Articles | Volume 20, issue 8
https://doi.org/10.5194/hess-20-3263-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-3263-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Aug 2016
Research article |
| 11 Aug 2016
Cloud tolerance of remote-sensing technologies to measure land surface temperature
Thomas R. H. Holmes
CORRESPONDING AUTHOR
Hydrology and Remote Sensing Lab., USDA-ARS, Beltsville, MD, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Christopher R. Hain
Earth Science Interdisciplinary Center, University of Maryland,
College Park, MD, USA
Martha C. Anderson
Hydrology and Remote Sensing Lab., USDA-ARS, Beltsville, MD, USA
Wade T. Crow
Hydrology and Remote Sensing Lab., USDA-ARS, Beltsville, MD, USA
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Cited
22 citations as recorded by crossref.
- Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. 10.5194/hess-24-3431-2020
- Retrieving K-Band Instantaneous Microwave Land Surface Emissivity Based on Passive Microwave Brightness Temperature and Atmospheric Precipitable Water Vapor Data F. Zhou et al. 10.1109/JSTARS.2017.2763167
- Partitioning evapotranspiration based on the total ecosystem conductance fractions of soil, interception, and canopy in different biomes M. Nguyen et al. 10.1016/j.jhydrol.2021.126970
- A two-step deep learning framework for mapping gapless all-weather land surface temperature using thermal infrared and passive microwave data P. Wu et al. 10.1016/j.rse.2022.113070
- Microwave implementation of two-source energy balance approach for estimating evapotranspiration T. Holmes et al. 10.5194/hess-22-1351-2018
- An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques T. Dowling et al. 10.3390/rs13173522
- Towards Estimating Land Evaporation at Field Scales Using GLEAM B. Martens et al. 10.3390/rs10111720
- Evaluation of evapotranspiration using energy-based and water balance hydrological models R. Fitria et al. 10.2166/wcc.2024.499
- Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques P. Wu et al. 10.1109/MGRS.2021.3050782
- Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data G. Thakur et al. 10.1038/s41598-022-12304-3
- Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions D. Chen et al. 10.3390/app12126068
- Predicting the onset of Betula pendula flowering in Poznań (Poland) using remote sensing thermal data P. Bogawski et al. 10.1016/j.scitotenv.2018.12.295
- A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing evapotranspiration from plant to global scales M. Anderson et al. 10.1016/j.agrformet.2024.109951
- Accumulated soil moisture deficit better indicates the effect of soil water stress on light use efficiency of grasslands during drought years Z. Zhang et al. 10.1016/j.agrformet.2022.109276
- Comprehensive assessment of four-parameter diurnal land surface temperature cycle models under clear-sky F. Hong et al. 10.1016/j.isprsjprs.2018.06.008
- Seamless Reconstruction of AMSR-E Land Surface Temperature Swath Gaps for China’s Landmass X. Huang et al. 10.1109/TGRS.2023.3335820
- Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system C. Brenner et al. 10.1080/01431161.2017.1280202
- A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements C. Huang et al. 10.1080/01431161.2018.1508920
- Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa? S. Gleixner et al. 10.3390/atmos11090996
- Passive microwave observations of South America and surrounding oceans from Russian Meteor-M No. 2 and Japan GCOM-W1 satellites L. Mitnik et al. 10.1080/01431161.2018.1425569
- Statistical Gap-Filling of SEVIRI Land Surface Temperature A. Dumitrescu et al. 10.3390/rs12091423
- Inter-comparing and improving land surface temperature estimates from passive microwaves over the Jiangsu province of the People’s Republic of China D. Hagan et al. 10.1080/01431161.2019.1580790
21 citations as recorded by crossref.
- Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. 10.5194/hess-24-3431-2020
- Retrieving K-Band Instantaneous Microwave Land Surface Emissivity Based on Passive Microwave Brightness Temperature and Atmospheric Precipitable Water Vapor Data F. Zhou et al. 10.1109/JSTARS.2017.2763167
- Partitioning evapotranspiration based on the total ecosystem conductance fractions of soil, interception, and canopy in different biomes M. Nguyen et al. 10.1016/j.jhydrol.2021.126970
- A two-step deep learning framework for mapping gapless all-weather land surface temperature using thermal infrared and passive microwave data P. Wu et al. 10.1016/j.rse.2022.113070
- Microwave implementation of two-source energy balance approach for estimating evapotranspiration T. Holmes et al. 10.5194/hess-22-1351-2018
- An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques T. Dowling et al. 10.3390/rs13173522
- Towards Estimating Land Evaporation at Field Scales Using GLEAM B. Martens et al. 10.3390/rs10111720
- Evaluation of evapotranspiration using energy-based and water balance hydrological models R. Fitria et al. 10.2166/wcc.2024.499
- Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques P. Wu et al. 10.1109/MGRS.2021.3050782
- Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data G. Thakur et al. 10.1038/s41598-022-12304-3
- Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions D. Chen et al. 10.3390/app12126068
- Predicting the onset of Betula pendula flowering in Poznań (Poland) using remote sensing thermal data P. Bogawski et al. 10.1016/j.scitotenv.2018.12.295
- A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing evapotranspiration from plant to global scales M. Anderson et al. 10.1016/j.agrformet.2024.109951
- Accumulated soil moisture deficit better indicates the effect of soil water stress on light use efficiency of grasslands during drought years Z. Zhang et al. 10.1016/j.agrformet.2022.109276
- Comprehensive assessment of four-parameter diurnal land surface temperature cycle models under clear-sky F. Hong et al. 10.1016/j.isprsjprs.2018.06.008
- Seamless Reconstruction of AMSR-E Land Surface Temperature Swath Gaps for China’s Landmass X. Huang et al. 10.1109/TGRS.2023.3335820
- Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system C. Brenner et al. 10.1080/01431161.2017.1280202
- A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements C. Huang et al. 10.1080/01431161.2018.1508920
- Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa? S. Gleixner et al. 10.3390/atmos11090996
- Passive microwave observations of South America and surrounding oceans from Russian Meteor-M No. 2 and Japan GCOM-W1 satellites L. Mitnik et al. 10.1080/01431161.2018.1425569
- Statistical Gap-Filling of SEVIRI Land Surface Temperature A. Dumitrescu et al. 10.3390/rs12091423
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Latest update: 17 Apr 2024
Short summary
We test the cloud tolerance of two technologies to estimate land surface temperature (LST) from space: microwave (MW) and thermal infrared (TIR). Although TIR has slightly lower errors than MW with ground data under clear-sky conditions, it suffers increasing negative bias as cloud cover increases. In contrast, we find no direct impact of clouds on the accuracy and bias of MW-LST. MW-LST can therefore be used to improve TIR cloud screening and increase sampling in clouded regions.
We test the cloud tolerance of two technologies to estimate land surface temperature (LST) from...
