively, and accuracy within 0.0038, 0.0022, and 0.0055 reflectance unit across all spectral bands of L9, L8, and L5 respectively. On average, the validation result showed a strong linear relation between the L2C2 SR products and ELM SR within 0.5 to 1 reflectance units. These results demonstrate the high accuracy and reliability of the L2C2 SR product, providing valuable information for a wide range of remote sensing applications, including land cover and land use mapping, vegetation monitoring, and climate change studies

    Application of Convolutional Neural Network in the Segmentation and Classification of High-Resolution Remote Sensing Images

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    Numerous convolution neural networks increase accuracy of classification for remote sensing scene images at the expense of the models space and time sophistication This causes the model to run slowly and prevents the realization of a trade-off among model accuracy and running time The loss of deep characteristics as the network gets deeper makes it impossible to retrieve the key aspects with a sample double branching structure which is bad for classifying remote sensing scene photo

    A study of remote sensing as applied to regional and small watersheds. Volume 1: Summary report

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    The accuracy of remotely sensed measurements to provide inputs to hydrologic models of watersheds is studied. A series of sensitivity analyses on continuous simulation models of three watersheds determined: (1)Optimal values and permissible tolerances of inputs to achieve accurate simulation of streamflow from the watersheds; (2) Which model inputs can be quantified from remote sensing, directly, indirectly or by inference; and (3) How accurate remotely sensed measurements (from spacecraft or aircraft) must be to provide a basis for quantifying model inputs within permissible tolerances

    Use of satellite-derived heterogeneous surface soil moisture for numerical weather prediction, The

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    Summer 1996.Bibliography: pages [296]-320

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    Strategies for using remotely sensed data in hydrologic models

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    Present and planned remote sensing capabilities were evaluated. The usefulness of six remote sensing capabilities (soil moisture, land cover, impervious area, areal extent of snow cover, areal extent of frozen ground, and water equivalent of the snow cover) with seven hydrologic models (API, CREAMS, NWSRFS, STORM, STANFORD, SSARR, and NWSRFS Snowmelt) were reviewed. The results indicate remote sensing information has only limited value for use with the hydrologic models in their present form. With minor modifications to the models the usefulness would be enhanced. Specific recommendations are made for incorporating snow covered area measurements in the NWSRFS Snowmelt model. Recommendations are also made for incorporating soil moisture measurements in NWSRFS. Suggestions are made for incorporating snow covered area, soil moisture, and others in STORM and SSARR. General characteristics of a hydrologic model needed to make maximum use of remotely sensed data are discussed. Suggested goals for improvements in remote sensing for use in models are also established

    Forest structure and aboveground biomass in the southwestern United States from MODIS and MISR

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    Red band bidirectional reflectance factor data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) acquired over the southwestern United States were interpreted through a simple geometric–optical (GO) canopy reflectance model to provide maps of fractional crown cover (dimensionless), mean canopy height (m), and aboveground woody biomass (Mg ha−1) on a 250 m grid. Model adjustment was performed after dynamic injection of a background contribution predicted via the kernel weights of a bidirectional reflectance distribution function (BRDF) model. Accuracy was assessed with respect to similar maps obtained with data from the NASA Multiangle Imaging Spectroradiometer (MISR) and to contemporaneous US Forest Service (USFS) maps based partly on Forest Inventory and Analysis (FIA) data. MODIS and MISR retrievals of forest fractional cover and mean height both showed compatibility with the USFS maps, with MODIS mean absolute errors (MAE) of 0.09 and 8.4 m respectively, compared with MISR MAE of 0.10 and 2.2 m, respectively. The respective MAE for aboveground woody biomass was ~10 Mg ha−1, the same as that from MISR, although the MODIS retrievals showed a much weaker correlation, noting that these statistics do not represent evaluation with respect to ground survey data. Good height retrieval accuracies with respect to averages from high resolution discrete return lidar data and matches between mean crown aspect ratio and mean crown radius maps and known vegetation type distributions both support the contention that the GO model results are not spurious when adjusted against MISR bidirectional reflectance factor data. These results highlight an alternative to empirical methods for the exploitation of moderate resolution remote sensing data in the mapping of woody plant canopies and assessment of woody biomass loss and recovery from disturbance in the southwestern United States and in parts of the world where similar environmental conditions prevail

    Utility of remote sensing data in retrieval of water quality consituents concentrations in coastal water of New Jersey

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    Three important optical properties used for monitoring coastal water quality are the concentrations of chlorophyll (CHL), color dissolved organic matter (CDOM) and total suspended materials (TSM). Ocean color remote sensing, a technique to collect color data by detection of upward radiance from a distance (Bukata et al.,1995), provides a synoptic view for determining these concentrations from upwelling radiances. In the open ocean (Case-I), it is not difficult to derive empirical algorithms relating the received radiances to surface concentrations of water quality parameters. In coastal waters (Case-Il), there are serious unresolved problems in extracting chlorophyll concentration because of high concentration of suspended particles (Gordon and Morel, 1983). There are three basic approaches to estimate optical water quality parameters from remotely sensed spectral data based on the definitions given by Morel & Gordon (1980): (1) an empirical method, in which statistical relationships between the upward radiance at the sea surface and the quantity of interest are taken into account; (2) a semiempirical method, in which the spectral characteristics of the parameters of interest are known and some modeling of the physics is introduced; and (3) an analytical method, in which radiative transfer models are used to extract the inherent optical properties (lOPs) and a suite of analysis methods can be used to optimally retrieve the water constituents from the remotely sensed upwelling radiance or irradiance reflectance signal. The focus of this research is the modification and application of analytical and statistical algorithms to characterize the physically based surface spectral reflectance for the waters of the Hudson/Raritan Estuary and to retrieve the water constituent concentrations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and LIght Detection And Ranging (LIDAR) signals. The approaches used here are based on the unique capabilities of AVIRIS and LIDAR data which can potentially provide a better understanding of how sunlight interacts with estuarine/inland water, especially when complemented with in situ measurements for analysis of water quality parameters and eutrophication processes. The results of analysis in forms of thematic maps are then input into geographic information system (GIS) of the study site for use by water resource managers and planners for better monitoring and management of water quality condition

    Application of machine learning to prediction of vegetation health

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    This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern
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