scholarly journals Changes in subsidence and uplift and the nighttime land surface temperature anomaly related the distance to the earthquake epicenter and the faults using Sentinel and MODIS imageries

2021 ◽  
Author(s):  
Reza Ghorbani Kalkhajeh ◽  
Ali Akbar Jamali
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cloud Cover ◽  
Temperature Anomaly ◽  
Universal Time ◽  
Earthquake Epicenter ◽  
Significant Difference ◽  
Before And After ◽  
Cloud Density

Abstract When an earthquake occurs, the faults of the region usually heat the rocks and soil of the region due to their movements. The purpose of this study was to analyze the uplift, subsidence, cloud cover and changes in nighttime land surface temperature (nLST) anomalies around faults and the earthquake epicenter in Kermanshah, Iran (date and time of earthquake 12 November, 2017 at 18:18 Coordinated Universal Time (UTC) and at 21:48 Iranian time(. Heat changes were investigated by considering the effect of other cooling factors such as vegetation (EVI), land altitude and soil moisture, rainfall and water areas. Using the MODIS sensor product, the amount of cloud cover and cooling factors were obtained. Using sentinel 1A the amount of earth uplift and subsidence were calculated. The results showed that using statistical analysis, a significant difference was observed in the nighttime land surface temperature around the faults and around the uplift and subsidence on the night of the accident, before and after night of earthquake. However, there was no significant difference between nighttime temperature and changes in the rate of spatial variation of cooling factors. It was found that the earthquake caused an increase in temperature at the fault and earthquake epicenter location. It also causes changes in height such as uplift and subsidence. Cloud cover situation showed before the earthquake, the cloud density was high and after the earthquake, the cloud density decreased. Crises managers can consider these results for monitoring metropolices for more readiness before earthquake accordance.

scholarly journals Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)

Atmosphere ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 334 ◽  
Author(s):  
Hamid Ghafarian Malamiri ◽  
Iman Rousta ◽  
Haraldur Olafsson ◽  
Hadi Zare ◽  
Hao Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Spectrum Analysis ◽  
Land Surface ◽  
Cloud Cover ◽  
Singular Spectrum Analysis ◽  
Series Data ◽  
Study Region ◽  
Singular Spectrum

Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.

scholarly journals Urban Land Surface Temperature Monitoring and Surface Thermal Runoff Pollution Evaluation Using UAV Thermal Remote Sensing Technology

2021 ◽  
Vol 13 (20) ◽  
pp. 11203
Author(s):  
Shanshan Xu ◽  
Kun Yang ◽  
Yuanting Xu ◽  
Yanhui Zhu ◽  
Yi Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Temperature ◽  
Water Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Surface ◽  
Surface Water Temperature ◽  
Before And After

With the continuous advancement of urbanization, the impervious surface expands. Urbanization has changed the structure of the natural land surface and led to the intensification of the urban heat island (UHI) effect. This will affect the surface runoff temperature, which, in turn, will affect the surface water temperature of urban lakes. This study will use UAS TIR (un-manned aerial system thermal infrared radiance) remote sensing and in situ observation technology to monitor the urban space surface temperature and thermal runoff in Kunming, Yunnan, in summer; explore the feasibility of UAS TIR remote sensing to continuously observe urban surface temperature during day and night; and analyze thermal runoff pollution. The results of the study show that the difference between UAS TIR LSTs and in situ LSTs (in situ air temperature 10 cm above the ground.) varies with the type of land covers. Urban surface thermal runoff has varying degrees of impact on water bodies. Based on the influence of physical factors such as vegetation and buildings and meteorological factors such as solar radiation, the RMSE between UAS LSTs and in situ LSTs varies from 1 to 5 °C. Land cover types such as pervious bricks, asphalt, and cement usually show higher RMSE values. Before and after rainfall, the in situ data of the lake surface water temperature (LSWT) showed a phenomenon of first falling and then rising. The linear regression analysis results show that the R2 of the daytime model is 0.92, which has high consistency; the average R2 at night is 0.38; the averages R2 before and after rainfall are 0.50 and 0.83, respectively; and the average RMSE is 1.94 °C. Observational data shows that thermal runoff quickly reaches thermal equilibrium with the land surface temperature about 30 min after rainfall. The thermal runoff around the lake has a certain warming effect on LSWT.

scholarly journals Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days

2020 ◽  
Vol 12 (9) ◽  
pp. 1398 ◽  
Author(s):  
Cheolhee Yoo ◽  
Jungho Im ◽  
Dongjin Cho ◽  
Naoto Yokoya ◽  
Junshi Xia ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Mean Squared Error ◽  
Surface Fluxes ◽  
Clear Sky ◽  
Cloudy Weather ◽  
Significant Difference ◽  
Better Than

Land surface temperature (LST) is used as a critical indicator for various environmental issues because it links land surface fluxes with the surface atmosphere. Moderate-resolution imaging spectroradiometers (MODIS) 1 km LSTs have been widely utilized but have the serious limitation of not being provided under cloudy weather conditions. In this study, we propose two schemes to estimate all-weather 1 km Aqua MODIS daytime (1:30 p.m.) and nighttime (1:30 a.m.) LSTs in South Korea for humid summer days. Scheme 1 (S1) is a two-step approach that first estimates 10 km LSTs and then conducts the spatial downscaling of LSTs from 10 km to 1 km. Scheme 2 (S2), a one-step algorithm, directly estimates the 1 km all-weather LSTs. Eight advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures, three MODIS-based annual cycle parameters, and six auxiliary variables were used for the LST estimation based on random forest machine learning. To confirm the effectiveness of each scheme, we have performed different validation experiments using clear-sky MODIS LSTs. Moreover, we have validated all-weather LSTs using bias-corrected LSTs from 10 in situ stations. In clear-sky daytime, the performance of S2 was better than S1. However, in cloudy sky daytime, S1 simulated low LSTs better than S2, with an average root mean squared error (RMSE) of 2.6 °C compared to an average RMSE of 3.8 °C over 10 stations. At nighttime, S1 and S2 demonstrated no significant difference in performance both under clear and cloudy sky conditions. When the two schemes were combined, the proposed all-weather LSTs resulted in an average R2 of 0.82 and 0.74 and with RMSE of 2.5 °C and 1.4 °C for daytime and nighttime, respectively, compared to the in situ data. This paper demonstrates the ability of the two different schemes to produce all-weather dynamic LSTs. The strategy proposed in this study can improve the applicability of LSTs in a variety of research and practical fields, particularly for areas that are very frequently covered with clouds.

scholarly journals Reconstruction of the Cloud-Free Time Series Satellite Observations of Land Surface Temperature (LST) Using Singular Spectrum Analysis (SSA)

2018 ◽  
Author(s):  
Hamid Reza Ghafarian Malamiri ◽  
Iman Rousta ◽  
Haraldur Olafsson ◽  
Hadi Zare ◽  
Hao Zhang
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Spectrum Analysis ◽  
Land Surface ◽  
Cloud Cover ◽  
Singular Spectrum Analysis ◽  
Series Data ◽  
Singular Spectrum

Land Surface Temperature (LST) is a basic parameter in energy exchange between the land and atmosphere and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. LST time series data have usually deficient, missing and unacceptable data caused by the presence of clouds in images, presence of dust in atmosphere and sensor failure. In this study, Singular Spectrum Analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of MODIS with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran and Turkmenistan and Caspian Sea. In this study, MODIS LST sensor (MOD11A1) was used during 2015 with 1×1 Km spatial resolution and day/night LST data (daily temporal resolution). The results of the data quality showed that cloud cover caused 36.37% of missing data in the studied time series with 730 day/night LST images. Further, the results of SSA algorithm in reconstruction of LST images indicated the Root Mean Square Error (RMSE) of 2.95 K between the original and reconstructed data in LST time series in the study region. In general, the findings showed that SSA algorithm using spatio-temporal interpolation in LST time series can be effectively used to resolve the problem of missing data caused by cloud cover.

scholarly journals SPATIAL AND TEMPORAL VARIATION OF LAND SURFACE TEMPERATURE IN FUJIAN PROVINCE FROM 2001 TO 2015

2018 ◽  
Vol XLII-3 ◽  
pp. 971-974
Author(s):  
Y. Li ◽  
X. Wang ◽  
Z. Ding
Keyword(s):  
Spatial Distribution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Cloud Cover ◽  
Spatial And Temporal Variation ◽  
Filter Method ◽  
Fujian Province ◽  
Slope Analysis

Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Fujian province were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the Savitzky-Golay (S-G) filter method was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 27 weather stations were employed to evaluate the fitting performance of the S-G filter method. Results indicate that S-G can effectively fit the LST time series and remove the influence of cloud cover. Based on the S-G-derived result, Spatial and temporal Variations of LST in Fujian province from 2001 to 2015 are analysed through slope analysis. The results show that: 1) the spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area and the average of LST was much higher in the east than in the west. 2) The annual mean temperature of LST declines slightly among 15 years in Fujian. 3) Slope analysis reflects the spatial distribution characteristics of LST changing trend in Fujian.Improvement areas of LST are mainly concentrated in the urban areas of Fujian, especially in the eastern urban areas. Apparent descent areas are mainly distributed in the area of Zhangzhou and eastern mountain area.

scholarly journals Worldwide continuous gap-filled MODIS land surface temperature dataset

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Shilo Shiff ◽  
David Helman ◽  
Itamar M. Lensky
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Anomaly ◽  
Eastern Mediterranean ◽  
Absolute Error ◽  
Google Earth ◽  
Climate Forecast System ◽  
Mean Values ◽  
Web App

AbstractSatellite land surface temperature (LST) is vital for climatological and environmental studies. However, LST datasets are not continuous in time and space mainly due to cloud cover. Here we combine LST with Climate Forecast System Version 2 (CFSv2) modeled temperatures to derive a continuous gap filled global LST dataset at a spatial resolution of 1 km. Temporal Fourier analysis is used to derive the seasonality (climatology) on a pixel-by-pixel basis, for LST and CFSv2 temperatures. Gaps are filled by adding the CFSv2 temperature anomaly to climatological LST. The accuracy is evaluated in nine regions across the globe using cloud-free LST (mean values: R2 = 0.93, Root Mean Square Error (RMSE) = 2.7 °C, Mean Absolute Error (MAE) = 2.1 °C). The provided dataset contains day, night, and daily mean LST for the Eastern Mediterranean. We provide a Google Earth Engine code and a web app that generates gap filled LST in any part of the world, alongside a pixel-based evaluation of the data in terms of MAE, RMSE and Pearson’s r.

Reconciling different approaches to quantifying surface cooling induced by afforestation in China using satellite observations

2021 ◽  
Author(s):  
Huanhuan Wang ◽  
Chao Yue ◽  
Sebastiaan Luyssaert ◽  
Jie Zhao ◽  
Hongfei Zhao
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Forest Cover ◽  
Satellite Observations ◽  
Forest Cover Change ◽  
Surface Cooling ◽  
Surface Temperature Change ◽  
Significant Difference

<p>Forest cover change can cause strong local biophysical feedbacks on climate. Satellite observations of land surface temperature (T) and land cover distribution or forest cover change have been widely used to examine the effects of afforestation/deforestation on local surface temperature change (ΔT). However, different approaches were used by previous analyses to quantifying ΔT, and it remains unclear whether results of ΔT by these approaches are comparable. We identified three influential approaches to quantifying ΔT used by previous studies, namely the actual ΔT resulting from actual changes in forest coverage over time and accounting for changes in background climate (ΔT<sub>a</sub> proposed by Alkama and Cescatti, 2016), potential ΔT by hypothesizing potential shifts between non-forest and forest at given native spatial resolutions of satellite products (ΔT<sub>p1</sub> by Li et al., 2015), and potential ΔT, but using the singular value decomposition technique to derive ΔT by hypothesizing a shift between a 100% complete non-forest and 100% forest (ΔT<sub>p2</sub> by Duveiller et al., 2019). China realized large-scale afforestation making it a suitable test case to compare satellite-based approaches for estimating ΔT following afforestation. We hypothesize that (1) ΔT<sub>a</sub> depends on the fraction of ground area that’s been afforested (F<sub>aff</sub>). (2) The relative magnitude between different approaches should be: ΔT<sub>a</sub> < ΔT<sub>p1</sub> < ΔT<sub>p2</sub>. (3) When ΔT<sub>a</sub> is extended to a hypothetical case that F<sub>aff</sub> reaches 100%, it should be comparable to ΔT<sub>p1</sub> or ΔT<sub>p2</sub>. We used multiple satellite observation products to test these hypotheses. The results show that the magnitude of actual daytime surface cooling by afforestation (ΔT<sub>a</sub>) increases with F<sub>aff</sub>, and is significantly lower than ΔT<sub>p1</sub> and ΔT<sub>p2</sub>. But no significant difference was found between ΔT<sub>p1</sub> and ΔT<sub>p2</sub>. A linear regression model established between ΔT<sub>a</sub> and F<sub>aff</sub> extends the ΔT<sub>a</sub>, when F<sub>aff</sub> reaches 100%, to a comparable magnitude than ΔT<sub>p1</sub> and ΔT<sub>p2</sub>. Our study thus highlights the importance to consider the actual surface cooling impact by afforestation projects in contrast to the potential effects, and provides a first study to reconcile different approaches to quantify the land surface temperature change due to afforestation.</p>

A review on remotely sensed land surface temperature anomaly as an earthquake precursor

2017 ◽  
Vol 63 ◽  
pp. 158-166 ◽  
Author(s):  
Anshuman Bhardwaj ◽  
Shaktiman Singh ◽  
Lydia Sam ◽  
P.K. Joshi ◽  
Akanksha Bhardwaj ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Anomaly ◽  
Remotely Sensed ◽  
Earthquake Precursor
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