Skin temperature from the Thermal Infrared Sounder IASI

Abstract. Skin temperature (Tskin) derived from infrared sensors on board satellites provides a continuous view of Earth’s surface day and night and allows for the monitoring of global temperature changes relevant for climate trends. Tskin from the Infrared Atmospheric Sounding Interferometer (IASI) has not been properly exploited to date to assess its long-term spatio-temporal variability and no current homogenous Tskin record from IASI exists. In this study, we present a fast retrieval method of Tskin based on an artificial neural network from a set of IASI channels selected using the information theory/entropy reduction technique. We compare and validate our IASI Tskin product with that from EUMETSAT Level 2, ECMWF Reanalysis ERA5, SEVIRI land-surface temperature products, as well as ground measurements. Our results show good correlation between the IASI neural network product and the datasets used for validation, with a standard deviation between 1 and 4 °C. This method can be applied to other infrared measurements, and allows for the construction of a robust Tskin dataset, making it suitable for trend analysis.

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Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Remote Sensing ◽

10.3390/rs12172777 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2777

Author(s):

Sarah Safieddine ◽

Ana Claudia Parracho ◽

Maya George ◽

Filipe Aires ◽

Victor Pellet ◽

...

Keyword(s):

Skin Temperature ◽

Climate Trends ◽

Remote Sensors ◽

Ground Station ◽

Entropy Reduction ◽

Surface Skin Temperature ◽

Artificial Neural ◽

Spatio Temporal ◽

Artificial Neural Network Ann ◽

Station Location

Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis.

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Cities Exacerbate Climate Warming

Urban Science ◽

10.3390/urbansci5010027 ◽

2021 ◽

Vol 5 (1) ◽

pp. 27

Author(s):

Lahouari Bounoua ◽

Kurtis Thome ◽

Joseph Nigro

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Climate Models ◽

Warming Trend ◽

Climate Mitigation ◽

Temperature Changes ◽

Surface Warming ◽

The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

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CONSTRUCTION AND ANALYSIS OF LONG-TERM SURFACE TEMPERATURE DATASET IN FUJIAN PROVINCE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-1223-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 1223-1227 ◽

Cited By ~ 1

Author(s):

W. E. Li ◽

X. Q. Wang ◽

H. Su

Keyword(s):

Time Series ◽

Surface Temperature ◽

Land Surface ◽

Rapid Development ◽

Temporal Distribution ◽

Heat Fluxes ◽

Urban Region ◽

Fujian Province ◽

Spatio Temporal

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

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Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran

Land Use Policy ◽

10.1016/j.landusepol.2017.11.023 ◽

2018 ◽

Vol 71 ◽

pp. 459-469 ◽

Cited By ~ 22

Author(s):

Amin Tayyebi ◽

Hossein Shafizadeh-Moghadam ◽

Amir H. Tayyebi

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temporal Patterns ◽

Rapid Urbanization ◽

Spatio Temporal

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Neural Network Structure for Spatio-Temporal Long-Term Memory

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2012.2191419 ◽

2012 ◽

Vol 23 (6) ◽

pp. 971-983 ◽

Cited By ~ 25

Author(s):

V. A. Nguyen ◽

J. A. Starzyk ◽

Wooi-Boon Goh ◽

D. Jachyra

Keyword(s):

Neural Network ◽

Network Structure ◽

Long Term Memory ◽

Term Memory ◽

Neural Network Structure ◽

Spatio Temporal

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A global long term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

10.5194/essd-2021-250 ◽

2021 ◽

Author(s):

Jianglei Xu ◽

Shunlin Liang ◽

Bo Jiang

Keyword(s):

Neural Network ◽

High Resolution ◽

Convolutional Neural Network ◽

Satellite Data ◽

Land Surface ◽

Radiant Energy ◽

Surface Radiation ◽

Radiation Budget ◽

Surface Radiation Budget

Abstract. The surface radiation budget, also known as all-wave net radiation (Rn), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate Rn, but existing satellite products have coarse spatial resolutions and limited temporal coverage. In this study, a point-surface matching estimation (PSME) method is proposed to estimate surface Rn using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05°) long-term (1981–2019) Rn product was subsequently generated from Advanced Very High-Resolution Radiometer (AVHRR) data. Specifically, the RCNN was employed to establish a nonlinear relationship between globally distributed ground measurements from 537 sites and AVHRR top of atmosphere (TOA) observations. Extended triplet collocation (ETC) technology was applied to address the spatial scale mismatch issue resulting from the low spatial support of ground measurements within the AVHRR footprint by selecting reliable sites for model training. The overall independent validation results show that the generated AVHRR Rn product is highly accurate, with R2, root-mean-square error (RMSE), and bias of 0.84, 26.66 Wm−2 (31.66 %), and 1.59 Wm−2 (1.89 %), respectively. Inter-comparisons with three other Rn products, i.e., the 5 km Global Land Surface Satellite (GLASS), the 1° Clouds and the Earth's Radiant Energy System (CERES), and the 0.5° × 0.625° Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), illustrate that our AVHRR Rn retrievals have the best accuracy under all of the considered surface and atmospheric conditions, especially thick cloud or hazy conditions. The spatiotemporal analyses of these four Rn datasets indicate that the AVHRR Rn product reasonably replicates the spatial pattern and temporal evolution trends of Rn observations. This dataset is freely available at https://doi.org/10.5281/zenodo.5509854 for 1981–2019 (Xu et al., 2021).

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