During few last years, climate change including global warming which is attributed to human activities and also its long-term adverse effects on the planet's functions have been identified as the most challenging discussion topics which have arisen many concerns and efforts to find the possible solutions. Since the warmth arising from Earth's landscapes affects the world's weather and climate patterns, we decided to study the changes in the Land Surface Temperature (LST) patterns in different seasons through non-linear methods. Here, we particularly want to estimate the non-integer dimension and fractal structure of the land surface temperature. For this study, the (LST) data has been obtained during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite. Depending on what time of the year data has been collected, temperatures change in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the U.S. state of Kansas, which does not belong to any of this type locations and we are interested to observe the seasonal changes in temperature in this county. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal process with non-integer dimension.
Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators
University of Freiburg（弗莱堡大学）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
A physics-constrained machine learning method for mapping gapless land surface temperature
School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）；Institute of Urban Meteorology, China Meteorological Administration（中国气象局城市气象研究所）；Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University（安徽大学安徽省信息材料与智能感知实验室）
Satellite-derived Land Surface Temperatures Strongly Mischaracterise Urban Heat Hazard
Nanjing University（南京大学）；Ruhr-University Bochum（波鸿鲁尔大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；École Polytechnique（巴黎综合理工学院）；University of Guelph（圭尔夫大学）；CIEMAT（能源、环境与技术研究研究中心）；University of New South Wales（新南威尔士大学）；National University of Singapore（新加坡国立大学）；National Observatory of Athens（雅典国家天文台）；The University of British Columbia（不列颠哥伦比亚大学）；Western University（韦仕敦大学）
A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature
School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）；Key Laboratory of Geographic Information System of Ministry of Education（教育部地理信息系统重点实验室）；Key Laboratory of Digital Cartography and Land Information Application of the Ministry of Natural Resources（自然资源部数字测绘与土地信息应用重点实验室）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）
A Mechanism-Coupled Split Window Network for Medium- to High-Resolution Land Surface Temperature Retrieval
School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）；Key Laboratory of Geographic Information System of Ministry of Education, Wuhan（教育部地理信息系统重点实验室）；Key Laboratory of Digital Cartography and Land Information Application of the Ministry of Natural Resources, Wuhan（自然资源部数字测图与土地信息应用重点实验室）
