scholarly journals Regional evaluation of an advanced very high resolution radiometer (AVHRR) two-channel aerosol retrieval algorithm

2004 ◽  
Vol 109 (D2) ◽  
Author(s):  
Tom X.-P. Zhao
Keyword(s):  
High Resolution ◽  
Retrieval Algorithm ◽  
Aerosol Retrieval ◽  
Very High

A Simplified high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces

2013 ◽  
Vol 136 ◽  
pp. 135-145 ◽  
Author(s):  
Muhammad Bilal ◽  
Janet E. Nichol ◽  
Max P. Bleiweiss ◽  
David Dubois
Keyword(s):  
High Resolution ◽  
Retrieval Algorithm ◽  
Aerosol Retrieval

scholarly journals Retrieval of aerosol optical depth over land surfaces from AVHRR data

2013 ◽  
Vol 6 (1) ◽  
pp. 2227-2251 ◽  
Author(s):  
L. Mei ◽  
Y. Xue ◽  
A. A. Kokhanovsky ◽  
W. von Hoyningen-Huene ◽  
G. de Leeuw ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Surface Reflectance ◽  
Aerosol Retrieval ◽  
Avhrr Data ◽  
Land Surfaces ◽  
Very High

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) radiance data provide a global, long-term, consistent time series having high spectral and spatial resolution and thus being valuable for the retrieval of surface spectral reflectance, albedo and surface temperature. Long term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. These data have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land and land surface albedo are challenging because of the information content of the measurement is limited and the inversion of these data products being ill defined. Solving the radiative transfer equations requires additional information and knowledge to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in Moderate Resolution Imaging Spectroradiometer (MODIS) data. Next, following the MODIS dark target approach, the surface reflectance at 0.64 μm was obtained. The comparison of the estimated surface reflectance at 0.64 μm with MODIS reflectance products (MOD09) shows a strong correlation (R = 0.7835). Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to Advanced Very High Resolution Radiometer (AVHRR) data. A simplified Look-Up Table (LUT) method, adopted from Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The Aerosol Optical Depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with a correlation coefficient R = 0.861 and Root Mean Square Error (RMSE) = 0.17. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to geostationary satellites.

scholarly journals Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

2021 ◽  
Author(s):  
Mohammad Reza Ehsani ◽  
Ali Behrangi ◽  
Abishek Adhikari ◽  
Yang Song ◽  
George J. Huffman ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Auxiliary Information ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
High Latitudes ◽  
Precipitation Measurement ◽  
Frequent Sampling ◽  
Global Precipitation ◽  
Very High

AbstractPrecipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High-Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because (1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; (2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and (3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.

Very High Resolution Analysis of the Dynamics of a Coronal Plasmoid

1994 ◽  
Vol 144 ◽  
pp. 593-596
Author(s):  
O. Bouchard ◽  
S. Koutchmy ◽  
L. November ◽  
J.-C. Vial ◽  
J. B. Zirker
Keyword(s):  
High Resolution ◽  
Solar Eclipse ◽  
Total Solar Eclipse ◽  
Field Of View ◽  
Small Field ◽  
High Resolution Analysis ◽  
Ccd Observations ◽  
Resolution Analysis ◽  
Very High ◽  
Small Field Of View

AbstractWe present the results of the analysis of a movie taken over a small field of view in the intermediate corona at a spatial resolution of 0.5“, a temporal resolution of 1 s and a spectral passband of 7 nm. These CCD observations were made at the prime focus of the 3.6 m aperture CFHT telescope during the 1991 total solar eclipse.

Three-dimensional fault geometry and kinematics of the 2008 M 7.1 Yutian earthquake revealed by very-high resolution satellite stereo imagery

2019 ◽  
Vol 232 ◽  
pp. 111300
Author(s):  
Xiaogang Song ◽  
Nana Han ◽  
Xinjian Shan ◽  
Chisheng Wang ◽  
Yingfeng Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
Fault Geometry ◽  
Stereo Imagery ◽  
Very High ◽  
Geometry And Kinematics

scholarly journals Research on Aerosol Retrieval Algorithm of Multi-parameter Optimization Model

2021 ◽  
Vol 658 (1) ◽  
pp. 012039
Author(s):  
Jiabao Yue ◽  
Donghai Xie ◽  
Jie Yu ◽  
Lin Zhu ◽  
Zhengyang He
Keyword(s):  
Parameter Optimization ◽  
Optimization Model ◽  
Retrieval Algorithm ◽  
Aerosol Retrieval

scholarly journals Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2508
Author(s):  
Loredana Oreti ◽  
Diego Giuliarelli ◽  
Antonio Tomao ◽  
Anna Barbati
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Parametric Method ◽  
Mixed Forest ◽  
Mixed Stands ◽  
Mapping Unit ◽  
Mixed Forests ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
Very High

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

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