<p>Himawari-8 is the next-generation geostationary meteorological satellite, which is developed by JMA and was been launched in October,2014. As the successor to the MTSAT series,Its spatial resolution, observation frequency and position accuracy are much better than the last generation, so it has large advantage in grassland fire monitoring. In this paper, we presentthe method of fire monitoring self-adaptive threshold based on Himawari-8 data, and takean example of using Himawari-8 data to monitor dynamically the grassland fire located near the border of China in April of 2016. The monitoring results show that the fire lasted about 22 hours, the size of burned area were large than 1500 km<sup>2</sup>, the longest duration of a fire pixel was about 6 hours. Through analyzing a series fire information from successive  Himawari-8 10 minutes frequency observation,the result shows that the expanding speed of the fire is 5.4 km in the direction from west to east during some duration, which is up to the extent of fast speed fire type,. Using this method, analyzed the dynamic monitoring in the next day and other scattered fire point in different areas, which indicate that this method is universality in fire monitoring and Himawari-8 can be well used to monitor the fire dynamically changing, get the location, area and temperature of the fire, evaluate the expanding speed, estimate the trends of fire development and raise the ability of grass land fire monitoring and early warning.</p>

In recent years, forest fires have not only destroyed a large amount of vegetation but also the number and burning area of forest fires in the world have increased significantly. In order to reflect the dynamic monitoring analysis and risk assessment of fires in my country in the past 14 years, this paper selects the national terrestrial forest as an area and uses satellite sensing products with a long time series to analyze the time and space of forest burning biomass and forest fires from a qualitative and quantitative perspective. Feature. A power-law distribution-based estimation model for forest burning biomass was established, and the accuracy of the estimation results and the interannual variation pattern reached more than 98%, forming the regional sensitivity of the remote sensing evaluation method. With the emergence of new sensors such as NPP-VIIRS and HIGH, the emergence of high-resolution data has enhanced the ability of forest fire area extraction and fire point information identification, which provides more data sources for forest burning NG biomass estimation and forest fire spatial and temporal pattern analysis using these thermal infrared sensors.

Abstract. Wild fires have a strong negative effect on environment, ecology and public health. However, the continuous degradation of mainstream global fire products leads to large uncertainty on the effective monitoring of wild fires and its influence. To fill this gap, we produced FY-3D global fire products with a similar spatial and temporal resolution, aiming to serve as the continuity and replacement for MODIS fire products. Firstly, the sensor parameters and major algorithms for noise detection and fire identification in FY-3D products were introduced. For accuracy assessment, five typical regions, Africa, South America, Indo-China Peninsula，Siberia and Australia, across the globe were selected. The overall consistence between FY-3D fire products and reference data exceeded 94 %, with a more than 90 % consistence in all regions. Furthermore, the consistence between FY-3D and MODIS fire products was examined. The result suggested that the overall consistence was 84.4 %, with a fluctuation across seasons, surface types and regions. The high accuracy and consistence with MODIS products proved that FY-3D fire product was an ideal tool for global fire monitoring. Specially, since detailed geographical conditions in China were considered, FY-3D products should be preferably employed for fires monitoring in China. FY-3D fire dataset can be downloaded at http://satellite.nsmc.org.cn/portalsite/default.aspx (NSMC, 2021).

Meteorology Geographic Information System (MeteoGIS) is a professional meteorological GIS platform with completely independent intelligent properties. It fully utilizes the national innovative GIS technologies in the meteorological scenario; MeteoGIS supports multiple databases, browsers and a variety of development environments, has a good cross-platform capability. It also has a massive vector and raster data management and distribution capacity. MeteoGIS extends the meteorological data models and data sets, and is able to produce meteorological thematic maps, layout and printing. It has integrated algorithms for meteorological applications and special-use analysis. The platform is comprised of development kits, data engine, desktop software, and Web development platforms.

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