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.

scholarly journals Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1900
Author(s):  
Cong Yin ◽  
Ernesto Lopez-Baeza ◽  
Manuel Martin-Neira ◽  
Roberto Fernandez-Moran ◽  
Lei Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Circular Polarization ◽  
European Space Agency ◽  
In Situ Measurements ◽  
Anchor Station ◽  
L Band ◽  
The Impact ◽  
Band Radiometer ◽  
Soil Moisture Monitoring

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.

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 Soil moisture and vegetation optical depth retrievals over heterogeneous scenes using LEWIS L-band radiometer

2021 ◽  
Vol 102 ◽  
pp. 102424
Author(s):  
M. Barrée ◽  
A. Mialon ◽  
T. Pellarin ◽  
M. Parrens ◽  
R. Biron ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
L Band ◽  
Band Radiometer

Sampling depth of L-band radiometer measurements of soil moisture and freeze-thaw dynamics on the Tibetan Plateau

2019 ◽  
Vol 226 ◽  
pp. 16-25 ◽  
Author(s):  
Donghai Zheng ◽  
Xin Li ◽  
Xin Wang ◽  
Zuoliang Wang ◽  
Jun Wen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
The Tibetan Plateau ◽  
Sampling Depth ◽  
Freeze Thaw ◽  
L Band ◽  
Band Radiometer

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

scholarly journals Relationships between GPP, Satellite Measures of Greenness and Canopy Water Content with Soil Moisture in Mediterranean-Climate Grassland and Oak Savanna

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shishi Liu ◽  
Oliver A. Chadwick ◽  
Dar A. Roberts ◽  
Chris J. Still
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Gross Primary Production ◽  
Resistance Index ◽  
Growing Season ◽  
Middle Part ◽  
Oak Savanna ◽  
Similar Response ◽  
The Impact ◽  
Canopy Water

We investigated the impact of soil moisture on gross primary production (GPP), chlorophyll content, and canopy water content represented by remotely sensed vegetation indices (VIs) in an open grassland and an oak savanna in California. We found for the annual grassland that GPP late in the growing season was controlled by the declining soil moisture, but there was a 10–20-day lag in the response of GPP to soil moisture. However, during the early and middle part of the growing season, solar radiation accounted for most of the variation in GPP. In the oak savanna, the grass understory exhibited a similar response, but oak trees were not sensitive to soil moisture in the upper 50 cm of the soil profile. Furthermore, while we found most VIs to be more or less related to soil moisture, the Visible Atmospherically Resistance Index (VARI) was the most sensitive to the change of soil moisture.

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