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Urban Design Factors Influencing Surface Urban Heat Island in the High-Density City of Guangzhou Based on the Local Climate Zone

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SENSORS 2019年第16期19卷 3459-3459页

作者： Shi, Yurong Xiang, Yirui Zhang, Yufeng South China Univ Technol Dept Architecture State Key Lab Subtrop Bldg Sci 381 Wushan Rd Guangzhou 510640 Guangdong Peoples R China

Surface urban heat island (SUHI) depicts the deteriorating thermal environment in high-density cities and local climate zone (LCZ) classification provides a universal protocol for SUHI identification. In this study, taking the central urbanized area of Guangzhou in the humid subtropical region of China as the study area, the maps or images of LCZ, land surface temperature, SUHI, and urban design factors were achieved using Landsat satellite data, GIS database, and a series of retrieval and classification algorithms, and the urban design factors influencing SUHI were investigated based on 625 samples of LCZs. The results show that on the 18 September 2016 at the local time of 10:51 a.m., the land surface temperature (LST) varied greatly from 26 degrees C to 40 degrees C and the SUHI changed with a wide range of -6 degrees C to 8 degrees C in the LCZs of the study area. Seven and five urban design factors influencing the summer daytime SUHI were identified for the two dominant LCZs of LCZs 1-5 (LCZ 1 to LCZ 5) and the mixed LCZ (containing at least three types of LCZs), respectively, in which vegetation cover ratio, floor area ratio, ground emissivity, and complete surface area ratio showed negative correlations and building density showed positive correlations. The summer daytime SUHI prediction models were obtained by using the step-wise multiple linear regression, with the performance of R-2 of 0.774, RMSE of 0.95 degrees C, and the d value of 0.91 for the model of LCZs 1-5, and the values of 0.819, 0.81 degrees C, and 0.94 for the model of the mixed LCZ, indicating that the models can effectively predict the changes of SUHI with LCZs. This study presents a methodology to efficiently achieve a large sample of SUHI and urban design factors of LCZs, and provides information beneficial to the urban designs and regenerations in high-density cities.

关键词： land surface temperature surface urban heat island social climate zone retrieval algorithms

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Analyzing the Impact of Sensor Characteristics on retrieval Methods of Solar-Induced Fluorescence

Analyzing the Impact of Sensor Characteristics on Retrieval ...

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International Conference on Optical and Photonics Engineering (icOPEN)

作者： Ding, Wenjuan Zhao, Feng Yang, Lizi Beihang Univ Beijing Peoples R China

ISBN: (数字)9781510610026

ISBN: (纸本)9781510610019;9781510610026

In this study, we evaluated the influence of retrieval algorithms and sensor characteristics, such as spectral resolution (SR) and signal to noise ratio (SNR), on the retrieval accuracy of fluorescence signal (Fs). Here Fs was retrieved by four commonly used retrieval methods, namely the original Fraunhofer Line Discriminator method (FLD), the 3 bands FLD (3FLD), the improved FLD (iFLD) and the spectral fitting method (SFM). Fs was retrieved in the oxygen A band centered at around 761nm (O2-A). We analyzed the impact of sensor characteristics on four retrieval methods based on simulated data which were generated by the model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), and obtained consistent conclusions when compared with experimental data. Results presented in this study indicate that both retrieval algorithms and sensor characteristics affect the retrieval accuracy of Fs. When applied to the actual measurement, we should choose the instrument with higher performance and adopt appropriate retrieval method according to measuring instruments and conditions.

关键词： Fluorescence signal (Fs) retrieval accuracy retrieval algorithms sensor characteristics

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An Integrated Examination of AMSR2 Products Over Ocean

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第9期10卷 3963-3974页

作者： Duncan, David Ian Kummerow, Christian D. Meier, Walter N. Colorado State Univ Dept Atmospher Sci Ft Collins CO 80523 USA NASA Goddard Space Flight Ctr Cryospher Sci Lab Greenbelt MD 20771 USA

Integrated processing and analysis are presented for oceanic retrievals of precipitation, sea ice concentration, columnar water vapor and cloud water, sea surface temperature, and near-surface wind from the advanced microwave scanning radiometer-2 (AMSR2) sensor. By developing a common algorithmic framework and permitting iterative interaction between historically separate science algorithms, ambiguous and contradictory retrieval results are minimized. The integration also serves to improve each algorithm individually, decreasing reliance on ancillary datasets. Case studies are presented that exemplify both ongoing challenges for these retrievals and potential research uses of such an integrated satellite product. Two cases are presented each for precipitation at high latitudes and marginal sea ice detection, with analysis supplemented by satellite data from CloudSat profiles and Himawari-8 imagery. Detection and retrieval of snowfall remains a challenge, while light rainfall detection can be aided by a variational algorithm. Sea ice concentrations below 20% cause disagreements, but the algorithms otherwise agree well on the sea ice edge. Potential mitigation strategies for ambiguous areas of light rainfall and marginal sea ice are discussed. The analysis demonstrates potential avenues for future algorithm development, but also some physical limitations of remote sensing with the AMSR2 frequencies.

关键词： Passive microwave remote sensing precipitation retrieval algorithms sea ice variational methods

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New Satellite Passive Microwave retrieval algorithms and their Application to Study Atmospheric Phenomena and Oceanic Parameters

New Satellite Passive Microwave Retrieval Algorithms and the...

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International Conference on Remote Sensing, Environment and Transportation Engineering

作者： Elizaveta Zabolotskikh Bertrand Chapron Leonid Bobylev Satellite Oceanography Laboratory RSHU St. Petersburg Russia Department of Oceanography and Ecosystem Dynamics IFREMER Brest France Nansen Centre St. Petersburg Russia

ISBN: (纸本)9781467308724

The new high accuracy algorithms for atmospheric and oceanic parameter retrievals from satellite passive microwave measurement data, based on Neural Network (NN) technique, employed for the radiative transfer equation inversion, are considered in this study. Extensive calibration of Special Sensor Microwave Imager/Sounder (SSMIS) measurement data onboard Defense Meteorological Satellite Program (DMSP) satellites (F16, F17 and F18) is fulfilled against the geophysical model used for brightness temperature calculations. The application of these algorithms, along with previously developed, applicable to Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Sounding Radiometer - Earth Observing System (AMSR-E) instruments, to satellite data allows conducting various case studies, including synoptic-scale and mesoscale weather system (e.g. polar lows, extratropical and tropical cyclones) investigation. Several case studies of weather systems in tropical and polar regions are comprehensively considered, involving all available satellite and in-situ data, with an accent on satellite passive microwave data usage.

关键词： satellite passive microwave data Neural Networks retrieval algorithms multisensor data usage, weather system studies

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THE BIOMASS MISSION retrieval algorithms: RESULTS FROM RECENT CAMPAIGNS

THE BIOMASS MISSION RETRIEVAL ALGORITHMS: RESULTS FROM RECEN...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Thuy Le Toan Lars Ulander Kostas Papathanassiou Fabio Rocca Shaun Quegan Malcolm Davidson Klaus Scipal CESBIO CNRS-CNES-Universite Paul Sabatier-IRD Department of Radar Systems FOI German Aerospace Center e. V. (DLR) Dipartimento di Elettronica ed Informazione Politecnico di Milano Centre for Terrestrial Carbon Dynamics University of Sheffield ESA-ESTEC

ISBN: (纸本)9781467311601

The BIOMASS mission is designed to map the full range of the world's above-ground forest biomass, for the needs of national scale inventory and global carbon flux calculations. This objective is achieved with advanced P-band SAR techniques. The P-band biomass measurement concept was based on previous work over the past two decades. During the preparatory phase, new campaigns have been conducted to address critical issues on the biomass retrieval algorithms, over tropical and boreal forests. The collected datasets comprise accurate and complete sets of in situ data and advanced P-band SAR data. This paper presents the retrieval algorithms developed using the collected datasets.

关键词： BIOMASS mission P-band SAR Polarimetry Pol-InSAR Tomography retrieval algorithms

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Effective Scattering Albedo of Forests Retrieved by SMOS and a Three-Parameter Algorithm

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2017年第12期14卷 2260-2264页

作者： Vittucci, C. Ferrazzoli, P. Richaume, P. Kerr, Y. Tor Vergata Univ Rome Dipartimento Ingn Civile & Ingn Informat I-00133 Rome Italy Ctr Etud Spatiales BIOsphere F-31401 Toulouse France

This letter illustrates results obtained using brightness temperature data collected by the Soil Moisture and Ocean Salinity radiometer and a three-parameter algorithm, retrieving soil moisture, vegetation optical depth, and the effective albedo of vegetation. Four eight-day time intervals have been considered. Only areas with a forest cover higher than 90% have been selected. For tropical forests, the retrieved albedo is in a range 0.05-0.07 and tends to decrease with an increasing optical depth. For these forests, the results are scarcely influenced by seasonal variations. For boreal forests of North America, the range of retrieved albedo is again 0.05-0.07 in July, but lower values are found in November for needleleaf forests of Canada and Northern U.S. For forests with higher optical depth, retrieved values of effective albedo show a lower spread.

关键词： Forests microwave radiometry retrieval algorithms

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Improving Land Surface Temperature retrievals over Mountainous Regions

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REMOTE SENSING 2017年第1期9卷 38-38页

作者： Bento, Virgilio A. DaCamara, Carlos C. Trigo, Isabel F. Martins, Joao P. A. Duguay-Tetzlaff, Anke Univ Lisbon Inst Dom Luiz Ed C1 P-1749016 Lisbon Portugal Inst Portugues Mar & Atmosfera IP Rua C do Aeroporto P-1749077 Lisbon Portugal Fed Off Meteorol & Climatol MeteoSwiss Operat Ctr 1 CH-8058 Zurich Switzerland

algorithms for Land Surface Temperature (LST) retrieval from infrared measurements are usually sensitive to the amount of water vapor present in the atmosphere. The Satellite Application Facilities on Climate Monitoring and Land Surface Analysis (CM SAF and LSA SAF) are currently compiling a 25 year LST Climate data record (CDR), which uses water vapor information from ERA-Int reanalysis. However, its relatively coarse spatial resolution may lead to systematic errors in the humidity profiles with implications in LST, particularly over mountainous areas. The present study compares LST estimated with three different retrieval algorithms: a radiative transfer-based physical mono-window (PMW), a statistical mono-window (SMW), and a generalized split-windows (GSW). The algorithms were tested over the Alpine region using ERA-Int reanalysis data and relied on the finer spatial scale Consortium for Small-Scale Modelling (COSMO) model data as a reference. Two methods were developed to correct ERA-Int water vapor misestimation: (1) an exponential parametrization of total precipitable water (TPW) appropriate for SMW/GSW;and (2) a level reduction method to be used in PMW. When ERA-Int TPW was used, the algorithm missed the right TPW class in 87% of the cases. When the exponential parametrization was used, the missing class rate decreased to 9%, and when the level reduction method was applied, the LST corrections went up to 1.7 K over the study region. Overall, the correction for pixel orography in TPW leads to corrections in LST estimations, which are relevant to ensure that long-term LST records meet climate requirements, particularly over mountainous regions.

关键词： LST Split-Windows Mono-Window retrieval algorithms thermal infrared Meteosat water vapor orographic correction

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Recent Advances in Soil Moisture Estimation from Remote Sensing

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WATER 2017年第7期9卷 530-530页

作者： Peng, Jian Loew, Alexander Ludwig Maximilians Univ Munchen Dept Geog D-80333 Munich Germany Max Planck Inst Meteorol D-20146 Hamburg Germany

Monitoring soil moisture dynamics from local to global scales is essential for a wide range of applications. The field of remote sensing of soil moisture has expanded greatly and the first dedicated soil moisture satellite missions (SMOS, SMAP) were launched, and new missions, such as SENTINEL-1 provide long-term perspectives for land surface monitoring. This special issue aims to summarize the recent advances in soil moisture estimation from remote sensing, including recent advances in retrieval algorithms, validation, and applications of satellite-based soil moisture products. Contributions in this special issue exploit the estimation of soil moisture from both microwave remote sensing data and thermal infrared information. The validation of satellite soil moisture products can be very challenging, due to the different spatial scales of in situ measurements and satellite data. Some papers present validation studies to quantify soil moisture uncertainties. On the other hand, soil moisture downscaling schemes and new methods for soil moisture retrieval from GPS are also addressed by some contributions. Soil moisture data are used in fields like agriculture, hydrology, and climate sciences. Several studies explore the use of soil moisture data for hydrological application such as runoff prediction.

关键词： soil moisture remote sensing retrieval algorithms uncertainties validations applications

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Improvements in atmospheric water vapor content retrievals over open oceans from satellite passive microwave radiometers 14

Improvements in atmospheric water vapor content retrievals o...

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14th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)

作者： Zabolotskikh, Elizaveta Chapron, Bertrand Russian State Hydrometeorol Univ Satellite Oceanog Lab St Petersburg Russia IFREMER Dept Oceanog & Ecosyst Dynam Brest France

ISBN: (纸本)9781509029518

High accuracy retrieval algorithm is developed for integrated water vapor content (WVC) retrieval from Advanced Microwave Sounding Radiometer 2 (AMSR2) measurements over open ocean areas. The algorithm is based on physical modeling of the brightness temperature (BT) of the upwelling radiation of the atmosphere - ocean system. The brightness temperature inversion is carried out with Neural Networks (NNs), trained on an ensemble of BTs, calculated using the data set of the atmospheric and oceanic parameters, governing passive microwave radiation of the atmosphere - ocean system. The improvement in the WVC retrieval is associated with the usage of the new ocean emissivity empirical model and exclusion of vertically polarized BT from the NNs inputs. The new model was incorporated into BT geophysical model and a new set of NNs coefficients was obtained for WVC algorithm. Though the results of the numerical experiment demonstrate higher retrieval accuracy for the old version of the NN model, real-valued validation with Global Positioning System WVC shows better performance of the new NN model with excluded vertically polarized measurements from NN inputs. The advantages of the new algorithm are the most remarkable under conditions of high winds. This is especially important in the studies of extreme weather events such as Polar Lows, Extratropical and Tropical Cyclones associated with high winds. Examples of WVC retrievals under extreme events are given.

关键词： satellite passive microwave measurement data retrieval algorithms integrated atmospheric water vapor content high winds Neural Networks Arctic Polar Lows

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User Intent in Multimedia Search: A Survey of the State of the Art and Future Challenges

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ACM COMPUTING SURVEYS 2016年第2期49卷 36-36页

作者： Kofler, Christoph Larson, Martha Hanjalic, Alan Bloomberg LP Search & Discoverabil 731 Lexington Ave New York NY 10022 USA Delft Univ Technol Fac Elect Engn Math & Comp Sci Dept Intelligent Syst Multimedia Comp Grp Mekelweg 4 NL-2628 CD Delft Netherlands

Today'smultimedia search engines are expected to respond to queries reflecting a wide variety of information needs from users with different goals. The topical dimension ("what"the user is searching for) of these information needs is well studied;however, the intent dimension ("why"the user is searching) has received relatively less attention. Specifically, intent is the "immediate reason, purpose, or goal"that motivates a user to query a search engine. We present a thorough survey of multimedia information retrieval research directed at the problem of enabling search engines to respond to user intent. The survey begins by defining intent, including a differentiation from related, often-confused concepts. It then presents the key conceptual models of search intent. The core is an overview of intent-aware approaches that operate at each stage of the multimedia search engine pipeline (i.e., indexing, query processing, ranking). We discuss intent in conventional text-based search wherever it provides insight into multimedia search intent or intent-aware approaches. Finally, we identify and discuss the most important future challenges for intent-aware multimedia search engines. Facing these challenges will allow multimedia information retrieval to recognize and respond to user intent and, as a result, fully satisfy the information needs of users.

关键词： algorithms Design Human Factors Theory User intent multimedia information retrieval multimedia search multimedia indexing retrieval algorithms

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