Showing papers in "Remote Sensing of Environment in 2012"
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TL;DR: An overview of the GMES Sentinel-2 mission including a technical system concept overview, image quality, Level 1 data processing and operational applications is provided.
2,517 citations
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TL;DR: The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images and as high as 96.4%.
1,620 citations
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TL;DR: The unique data availability performance of the Sentinel-1 routine operations makes the mission particularly suitable for emergency response support, marine surveillance, ice monitoring and interferometric applications such as detection of subsidence and landslides.
1,260 citations
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TL;DR: The European Space Agency's Sentinel-5 Precursor (S-5 P) is a low Earth orbit polar satellite to provide information and services on air quality, climate and the ozone layer in the timeframe 2015-2022 as discussed by the authors.
1,092 citations
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TL;DR: The Operational Sea Surface Temperature (SST) and Sea Ice Analysis (OSTIA) as discussed by the authors system uses satellite SST data provided by international agencies via the Group for High Resolution SST (GHRSST) Regional/Global Task Sharing (R/GTS) framework.
977 citations
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TL;DR: The new data policy is revolutionizing the use of Landsat data, spurring the creation of robust standard products and new science and applications approaches, and promoting increased international collaboration to meet the Earth observing needs of the 21st century.
976 citations
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TL;DR: The models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10–100 m), and the advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms.
905 citations
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TL;DR: Coupling free and open access to large data volumes with improved processing power will result in automated image pre-processing and land cover characterization methods that need to leverage high-performance computing capabilities in advancing the land cover monitoring discipline.
824 citations
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TL;DR: In this paper, pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM).
785 citations
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TL;DR: The Landsat Data Continuity Mission (LDCM) as mentioned in this paper is a successor to the Landsat data continuity mission (LDSM) that will collect, archive, and distribute the image data as part of the Earth Resources Observation and Science (EROS) archive.
738 citations
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TL;DR: In this article, the remote detection of water stress in a citrus orchard was investigated using leaf-level measurements of chlorophyll fluorescence and Photochemical Reflectance Index (PRI) data, seasonal time-series of crown tem- perature and PRI, and high-resolution airborne imagery.
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TL;DR: In this article, the authors presented an approach for combining four passive microwave products from the VU University Amsterdam/National Aeronautics and Space Administration and two active microwave items from the Vienna University of Technology.
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TL;DR: In this article, the authors evaluated the accuracy of the GIMMS3g data by comparison with the global Terra MODIS NDVI (MOD13C2 Collection 5) data using linear regression trend analysis.
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TL;DR: In this article, the authors present the case for using Lidar sampling as a means to enable timely and robust large-area characterizations, and discuss the potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring.
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University of Copenhagen1, University of Maryland, College Park2, Council for Scientific and Industrial Research3, ETH Zurich4, University of Bonn5, Centre national de la recherche scientifique6, Potsdam Institute for Climate Impact Research7, Clark University8, University of Virginia9, University of Florida10, Lund University11, Cheikh Anta Diop University12, University of Buenos Aires13, Helmholtz Centre for Environmental Research - UFZ14
TL;DR: In this paper, the authors provided an analysis of trends in vegetation greenness of semi-arid areas using AVHRR GIMMS from 1981 to 2007, and found that greenness increases are found both in semi-arsid areas where precipitation is the dominating limiting factor for plant production (0.019 NDVI units) and in semiarid regions where air temperature is the primarily growth constraint (0.,013 NDVI Units).
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TL;DR: In this article, the utility of moderate-resolution thermal satellite imagery in water resource management is explored, including methods developed to safeguard evapotranspiration (ET) estimates from expected errors in the remote sensing inputs.
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TL;DR: Sentinel-3 as mentioned in this paper is an Earth observation satellite mission specifically designed for Global Monitoring for Environment and Security (GMES) to ensure the long-term collection and operational delivery of high-quality measurements to GMES ocean, land, and atmospheric services, while contributing to the emergency and security services.
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TL;DR: In this article, a random forest classifier was applied to spectral as well as mono- and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape.
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TL;DR: In this article, a multi-purpose time-series-based disturbance detection approach is proposed to identify and model stable historical variation to enable change detection within newly acquired data, which can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling.
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TL;DR: In this paper, a multispectral expert system used a neural network approach to provide Rapid Response thickness class maps using a spectral library approach based on the shape and depth of near infrared spectral absorption features.
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TL;DR: In this article, a method for the retrieval of sun-induced terrestrial chlorophyll fluorescence (F_s) from the Fraunhofer lines resolved by GOSAT-FTS measurements is presented.
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TL;DR: The Sentinel-1, -2, and -3 satellite missions can meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities as mentioned in this paper.
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TL;DR: In this article, the authors studied the short and long-term characteristics of lake inundation and found significant seasonality and inter-annual variability in the monthly and annual mean inundation areas.
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TL;DR: GPR proved to be a fast and accurate nonlinear retrieval algorithm that can be potentially implemented for operational monitoring applications and provided confidence intervals of the estimates and insight in relevant bands, which are key advantages over the other methods.
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TL;DR: In this article, the authors assessed four techniques: Fourier analysis, asymmetric Gaussian model, double logistic model and the Whittaker filter for smoothing multi-temporal satellite sensor observations with the ultimate purpose of deriving an appropriate annual vegetation growth cycle and estimating phenological parameters reliably.
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TL;DR: In this article, the authors used three regression models: linear, power-law and exponential functions to quantify the long-term relationships between nighttime weighted light area and four urbanization variables: population, gross domestic product (GDP), built-up area and electric power consumption.
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TL;DR: In this paper, the authors analyzed two multi-sensor set-ups: (1) airborne high spatial resolution hyperspectral images combined with LiDAR data; and (2) high spatial-resolution satellite multispectral image combined with Lidar data.
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TL;DR: In this article, a straight forward, application-oriented approach using multi-temporal remotely sensed data to systematically monitor the spatiotemporal dynamics of the world's urban giants is presented.
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TL;DR: In this article, a change detection algorithm for continuous monitoring of forest disturbance at high temporal frequency is developed using all available Landsat 7 images in two years, time series models consisting of sines and cosines are estimated for each pixel for each spectral band.
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TL;DR: A comprehensive overview of water constituent retrieval algorithms and underlying definitions and models for optically deep and complex waters using earth observation data is provided in this article, where the performance of these algorithms is assessed based on validation experiments published between January 2006 and May 2011.