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Arindam Samanta, Sangram Ganguly, Eric Vermote, Ramakrishna R. Nemani, and Ranga B. Myneni

both MOD13A2 and MOD13C1/C2 and can assume values from 0 (0000: best quality) to 15 (1111: not useful). 4. Results and discussion Remote sensing of the Amazon forests at solar wavelengths is complicated due to persistent cloud and aerosols presence. Atmospheric corruption of surface reflectance data, from which the vegetation indices are evaluated, depends on prevailing cloud and aerosol optical thicknesses, among other factors ( Vermote and Kotchenova 2008 ; Tanre et al. 1992 ). The two data

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Yang Zhao, Gerald G. Mace, and Jennifer M. Comstock

the cirrus research community regarding the contribution of the small particle mode to cirrus bulk properties with that argument being derived principally from data collected in situ. Following Mitchell et al. (2010) we examine remote sensing data to shed light on this issue except that we use a ground-based dataset consisting of extinction derived from Raman lidar combined with coincident Doppler velocity and radar reflectivity measured by Ka-band Doppler radar. We assume, as did Ivanova et al

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Carola Dahlke, Alexander Loew, and Christian Reick

conditions. However, our experience with climate modeling shows that phenology is a critical aspect in global climate models. Phenology models are problematic because of the large uncertainties in modeling. So far, only a few studies assess phenology models with satellite observation data (e.g., Lüdeke et al. 1996 ; Randerson et al. 2009 ), and the reason for this clearly lies in the difficulty of determining comparable phenological events from different datasets, models, and remote sensing

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Craig R. Ferguson and Eric F. Wood

many mechanisms that constitute land–atmosphere interaction (coupling) is viewed by the World Climate Research Programme (WCRP) Global Energy and Water Cycle Experiment (GEWEX) as a key step toward improving short-to-medium-range weather forecast skill. A multiyear global categorization of coupling is unprecedented and only now is feasible with remote sensing. In the past, coupling research has largely focused on the analysis of large-scale model outputs. Betts (2004) developed many of the

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J. Bühl, S. Alexander, S. Crewell, A. Heymsfield, H. Kalesse, A. Khain, M. Maahn, K. Van Tricht, and M. Wendisch

1. Introduction A major goal of remote sensing of ice in clouds is the measurement of cloud optical properties because ice-forming clouds can influence Earth’s radiative properties ( Fig. 10-1 ). Figure 10-1a indicates that the magnitude of the solar radiative cooling of mixed-phase clouds strongly depends on the ice content. The more ice is in the cloud, the less the solar cooling effect. This is mostly a result of the decreasing optical thickness of the cloud when the ice content increases

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Gregory P. Gerbi, Emmanuel Boss, P. Jeremy Werdell, Christopher W. Proctor, Nils Haëntjens, Marlon R. Lewis, Keith Brown, Diego Sorrentino, J. Ronald V. Zaneveld, Andrew H. Barnard, John Koegler, Hugh Fargher, Matthew DeDonato, and William Wallace

1. Introduction Satellite-based radiometry missions of the ocean require validation of data products by in situ measurements to assess and improve, if necessary, the accuracy of satellite-derived quantities ( Mueller et al. 2003b ). The most fundamental of the quantities estimated by satellites are water-leaving radiance and remote sensing reflectance (the ratio of upwelling radiance to downwelling irradiance at the ocean surface). These are used to determine optical and biological

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Martha C. Anderson, Christopher Hain, Brian Wardlow, Agustin Pimstein, John R. Mecikalski, and William P. Kustas

remote sensing evaporative stress index (ESI), representing temporal anomalies in the ratio of actual evapotranspiration (ET) to potential ET (PET). In contrast with precipitation-based indices, the ESI algorithm requires no information about antecedent precipitation or subsurface soil characteristics. In this modeling approach, time-differential land surface temperature (LST) measurements derived from satellite imagery collected by the Geostationary Operational Environmental Satellites (GOES) in the

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Trent W. Ford, Steven M. Quiring, Balbhadra Thakur, Rohit Jogineedi, Adam Houston, Shanshui Yuan, Ajay Kalra, and Noah Lock

dataset used. In this study, we will focus on soil moisture dataset dependency, because our analysis does not rely on precipitation observations (see below). Many studies infer soil moisture–precipitation feedback from a single source of soil moisture information, whether in situ measurement, remote sensing observation, or model simulation. Many studies have evaluated the differences between these soil moisture datasets (e.g., Albergel et al. 2012 ; Su et al. 2013 ; Tuttle and Salvucci 2014

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Daniela Nowak, Dominique Ruffieux, Judith L. Agnew, and Laurent Vuilleumier

1. Introduction Precise forecasting of the formation, evolution, and erosion of fog and low stratus is a major challenge for meteorology, especially in complex topography. One of the goals of the COST 720 Temperature, Humidity, and Cloud (TUC) winter experiment undertaken in Switzerland in 2003/04 ( Ruffieux et al. 2006 ) was to provide a dataset for determining the base and top of low clouds using a simple combination of ground-based remote sensing instruments. Frequent and detailed

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Pennan Chinnasamy, Jason A. Hubbart, and Govindasamy Agoramoorthy

that formulation of management plans for groundwater resources for a vast country like India will be challenging because of limited observation sites and data frequency. While unable to replace direct monitoring, satellite technology may provide a useful indirect method to complement current observed datasets leading to more accurate groundwater resource estimations ( Rodell et al. 2009 ). Rodell et al. ( Rodell et al. 2009 ) used satellite remote sensing data to show that groundwater is being

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