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

Jia, Shenyue; Kim, Seung Hee; Nghiem, S. V.; Kafatos, Menas

doi:10.3390/rs11131575

Cited by 31 publications

(19 citation statements)

References 42 publications

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“…Nevertheless, it poses the question that wildfires may be highly frequent in cloudy areas (e.g., the tropics are inevitably affected by fire [59] and are often covered by clouds [60]). Microwave remote sensing data has been demonstrated to have better potential application in LFMC inversion because of its strong penetration ability [61] and high sensitivity to surface moisture [62][63][64][65]. The combination of multi-source optical and microwave remote sensing may be an effective way to alleviate the missing data problem caused by weather conditions, allowing high quality and long-term LFMC products for wildfire risk assessment to be generated.…”

Section: Discussionmentioning

confidence: 99%

Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Luo

Quan

et al. 2019

Forests

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Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.

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Section: Discussionmentioning

confidence: 99%

Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Luo

Quan

et al. 2019

Forests

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“…The inversion result of wheat canopy water content based on C-band radar is similar to that of the above study, which indicates that the proposed method in our study can effectively estimate crop wheat canopy water content in farmland areas. In addition, previous studies used the brightness temperature of passive microwave to invert vegetation water content, compared with our study, the higher accuracy of vegetation water content inversion can be obtained from these [46,47]. However, Sentinel-1, as an active microwave sensor, has higher spatial resolution compared with passive radar.…”

Section: Discussionmentioning

confidence: 60%

Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery

Han

Liu

et al. 2019

Sensors

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This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.

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“…In addition to directly relating soil moisture to fire activity, as done in this study, there is also potential to predict live fuel moisture content as an intermediary explanatory variable (Jia et al, 2019; Wang et al, 2019). Predicting fuel moisture content over such a large spatial scale, such as the Western United States, is complicated due to the diversity of fuel types and inherent climate controls on vegetation phenology but should be explored in future work.…”

Section: Discussionmentioning

confidence: 99%

Microwave Retrievals of Soil Moisture Improve Grassland Wildfire Predictions

Rigden

Powell

Trevino

et al. 2020

Geophysical Research Letters

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Statistical analyses of wildfires demonstrate that vapor pressure deficit (VPD) allows for skillful predictions, likely because it reflects fuel moisture content. Soil moisture provides a potentially complimentary measure of water availability but has been less explored because of sparse measurements. Using measurements from the Soil Moisture Active Passive satellite, the predictive skill afforded by using VPD and soil moisture together is explored across the Western United States. Receiver operating characteristic curves estimated from 1,907 fires indicate that inclusion of soil moisture in addition to VPD observations permits for more skillful prediction (p < 0.05). When VPD already signals high risk, the addition of soil moisture reduces the false positive rate grasslands (from 75% to 62%), shrublands (76% to 67%), and forests (74% to 68%) for a true positive rate of 75%. These results show potential to improve daily fire risk models with the addition of remotely sensed soil moisture. Plain Language Summary Identifying environmental conditions that lead to fire ignition is critical for risk management, fire mitigation, and resource allocation. Vapor pressure deficit (VPD) has been shown to be an effective predictor because it reflects water availability near the land surface. More direct measures of water availability, such as soil moisture, have yet to be thoroughly explored because measurements have historically been sparse. Using recently available soil moisture observations from satellites, we explore the ability to retrospectively predict 1,907 fires across the Western United States in grasslands, shrublands, and evergreen needleleaf forests during June-August. Our results show that soil moisture can be used in conjunction with VPD to improve predictions of fires, particularly in grasslands when fire risk is already high.

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

Cited by 31 publications

References 42 publications

Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery

Microwave Retrievals of Soil Moisture Improve Grassland Wildfire Predictions

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