Process-based and geostationary meteorological satellite-enhanced dead fuel moisture content estimation

Figures & data

Figure 1. Location and sampling sites (96 study sites located in 3 areas) in Liangshan Yi Autonomous Prefecture. The background DEM map used in the upper left map is from ASTER GDEM 30M. The other three maps (1, 2, and 3) used World_Imager from the Environmental Systems Research Institute (ESRI) as the background map.

Figure 2. Diagram of dead fuel. Where 1-h dead fuel usually refers to dead fuel with diameter less than 0.635 cm.

Table 1. The satellite data (including himawari-8 and GK2A) and gridded weather data (GFS) used in this study.

Sources	Datasets	Temporal ranges	Spatial resolution	Temporal resolution	Target variables
Himawari-8	JAXA Short Wave Radiation	2020.12.05–2021.05.03	5 km	10 minutes	SR
	CGLS Houly LST		5 km	1 hour	LST
	Himawari-8 reflectance		2 km	10 minutes	NDVI
GK2A	Total Precipitable Water		6 km	10minutes	WV
GFS	Wind speed		0.25°	1 hour	WS
	Precipitation		0.25°	1 hour	prec

Figure 3. The flow chart of our approach, including sensitivity analysis, remote sensing of meteorological variables and process-based models.

Figure 4. The schematic diagram of the process-based model, FSMM, in which the dead fuel (e.g. pine branches) is idealized as a cylinder. The FSMM modelled the water and energy balance processes with input of meteorological variables.

Table 2. Ranges of input meteorological variables used for sensitivity analysis (in simulations). These ranges were determined using the prior knowledge of meteorological conditions in Liangshan.

Variables	Tair (℃)	RH (%)	SR (W·m^−2)	WS (m·s^−1)	prec (mm·hour^−1)
Ranges	(−10, 40)	(0, 100)	(0, 1500)	(0, 32.6)	(0, 15.9)

Table 3. Summary of methods used to estimate meteorological variables. Among them, T_air, RH and SR were from satellites while WS and prec from GFS.

Variable	Abbreviation	Approach
Air temperature	Tair	Calculated from LST, NDVI and SR
Relative humidity	RH	Calculated from Tair and WV
Solar radiation	SR	Himawari-8 product
Wind speed	WS	From GFS (no feasible approach from Himawari-8)
Precipitation	prec	From GFS (no feasible approach from Himawari-8)

Table 4. The S_i, S_Ti of meteorological variables (including T_air, RH, SR, WS and prec) required by the process-based model, FSMM.

Sensitivity	Tair	RH	SR	WS	prec
Si	0.06	0.75	<0.01	0.09	<0.01
STi	0.10	0.80	<0.01	0.15	0.04

Figure 5. Performance of FSMM using different sources of input variables: (a) All input variables from GFS, (b) Satellite-derived RH and other variables from GFS, (c) Satellite-derived RH, T_air, SR, and other variables from GFS, (d) Satellite-derived RH and field-measured T_air and WS. (e) Field-measured RH, T_air and WS.

Figure 6. The performance of the Simard (a), Random forest regression (b), and the FSMM (c) Using satellite-derived RH and other variables from GFS.

Figure 7. The 1-h DFMC maps (hourly from local time 8:00 to 15:00) of Liangshan prior to the Xichang fire, which occurred at 15:35 on March 30, 2020. Maps with DFMC estimation were masked by evergreen needle-leaved forests in the global land-cover product (GLC_FCS30) (Zhang et al. 2021). The DFMC in the grey pixel is missing due to the lack of RH values caused by cloud cover. And the white pixel means that the vegetation type is not evergreen needle-leaved forests.

Figure 8. The DFMC trends in the burned area of wildfires ((a) Xichang-2020, (b) Mianning-2021, (c) Muli-2023, and (d) Daocheng-2023). The dashed red line indicates the threshold values (9.9%, as suggested by Nolan et al. (2016a)) associated with the fire potential in forests.

Zhang, X., L. Liu, X. Chen, Y. Gao, S. Xie, and J. Mi. 2021. “GLC_FCS30: Global Land-Cover Product with Fine Classification System at 30 M Using Time-Series Landsat Imagery.” Earth System Science Data 13:2753–2776. https://doi.org/10.5194/essd-13-2753-2021.

 Web of Science ®Google Scholar

Nolan, R. H., M. M. Boer, V. Dios, G. Caccamo, and R. A. Bradstock. 2016. “Large Scale, Dynamic Transformations in Fuel Moisture Drive Wildfire Activity Across South-Eastern Australia: Transformations in Fuel Moisture.” Geophysical Research Letters 43 (9): ágs. 233–256. https://doi.org/10.1002/2016gl068614.

 Web of Science ®Google Scholar

Data availability statement

The Himawari-8 LST data are available from Copernicus Global Land Service (CGLS) (https://land.copernicus.eu/global/products/lst, last visit on 12th September, 2023). The Himawari-8 SR and reflectance Data can be download using ftp (ftp.ptree.jaxa.jp, last visit on 12th September, 2023).The GK2A WV product (Total Precipitable Water) is available from National Meteorological Satellite Center (NMSC) of Korea Meteorological Administration (KMA) (https://datasvc.nmsc.kma.go.kr/datasvc/html/data/listData.do, last visit on 24th December, 2023). The 1-h field measured DFMC data used in this study will be made available on request.