scholarly journals A New Algorithm for the Characterization of Thermal Infrared Anomalies in Tectonic Activities

2018 ◽  
Vol 10 (12) ◽  
pp. 1941 ◽  
Author(s):  
Dongmei Song ◽  
Ruihuan Xie ◽  
Lin Zang ◽  
Jingyuan Yin ◽  
Kai Qin ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Earthquake ◽  
Thermal Infrared ◽  
Tibet Plateau ◽  
Seismogenic Zone ◽  
The Tibetan Plateau ◽  
Sensing Technology ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Movement Trace ◽  
Thermal Infrared Anomalies

The monitoring of earthquake events is a very important and challenging task. Remote sensing technology has been found to strengthen the monitoring abilities of the Earth’s surface at a macroscopic scale. Therefore, it has proven to be very helpful in the exploration of some important anomalies, which cannot be seen in a small scope. Previously, thermal infrared (TIR) anomalies have been widely regarded as indications of early warnings for earthquake events. At the present time, some classic algorithms exist, which have been developed to extract TIR anomaly signals before the onset of large earthquakes. In this research study, with the aim of addressing some of the deficiencies of the classic algorithm, which is currently used for noise filtering during the process of extracting tectonic TIR anomalies signals, a novel TTIA (tectonic thermal infrared anomalies) algorithm was proposed to characterize earthquake TIR anomalies using the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature dataset (MOD11A2). Then, for the purpose of determining the rule of the TIR anomalies prior to large earthquake events, the Qinghai-Tibet Plateau in China was chosen as the study area. It is known that tectonic movements are very active in the study area, and major earthquakes often occur. The following conclusions were obtained from the experimental results of this study: (1) The TIR anomalies extracted using the proposed TTIA method showed a very obvious spatial distribution characteristic along the tectonic faults, which indicated that the proposed algorithm had distinctive advantages in removing or weakening the disturbances of the atectonic TIR anomalies signals; (2) The seismogenic zone was observed to be a more effective observation scale for assisting in the deeper understanding and investigations of the mid- and short-term seismogenic and crust stress change processes; (3) The movement trace of the centroids of the TIR anomalies on the Tibetan Plateau three years prior to earthquake events contributed to improved judgments of dangerous regions where major earthquakes may occur in the future.

A Spatial and Temporal Continuum Remotely Sensed Soil Moisture Dataset of the Tibet Plateau From 2002 to 2015

2020 ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Xi Chen
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
European Space Agency ◽  
Remotely Sensed ◽  
General Regression Neural Network ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Space Agency ◽  
Spatio Temporal

<p><strong>Abstract</strong>:</p><p>Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002–2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25° resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R<sup>2</sup> larger than 0.65, and RMSE less than 0.08 cm<sup>3</sup> cm<sup>-3</sup> and Bias less than 0.07 cm<sup>3</sup> cm<sup>-3 </sup>at 0.25° and 1° spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial and temporal characteristics and trend of SM over TP were analyzed based on this dataset.</p><p><strong>Keywords: </strong>Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau</p>

scholarly journals Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

2016 ◽  
Author(s):  
Hongbo Zhang ◽  
Fan Zhang ◽  
Guoqing Zhang ◽  
Xiaobo He ◽  
Lide Tian
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Estimation Accuracy ◽  
Daily Maximum ◽  
The Tibetan Plateau ◽  
Cloud Detection ◽  
Air Temperatures ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data have played a significant role in estimating the air temperature (Tair) due to the sparseness of ground measurements, especially for remote mountainous areas. Generally, two types of air temperatures are studied including daily maximum (Tmax) and minimum (Tmin) air temperatures. MODIS daytime and nighttime LST are often used as proxies for estimating Tmax and Tmin, respectively. The Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %). The presence of clouds can affect the relationship between Tair and LST and can further affect the estimation accuracies. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from over the TP. Comparisons made between in-situ cloudiness observations and MODIS claimed clear-sky records show that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Our validation of the MODIS LST values under different cloudiness constraining conditions shows that the accuracy of MODIS nighttime LST is severely affected by undetected clouds. Large errors introduced by undetected clouds are found to significantly affect the Tmin estimations based on nighttime LST through cloud effect tests. However, clouds are mainly found to affect Tmax estimation by affecting the essential relationship between Tmax and daytime LST. The obviously larger errors of Tmax estimation than those of Tmin could be attributed to larger MODIS daytime LST errors resulting from higher degrees of daytime LST heterogeneity within MODIS pixel than those of nighttime LST. Constraining all four MODIS observations per day to non-cloudy observations can efficiently screen samples to build a strong fit of Tmin estimation using MODIS nighttime LST. The present study reveals the effects of clouds on Tair estimation through MODIS LST and will thus help improve the estimation accuracy levels while alleviating the problems associated with severe data sparseness over the TP.

scholarly journals A New Map of the Permafrost Distribution on the Tibetan Plateau

2016 ◽  
Author(s):  
Defu Zou ◽  
Lin Zhao ◽  
Yu Sheng ◽  
Ji Chen ◽  
Guojie Hu ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Water Resource Management ◽  
Climate Modelling ◽  
The Tibetan Plateau ◽  
Freezing And Thawing ◽  
Frozen Ground ◽  
Data Set ◽  
Distribution Maps ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The Tibetan Plateau (TP) possesses the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. A detailed database of the distribution and characteristics of permafrost is crucial for engineering planning, water resource management, ecosystem protection, climate modelling, and carbon cycle research. Although some permafrost distribution maps have been compiled in previous studies and have been proven to be very useful, due to the limited data source, ambiguous criteria, little validation, and the deficiency of high-quality spatial datasets, there is high uncertainty in the mapping of the permafrost distribution on the TP. In this paper, a new permafrost map was generated mostly based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs) and validated by various ground-based datasets. The soil thermal properties of five soil types across the TP were estimated according to an empirical equation and in situ observed soil properties (moisture content and bulk density). The Temperature at the Top of Permafrost (TTOP) model was applied to simulate the permafrost distribution. The results show that permafrost, seasonally frozen ground, and unfrozen ground covered areas of 1.06×106 km2 (40 %), 1.46×106 km2 (56 %), and 0.03×106 km2 (1 %), respectively, excluding glaciers and lakes. The ground-based observations of the permafrost distribution across the five investigated regions (IRs, located in the transition zones of the permafrost and seasonally frozen ground) and three highway transects (across the entire permafrost regions from north to south) have been used to validate the model. The result of the validation shows that the kappa coefficient varies from 0.38 to 0.78 with an average of 0.57 at the five IRs and 0.62 to 0.74 with an average of 0.68 within the three transects. Compared with two maps compiled in 1996 and 2006 (kappa coefficients in average 0.06 and 0.35 in five IRs, 0.34 and 0.50 within three transects, respectively), the result of the TTOP modelling shows greater accuracy, especially in identifying thawing regions. Overall, the results provide much more detailed maps of the permafrost distribution and could be a promising basic data set for further research on permafrost on the Tibetan Plateau.

scholarly journals A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Hongjie Xie ◽  
Wei Wan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
General Regression Neural Network ◽  
Tibet Plateau ◽  
Sufficient Information ◽  
The Tibetan Plateau ◽  
Significant Information ◽  
Spatio Temporal ◽  
General Regression

Abstract Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002–2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP.

scholarly journals A Statistical Analysis of TIR Anomalies extracted by RST in Relation with Earthquake in Sichuan Area with Use of MODIS LST Data

2018 ◽  
Author(s):  
Ying Zhang ◽  
Qingyan Meng
Keyword(s):  
Statistical Analysis ◽  
Seismic Hazard ◽  
Earthquake Prediction ◽  
Land Surface ◽  
Thermal Infrared ◽  
Robust Estimator ◽  
Earthquake Catalog ◽  
Large Area ◽  
Prediction Ability ◽  
Thermal Infrared Anomalies

Abstract. There is a long history for research of earthquake prediction, but weakness of traditional approaches to study seismic hazard have been more and more evident. Remote sensing and earth observation technology, which is a new method that can instantly acquire a large area of abnormal information caused by earthquakes, is believed to be the key to the breakthrough of the bottleneck in the study of earthquake prediction. A multi-parametric approach seems, instead, to be the most promising approach in order to increase reliability and precision of short-term seismic hazard forecast, and Thermal Infrared (TIR) anomaly is an important part of the earthquake precursors. Though many scientists have studied the correlation among TIR anomalies identified by the Robust Satellite Techniques (RST) methodology and single earthquake, there is few study to extract the TIR anomalies in long period and large study area. Moreover, a statistical analysis of TIR anomalies in relation with earthquake is needed to determine whether there is the existence of TIR anomalies before earthquake. In this paper, a refined RST data analysis and Robust Estimator of TIR Anomalies (RETIRA) index were used to extract the TIR anomalies from 2002 to 2018 in Sichuan area with use of Moderate-resolution Imaging Spectro-radiometer (MODIS) Land Surface Temperature (LST), and the earthquake catalog were also used to study the correlation between TIR anomalies and occurrences of earthquake. Most of the thermal infrared anomalies correspond to earthquakes, and statistical methods are used to prove that there is a correlation between the extracted thermal infrared anomalies and earthquakes. And this is the first time to evaluate earthquakes prediction ability with use of PPV, FDR, TPR and FNR, the statistical result shows that the prediction ability of RST in Sichuan area is limited.

scholarly journals Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

2016 ◽  
Vol 16 (21) ◽  
pp. 13681-13696 ◽  
Author(s):  
Hongbo Zhang ◽  
Fan Zhang ◽  
Guoqing Zhang ◽  
Xiaobo He ◽  
Lide Tian
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Estimation Accuracy ◽  
Daily Maximum ◽  
The Tibetan Plateau ◽  
Cloud Detection ◽  
Air Temperatures ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (Tmax) and minimum (Tmin) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (Tair) estimation accuracy. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the Tmin estimation accuracy. However, for Tmax estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between Tmax and daytime LST. The errors of Tmax estimation are obviously larger than those of Tmin and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate Tmin estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on Tmax and Tmin estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the Tair estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

Estimating daily 1-km surface air temperature from satellite all-weather land surface temperature over the Tibetan Plateau

2020 ◽  
Author(s):  
Xin Wen ◽  
Ji Zhou ◽  
Xiaodong Zhang ◽  
Jin Ma
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Climate Warming ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Thermal Infrared ◽  
The Tibetan Plateau

<p>Over the past several decades, global climate change, particularly the rising temperature has caused public concerns. In the context of climate warming, many environmental and water problems such as decreasing runoff, shrinking glaciers and permafrost, vegetation degradation and desertification can be attributed to rapid climate change. Surface air temperature (SAT) plays a key role in land-atmospheric interactions and is an important parameter for climate change studies. Traditional SAT data are collected by ground meteorological observation. Nevertheless, such traditional measurements at ground stations cannot capture the spatial variations of SAT, especially over complicated areas such as the Tibetan Plateau, where meteorological stations are with large elevation variability and unreasonable spatial distribution. In contrast, satellite remote sensing provides an direct observation of land surface temperature (LST) and, thus, also provides an possible way to obtain SAT since LST and SAT are generally closely related to each other. The scientific communities have developed various methods to estimate SAT from LST through statistical or physical models. The widely used satellite LST, however, is derived from satellite thermal infrared remote sensing and thus, significantly affected by the clouds.</p><p>In this study, we report an examination of the estimation of daily 1-km SAT from the all-weather satellite LST over the Tibetan Plateau. The estimation of SAT is based on a noval method that dynamicall integrates the newly published 1-km all-weather LST data by merging satellite thermal infrared and microwave remote sensing observations based on the random forest. The matchups of the ground measured SAT at stations and the corresponding all-weather LST were separated into the training set and valiation set. In addition, independent SAT measured at experimental ground sites were used to evaluate the SAT method. Results indicate that reasonably integrating multiple LST terms provides daily average all-weather SAT with satisfactory accuracies over the Tibetan Plateau. The estimated SAT based on the proposed method has ignorable systematic error and low root-mean squared error when validated with ground measured SAT under all-weather conditions. Further comparison demonstrates that the SAT estimate agree well with other SAT estimated from satellite thermal infrared LST under cloud-free condition. In addition, the SAT method has the potential to be generalized and extended to various complicated areas. With this method, the daily 1-km SAT for the entire Tibetan Plateau from 2003 to 2018 were produced. This dataset is of great value to examine recent climate warming trend and the land-atmospheirc interactions in the entire Tibetan Plateau.</p>

scholarly journals Performance of GLASS and MODIS Satellite Albedo Products in Diagnosing Albedo Variations during Different Time Scales and Special Weather Conditions in the Tibetan Plateau

2020 ◽  
Vol 12 (15) ◽  
pp. 2456
Author(s):  
Yingying An ◽  
Xianhong Meng ◽  
Lin Zhao ◽  
Zhaoguo Li ◽  
Shaoying Wang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Surface Albedo ◽  
Weather Conditions ◽  
Glass Product ◽  
The Tibetan Plateau ◽  
Actual Surface ◽  
Multiple Observations ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Surface albedo is a crucial parameter in accurately and quantitatively estimating energy and water budget on the Tibetan Plateau (TP) and is also one of the largest radiative uncertainties in land surface modelling attempts. Based on an 8-year ground-based observation of the surface albedo over typical alpine meadows at Maqu and Maduo sites in the eastern TP, the performance of surface albedo products of Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) in describing albedo variations at daily, 8-day, seasonal timescales, and during different special weather conditions were analyzed. Compared with the ground-based observation in Maqu, the 8-day albedo products from GLASS and MCD43B3 present maximum negative biases of −0.030 and −0.027 at Maqu, respectively. The black-sky albedo (BSA) of GLASS product coincides well with the ground-based observation in Maduo, with root mean square error (RMSE) of 0.092 and correlation coefficient (R) of 0.833, whereas that of MCD43B3 had an RMSE of 0.072 and R of 0.752. However, they are underestimated when the albedo is greater than 0.4. At the seasonal timescale, the BSA of GLASS and MCD43B3 underestimated the ground-based observation of Maqu by 0.015 in summer, while their white-sky albedo (WSA) are slightly overestimated and closer to the ground-based observation. In daily timescale, the response of surface albedo to soil moisture is different in semihumid and semiarid areas in summer. For both sites, the blue-sky-albedo of MCD43A3 has better agreement with the ground-based observation than GLASS and MCD43B3, as it improves the temporal resolution and calculates the albedo by weighting multiple observations within 16 days to be closer to the actual surface. However, even MCD43A3 could not capture the slowdown processes of albedo changes resulted by small snowfall processes or the snow aging due to cloud cover and inversion algorithms.

scholarly journals Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau

2021 ◽  
Vol 13 (22) ◽  
pp. 4574
Author(s):  
Yanmei Zhong ◽  
Lingkui Meng ◽  
Zushuai Wei ◽  
Jian Yang ◽  
Weiwei Song ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
The Tibetan Plateau ◽  
Auxiliary Variables ◽  
Clear Sky ◽  
Validation Experiment

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.

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