A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements

Huang, Cheng; Duan, Si‐Bo; Jiang, Xuefeng; Han, Xiao-Jing; Leng, Pei; Gao, Maofang; Li, Zhao-Liang

doi:10.1080/01431161.2018.1508920

Cited by 45 publications

(15 citation statements)

References 40 publications

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“…There are a large number of different sizes of cloud droplets, raindrops and ice crystals in the cloud from which microwave radiation signals from the ground are scattered during upward transmission [37,38]. Although microwave radiation can penetrate clouds, rain and the atmosphere, it is attenuated during the transmission process, which reduces the amount of radiation reaching the satellite sensor [39][40][41][42]. If the attenuated observations were used to calculate the parameters, the retrieving results for the surface emissivity would be directly affected by the satellite observation errors.…”

Section: Data Processingmentioning

confidence: 99%

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

Qian

Bao

et al. 2019

Remote Sensing

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The Qinghai-Tibetan plateau plays an important role in climate change with its unique characteristics, and the surface emissivity is an important parameter to describe the surface characteristics. It is also very important for the accurate retrieval of surface and atmospheric parameters. Different types of surface features have their own radiation characteristics due to their differences in structure, water content and roughness. In this study, the microwave land surface emissivity (10.65, 18.7, 23.8, 36.5 and 89 GHz) of the Qinghai-Tibetan Plateau was calculated using the simplified microwave radiation transmission equation under clear atmospheric conditions based on Level 1 brightness temperatures from the Microwave Radiation Imager onboard the FY-3B meteorological satellite (FY-3B/MWRI) and the National Centers for Environmental Prediction Final (NCEP-FNL) Global Operational Analysis dataset. Furthermore, according to the IGBP (International Geosphere-Biosphere Program) classified data, the spectrum and spatial distribution characteristics of microwave surface emittance in Qinghai-Tibetan plateau were further analyzed. The results show that almost all 16 types of emissivity from IGBP at dual-polarization (vertical and horizontal) increase with the increase of frequency. The spatial distribution of the retrieving results is in line with the changes of surface cover types on the Qinghai-Tibetan plateau, showing the distribution characteristics of large polarization difference of surface emissivity in the northwest and small polarization difference in the southeast, and diverse vegetation can be clearly seen in the retrieving results. In addition, the emissivity is closely related to the type of land surface. Since the emissivity of vegetation is higher than that of bare soil, the contribution of bare soil increases and the surface emissivity decreases as the density of vegetation decreases. Finally, the source of retrieval error was analyzed. The errors in calculating the surface emissivity might mainly come from spatiotemporal collocation of reanalysis data with satellite measurements, the quality of these auxiliary datasets and cloud and precipitation pixel discrimination scheme. Further quantitative analysis of these errors is required, and even standard procedures may need to be improved as well to improve the accuracy of the calculation.

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Section: Data Processingmentioning

confidence: 99%

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

Qian

Bao

et al. 2019

Remote Sensing

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“…According to the literature review, many studies have been conducted to retrieve the LST from the observations of the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Image (SSM/I), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer (AMSR, named AMSR‐E on satellite Aqua and AMSR2 on satellite GCOM‐W), etc. (Chen et al, 2011; Huang et al, 2018; Mao et al, 2018; McFarland et al, 1990; Njoku & Li, 1999; Pulliainen et al, 1997; Zhou et al, 2015). The algorithms used to retrieve the LST from MW observations can be grouped into the following three categories: the physical models, the semiempirical models, and the empirical statistical models.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR‐E Data Considering the Comprehensive Effects of Environmental Variables

Zhang

Cheng

2020

Earth and Space Science

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Microwave (MW) remote sensing has the potential to obtain all-weather land surface temperature (LST) and serves as a complement to the thermal-infrared (TIR) LST under cloudy sky conditions. However, the accuracy of MW LST is generally lower than that of TIR LST, making the retrieval of highly accurate all-weather LST a challenging task. We propose an empirical algorithm for retrieving LST from the Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperature (BT) data. First, we constructed a comprehensive classification system of environmental variables (CCSEV), allowing for the influence of topography, land cover, solar radiation, and atmospheric condition on the spatiotemporal distribution of LST, then the LST was expressed as a function of the combination of different AMSR-E channels for each CCSEV class. When performing the testing with the data from 2005, 2009 and 2011, the accuracy is 3.27 K, 2.65 K and 3.48 K in the daytime and 2.94 K, 2.63 K, 2.15 K at nighttime, respectively. The proposed algorithm was compared to an existing algorithm developed for China without considering the topography. The result shows that the accuracy of LST has improved by 2.81 K in the daytime and 2.14 K at nighttime in China, compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST. The verification at the Naqu sites in the Qinghai-Tibet Plateau shows that the accuracy has improved by 1-2 K in the daytime and 0.7-1 K at nighttime. These results indicate that the developed algorithm is universal and accurate and benefits the retrieval of accurate all-weather LST.

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“…MW LST) and TIR LST. Many studies have focused on the development of MW LST retrieval algorithms and the accuracy of retrieved MW LST has obviously improved [20][21][22][23][24][25][26][27]; however, few studies have paid attention to its spatial scale difference with TIR LST.…”

Section: Introductionmentioning

confidence: 99%

A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature from AMSR-E Data

Zhang

Wang

Cheng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A stepwise downscaling method is proposed for generating high-resolution land surface temperature (LST) from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data to benefit the fusion of thermal infrared and microwave data for high quality all-weather LST. This method sets a series of intermediate resolution levels between the initial (0.25°) and target (0.01°) resolutions, then downscales AMSR-E LST from one resolution to the next one step at a time, starting from 0.25° and ending with 0.01°. The geographically weighted regression model is adopted in each step to construct the relationship between LST and environmental variables, including normalized differential vegetation index, elevation and slope. The stepwise method is verified over three regions in China that represent different characteristics of landscape heterogeneity varying from the highest to the lowest: the Yunnan-Guizhou Plateau (YGP), the border of Shanxi Province and Henan Province (BSH), and the center part of Inner Mongolia (CIM). Verified using the emulated AMSR-E LST resampled from reference MODIS LST available in 2010, the results show that the proportions of dates when the stepwise method is better are 100%, 78.1%, and 51.5% in the YGP, BSH, and CIM regions, respectively, which means the stepwise method has advantage over the direct method in the regions with high heterogeneity. For real AMSR-E LST, the downscaled LST exhibits a similar spatial pattern to that of emulated data but suffers from reduced accuracy and contrast, which is caused by the smooth spatial pattern and low accuracy of the real AMSR-E LST.

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A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements

Cited by 45 publications

References 40 publications

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR‐E Data Considering the Comprehensive Effects of Environmental Variables

A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature from AMSR-E Data

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