scholarly journals Monitoring Live Fuel Moisture Using Soil Moisture and Remote Sensing Proxies

Fire Ecology ◽  
2012 ◽  
Vol 8 (3) ◽  
pp. 71-87 ◽  
Author(s):  
Yi Qi ◽  
Philip E. Dennison ◽  
Jessica Spencer ◽  
David Riaño
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Fuel Moisture ◽  
Live Fuel Moisture

A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

2013 ◽  
Vol 136 ◽  
pp. 455-468 ◽  
Author(s):  
Marta Yebra ◽  
Philip E. Dennison ◽  
Emilio Chuvieco ◽  
David Riaño ◽  
Philip Zylstra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Moisture Content ◽  
Fire Danger ◽  
Fuel Moisture ◽  
Danger Assessment ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content

Soil Moisture and Live Fuel Moisture Content as key remote sensing variables to unlock improved wildfire predictions

2021 ◽  
Author(s):  
Florian Briquemont ◽  
Akli Benali
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Fire Risk ◽  
Simulation Models ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Live Fuel Moisture ◽  
Risk Maps ◽  
Fuel Moisture Content ◽  
The Impact

<p>Large wildfires are amongst the most destructive natural disasters in southern Europe, posing a serious threat to both human lives and the environment.</p><p>Although wildfire simulations and fire risk maps are already very a useful tool to assist fire managers in their decisions, the complexity of fire spread and ignition mechanisms can greatly hinder their accuracy. An important step in improving the reliability of wildfire prediction systems is to implement additional drivers of fire spread and fire risk in simulation models.</p><p>Despite their recognized importance as factors influencing fuel flammability and fire spread, soil moisture and live fuel moisture content are rarely implemented in the simulation of large wildfires due to the lack of sufficient and accurate data. Fortunately, new satellite products are giving the opportunity to assess these parameters on large areas with high temporal and spatial resolution.</p><p>The purpose of this study is twofold. First, we aimed to evaluate the capabilities of satellite data to estimate soil moisture and live fuel moisture content in different landcovers.  Secondly, we focused on the potential of these estimates for assessing fire risk and fire spread patterns of large wildfires in Portugal. Ultimately, the goal of this study is to implement these estimated variables in fire spread simulations and fire risk maps.<br><br>We compared datasets retrieved from Sentinel 1, SMAP (Soil Moisture Active Passive radiometer) and MODIS (Moderate Resolution Imaging Spectrometer) missions. Several estimators of LFMC based on spectral indices were tested and their patterns were compared with field data. Based on these estimators, we assessed the impact of LFMC and soil moisture on the extent and occurrence of large wildfires. Finally, we built a database of detailed historical wildfire progressions, which we used to evaluate the influence of soil moisture and LFMC on the velocity and direction of the fire spread.</p>

scholarly journals Estimating Live Fuel Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California, USA

2019 ◽  
Vol 11 (13) ◽  
pp. 1575 ◽  
Author(s):  
Shenyue Jia ◽  
Seung Hee Kim ◽  
Son V. Nghiem ◽  
Menas Kafatos
Keyword(s):  
Soil Moisture ◽  
Southern California ◽  
Reference Model ◽  
Resistance Index ◽  
Mediterranean Ecosystem ◽  
Fuel Moisture ◽  
L Band ◽  
Live Fuel Moisture ◽  
The Impact ◽  
Band Radiometer

Live fuel moisture (LFM) is a field-measured indicator of vegetation water content and a crucial observation of vegetation flammability. This study presents a new multi-variant regression model to estimate LFM in the Mediterranean ecosystem of Southern California, USA, using the Soil Moisture Active Passive (SMAP) L-band radiometer soil moisture (SMAP SM) from April 2015 to December 2018 over 12 chamise (Adenostoma fasciculatum) LFM sites. The two-month lag between SMAP SM and LFM was utilized either as steps to synchronize the SMAP SM to the LFM series or as the leading time window to calculate the accumulative SMAP SM. Cumulative growing degree days (CGDDs) were also employed to address the impact from heat. Models were constructed separately for the green-up and brown-down periods. An inverse exponential weight function was applied in the calculation of accumulative SMAP SM to address the different contribution to the LFM between the earlier and present SMAP SM. The model using the weighted accumulative SMAP SM and CGDDs yielded the best results and outperformed the reference model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Visible Atmospherically Resistance Index. Our study provides a new way to empirically estimate the LFM in chaparral areas and extends the application of SMAP SM in the study of wildfire risk.

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