Global Microwave Land Surface Emissivity Under all Weather Conditions Derived From Fengyun‐3D Satellite Observations

Abstract A global microwave land surface emissivity (MLSE) data set from January 2020 to December 2022 under all‐weather conditions was retrieved using microwave brightness temperatures from Fengyun‐3D Microwave Radiation Imager (MWRI), combined with cloud properties derived from the Medium Resolution Spectral Imager on the same satellite. The contributions of clouds and atmospheric particles were calculated using a four‐stream microwave radiative transfer model and subtracted from the Tbs measured at the top of the atmosphere. To evaluate the MWRI MLSE, we compared it with four reference clear‐sky emissivity data sets retrieved from Global Precipitation Mission Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR‐E), and the Tool to Estimate Land Surface Emissivity at Microwaves and Millimeter waves (TELSEM) which is based on the measurements of Special Sensor Microwave/Imager (SSM/I). We also assessed the sensitivity of seasonal variations of MLSE to surface conditions indicated by the normalized difference vegetation index and precipitation seasonal cycles. Results demonstrate that our retrieval achieves good consistencies with the four reference products, exhibiting similar surface characteristics and spectral dependencies. Based on the all‐weather retrievals, we studied the potential impacts of clouds on MLSE in tropical regions. Results indicate that the presence of clouds may lead to a decrease in land surface emissivity due to potentially increased moisture in the soil‐vegetation mediums.

Authors

Institutions

Publication Details

Journal
Journal of Geophysical Research Atmospheres
Published
2025-06-07
DOI
https://doi.org/10.1029/2024jd043282
Citations
4
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
3.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Global Microwave Land Surface Emissivity Under all Weather Conditions Derived From Fengyun‐3D Satellite Observations

Jiheng Hu, Peng Zhang, Rui Li, Yonghong Liu et al.
4 citations
Journal of Geophysical Research Atmospheres
Soil Moisture and Remote Sensing
3.20
article

Global Microwave Land Surface Emissivity Under all Weather Conditions Derived From Fengyun‐3D Satellite Observations

Jiheng Hu, Peng Zhang, Rui Li, Yonghong Liu, Qingyang Liu
article en
4 citations

Abstract

Abstract A global microwave land surface emissivity (MLSE) data set from January 2020 to December 2022 under all‐weather conditions was retrieved using microwave brightness temperatures from Fengyun‐3D Microwave Radiation Imager (MWRI), combined with cloud properties derived from the Medium Resolution Spectral Imager on the same satellite. The contributions of clouds and atmospheric particles were calculated using a four‐stream microwave radiative transfer model and subtracted from the Tbs measured at the top of the atmosphere. To evaluate the MWRI MLSE, we compared it with four reference clear‐sky emissivity data sets retrieved from Global Precipitation Mission Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR‐E), and the Tool to Estimate Land Surface Emissivity at Microwaves and Millimeter waves (TELSEM) which is based on the measurements of Special Sensor Microwave/Imager (SSM/I). We also assessed the sensitivity of seasonal variations of MLSE to surface conditions indicated by the normalized difference vegetation index and precipitation seasonal cycles. Results demonstrate that our retrieval achieves good consistencies with the four reference products, exhibiting similar surface characteristics and spectral dependencies. Based on the all‐weather retrievals, we studied the potential impacts of clouds on MLSE in tropical regions. Results indicate that the presence of clouds may lead to a decrease in land surface emissivity due to potentially increased moisture in the soil‐vegetation mediums.

Journal of Geophysical Research AtmospheresVol. 130(11)
University of Science and Technology of China (CN), China Meteorological Administration (CN), University of Michigan (US)
Openalex Percentile: Top 9%
Soil Moisture and Remote Sensing
3.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.