# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 1 Introduction  
Wildfires are the essential factor in the formation and evolution of ecosystems [1; 2]. They shape the creation of a natural landscape, ensure the diversity and stability of the organism and change the biophysical characteristics of the soil [1; 3; 4; 5]. Additionally, wildfires are the natural disaster that can cause severe economic, environmental and social losses [6; 7; 8; 9; 10]. According to the Emergency Events Database (EM-DAT), the global annual average economic losses due to wildfires by 2015, reached 2677 million US dollars, and around six million people are affected worldwide, every year [11; 12]. These numbers highlight the importance of a quantitative understanding of wildfire behavior. Fire Spread Rate (FSR) is a fundamental parameter for estimating wildfire behavior, flame height, flamelength, fire line intensity, duration of the fire, etc. [13; 14; 15; 16]. Wildfires typically spread forward with a rapid FSR in areas with dense, unbroken vegetation. Estimating near real-time FSR during wildfires can provide timely useful information for fire monitoring and management and thus improve the efficiency of operational firefighting [17; 18; 19]. Several methods have been applied to estimate FSR in the last few decades. These methods can be divided into two categories: traditional (e.g., laboratory or field observation, fire spread model estimation) and remotely sensing based methods (e.g., extraction from satellite and airborne images). The field or laboratory experiments generally estimate FSR by using ocular observations, visible or infrared spectrum images and thermocouple instruments [20]. These methods are supposed to be highly accurate. On the other hand, they also have the disadvantage of being site-specific, low efficiency, and therefore impossible to be extended to larger scales [20]. Fire spread models can be further classified into two categories: empirical statistical and physical models [21; 22]. Empirical statistical models make use of observed data generated from the field or laboratory experiments to establish the relationship between FSR and fuel-, topographical-, and meteorological-related factors [23]. These methods are simple and computationally efficient and have been developed for fuels ranging from grasslands to shrublands and forests in different regions [16; 24; 25; 26; 27]. However, empirical approaches tend to be site-specific and data-dependent, therefore requiring frequent re-calibrations ensuring these are applicable to new circumstances (e.g., more severe fire as a result of climate change or different fuel arrangements). To that end, empirical approaches lack generality [28]. Physical models, such as the FIRETEC [29], AIOLOS-F [30], FIRESTAR [31] and WFDS [32] are more robust since they take into account the mechanism of fire propagation by combining thermodynamics, air-dynamics and botany [33]. However, calculating the balanced equations of the conservation of energy and momentum is usually time-consuming. Wildfires tend to burn in more extreme conditions and uncertain locations [34] which makes near real-time FSR estimation using traditional methods more difficult. Remote sensing technology can directly obtain information from the ground and provide a unique, cost-effective source of information for a spatial and temporal understanding of wildfire behavior. Satellite products widely used for extracting the FSR are the Moderate Resolution Imaging Spectroradiometer (MODIS) global scale active fire (MCD14 ML) and burned area (MCD64A1 and MCD45A1) products. For example, [PERSON] et al. [35] used MODIS Terra (MOD14) and Aqua (MYD14) satellite products to reconstruct the fires that occurred in Siberia between 2001 and 2004 and then obtained historical annual maps of FSR. Based on the morphological spread structure of each fire event, [PERSON] et al.