Since the beginning of the 21st century,major earthquakes have frequently occurred worldwide.To explore the impact of astronomical factors on earthquakes,in this study,the statistical analysis method of correlation is...Since the beginning of the 21st century,major earthquakes have frequently occurred worldwide.To explore the impact of astronomical factors on earthquakes,in this study,the statistical analysis method of correlation is used to systematically analyze the effects of astronomical factors,such as solar activity,Earth’s rotation,lunar declination angle,celestial tidal force,and other phenomena on M≥8 global earthquakes at the beginning of the 21st century.With regard to solar activity,this study focuses on the analysis of the 11-year and century cycles of solar activity.The causal relationship of the Earth’s rotation is not obvious in this work and previous works;in contrast,the valley period of the solar activity century cycle may be an important astronomical factor leading to the frequent occurrence of global earthquakes at the beginning of the 21st century.This topic warrants further study.展开更多
Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time ser...Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature(ST) measured at different depths(0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature(LST) and normalized difference vegetation index(NDVI) derived from AQUA/TERRA MODIS data, and solar declination(Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination(R^2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R^2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors(RMSEs) and R^2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.展开更多
文摘Since the beginning of the 21st century,major earthquakes have frequently occurred worldwide.To explore the impact of astronomical factors on earthquakes,in this study,the statistical analysis method of correlation is used to systematically analyze the effects of astronomical factors,such as solar activity,Earth’s rotation,lunar declination angle,celestial tidal force,and other phenomena on M≥8 global earthquakes at the beginning of the 21st century.With regard to solar activity,this study focuses on the analysis of the 11-year and century cycles of solar activity.The causal relationship of the Earth’s rotation is not obvious in this work and previous works;in contrast,the valley period of the solar activity century cycle may be an important astronomical factor leading to the frequent occurrence of global earthquakes at the beginning of the 21st century.This topic warrants further study.
基金supported by the National Natural Science Foundation of China (41671418 and 41371326)the Science and Technology Facilities Council of UK-Newton Agritech Programme (Sentinels of Wheat)the Fundamental Research Funds for the Central Universities, China (2019TC117)
文摘Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature(ST) measured at different depths(0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature(LST) and normalized difference vegetation index(NDVI) derived from AQUA/TERRA MODIS data, and solar declination(Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination(R^2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R^2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors(RMSEs) and R^2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.
