Development of a land surface emissivity algorithm for use by microwave rain retrieval algorithms

2012 ◽  
Author(s):  
Fumie A. Furuzawa ◽  
Hirohiko Masunaga ◽  
Kenji Nakamura
Keyword(s):  
Land Surface ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Retrieval Algorithms ◽  
Rain Retrieval

scholarly journals Study of Aerosol Influence on Nighttime Land Surface Temperature Retrieval Based on Two Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Caixia Gao ◽  
Enyu Zhao ◽  
Chuanrong Li ◽  
Yonggang Qian ◽  
Lingling Ma ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Vapor ◽  
Standard Deviation ◽  
Land Surface ◽  
Simulated Data ◽  
Mean Square ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Instrument Noise ◽  
Temperature Retrieval

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

Dependence of Simulation Biases at AHI Surface-Sensitive Channels on Land Surface Emissivity over China

2018 ◽  
Vol 35 (6) ◽  
pp. 1283-1298 ◽  
Author(s):  
X. Zhuge ◽  
X. Zou ◽  
F. Weng ◽  
M. Sun
Keyword(s):  
Brightness Temperature ◽  
Spectral Resolution ◽  
Land Surface ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Emissivity ◽  
Bare Rock ◽  
Land Surface Emissivity ◽  
M Channels

AbstractThis study compares the simulation biases of Advanced Himawari Imager (AHI) brightness temperature to observations made at night over China through the use of three land surface emissivity (LSE) datasets. The University of Wisconsin–Madison High Spectral Resolution Emissivity dataset, the Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer and Moderate Resolution Imaging Spectroradiometer Emissivity database over Land High Spectral Resolution Emissivity dataset, and the International Geosphere–Biosphere Programme (IGBP) infrared LSE module, as well as land skin temperature observations from the National Basic Meteorological Observing stations in China are used as inputs to the Community Radiative Transfer Model. The results suggest that the standard deviations of AHI observations minus background simulations (OMBs) are largely consistent for the three LSE datasets. Also, negative biases of the OMBs of brightness temperature uniformly occur for each of the three datasets. There are no significant differences in OMB biases estimated with the three LSE datasets over cropland and forest surface types for all five AHI surface-sensitive channels. Over the grassland surface type, significant differences (~0.8 K) are found at the 10.4-, 11.2-, and 12.4-μm channels if using the IGBP dataset. Over nonvegetated surface types (e.g., sandy land, gobi, and bare rock), the lack of a monthly variation in IGBP LSE introduces large negative biases for the 3.9- and 8.6-μm channels, which are greater than those from the two other LSE datasets. Thus, improvements in simulating AHI infrared surface-sensitive channels can be made when using spatially and temporally varying LSE estimates.

scholarly journals An objective methodology for infrared land surface emissivity evaluation

2010 ◽  
Vol 115 (D22) ◽  
Author(s):  
Zhenglong Li ◽  
Jun Li ◽  
Xin Jin ◽  
Timothy J. Schmit ◽  
Eva E. Borbas ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Objective Methodology

Linear Retrieval of Microwave Land Surface Emissivity over the Desert Area in January

2020 ◽  
Vol 57 (21) ◽  
pp. 212801
Author(s):  
官元红 Guan Yuanhong ◽  
王文君 Wang Wenjun ◽  
陆其峰 Lu Qifeng ◽  
鲍艳松 Bao Yansong ◽  
郑婷文 Zheng Tingwen
Keyword(s):  
Land Surface ◽  
Desert Area ◽  
Surface Emissivity ◽  
Land Surface Emissivity

scholarly journals Practical retrieval of land surface emissivity spectra in 8-14μm from hyperspectral thermal infrared data

2012 ◽  
Vol 20 (22) ◽  
pp. 24761 ◽  
Author(s):  
Hua Wu ◽  
Ning Wang ◽  
Li Ni ◽  
Bo-Hui Tang ◽  
Zhao-Liang Li
Keyword(s):  
Land Surface ◽  
Thermal Infrared ◽  
Surface Emissivity ◽  
Land Surface Emissivity ◽  
Emissivity Spectra ◽  
Infrared Data
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