If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, statistical methods for environmental pollution monitoring always becomes the most wanted book. Many people are absolutely searching for this book. It means that many love to read this kind of book.

Statistical Methods For Environmental Pollution Monitoring

Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications

Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most‐used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR‐based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR‐based LST and the state‐of‐the‐art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges.

Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change.

Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations

Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement

Land surface temperature (LST) and its annual or inter-annual variations play an important role in understanding global climate change, urban heat island, and the process of land-atmosphere energy exchange Many annual temperature cycle (ATC) models [ie, ATC with three or five parameters (ACP3 or ACP5)] have been proposed to analyze the annual variations of LST in the past decades In this study, two year-to-year continuous and derivable models (YYCD_ACP3 and YYCD_ACP5 models) were proposed to model several years of ATCs The fitting results of the YYCD_ACP3 model with global Aqua/MODIS daytime LSTs from 2014 to 2018 show that the YYCD_ACP3 model achieved a good performance in fitting the time-series LSTs with an overall normalized root-mean-square error (NRMSE) of 021, coefficient of determination (R
 2
) of 074, and refined index of agreement ($d$ ) of 085 In addition, the modeling results of ten representative samples covering different climatic conditions and land cover worldwide show that, except for two sites located in tropical and Antarctic, the YYCD_ACP3 model could show a good performance with R
 2
 greater than 06 Although the ACP3 model shows similar performance to the YYCD_ACP3 model, the fitting curve of the YYCD_ACP3 model is continuous and smooth for describing the interannual variations of LST When the LSTs of 2014-2018 are fitted as a whole by using both models, the YYCD_ACP3 model shows a slightly better performance than that of the ACP3 model The application of the YYCD_ACP3 model with the global MODIS LSTs from 2003 to 2018 indicates that the results of the YYCD_ACP3 model have the potential to reveal the interannual variations of LST Therefore, we conclude that the YYCD models are valuable for modeling the variations of LST over several years and can be widely applied

/pdf/modeling-year-to-year-variations-of-clear-sky-land-surface-2y6g76scj7.pdf

Modeling Year-to-Year Variations of Clear-Sky Land Surface Temperature Using Aqua/MODIS Data

Thermal radiation of water is polarized, and thermal infrared polarization characteristics are investigated to detect thermal infrared polarized remote sensing and theoretically support the interpretation of water remote-sensing processes Quantitative analysis of the influence of detection angle, azimuth angle, wave band and temperature as well as their interactions in various polarized conditions on the thermal infrared polarized characteristics of water is conducted via orthogonal experiment design and spectrum analysis Results show that the polarized brightness temperature of water decreases with the increase in detection angle and increases with the increase in temperature Parabolic distribution occurs between the polarized brightness temperature and azimuth angle and the peak of the parabola is located near the azimuth angle of 180° The polarized brightness temperature in the four wave bands is different and presents distinct characteristics in various detection conditions The interactio

Quantitative analysis of influencing factors and interactions of thermal infrared polarization of water

Land surface temperature (LST) is a comprehensive embodiment of surface energy balance and land surface processes. The spatial and temporal variation of LST is of great significance for studying surface characteristics and climate change. In this study, the spatiotemporal variations of LST in China from 2003 to 2018 is examined by using the continuous and derivable annual temperature cycle model. The trends of the annual mean and annual amplitude of LST is detected using the Mann–Kendall test and Theil–Sen estimator. In addition, we have further revealed the correlation between normalized difference vegetation index (NDVI) and LST in different land cover types. The results show that the annual mean LST presents a spatial distribution pattern of high values in the southern regions and low values in the northern regions and that the factors of altitude and land cover type also affect the LST's spatial distribution. The annual amplitude of the LST presents a spatial distribution pattern of high values in the northern regions and low values in the southern regions. In the majority of instances, the phase of LST in China was positioned between the 175th and 205th day of each year. Both the annual mean and annual amplitude of the LST have a mean increasing trend in China with a rate of 0.02 K/year, and the areas with large significant changes accounted for 8.6% out of the total area, with a mean rate of approximately 0.05 K/year, in this period. Significant changes in the annual mean LST were correlated with the change in vegetation coverage and the impact of land cover types on the interannual variations of LST is also determined in this study. While the increase in vegetation coverage in barren land exhibits a clearly recognizable upward trend in the annual mean LST, the improved vegetation coverage in the grassland region presents a downward trend in annual mean LST.

/pdf/interannual-spatiotemporal-variations-of-land-surface-2a3icv1wzd.pdf

Interannual Spatiotemporal Variations of Land Surface Temperature in China From 2003 to 2018

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Modeling Year-to-Year Variations of Clear-Sky Land Surface Temperature Using Aqua/MODIS Data

Quantitative analysis of influencing factors and interactions of thermal infrared polarization of water

Interannual Spatiotemporal Variations of Land Surface Temperature in China From 2003 to 2018