Estimating Fire Background Temperature at a Geostationary Scale - An Evaluation of Contextual Methods for AHI-8

An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixels background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixels surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixels total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5×5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI

[1]  Luke Wallace,et al.  ASSESSMENT OF THE UTILITY OF THE ADVANCED HIMAWARI IMAGER TO DETECT ACTIVE FIRE OVER AUSTRALIA , 2016 .

[2]  Tiejun Wang,et al.  stimating land-surface temperature under clouds using MSG / SEVIRI bservations , 2011 .

[3]  Jennifer Robinson,et al.  Fire from space : global fire evaluation using infrared remote sensing , 1991 .

[4]  W. Setzer,et al.  Satellite Remote Sensing of Fires: Potential and Limitations , 1993 .

[5]  Steven Platnick,et al.  Estimating the direct radiative effect of absorbing aerosols overlying marine boundary layer clouds in the southeast Atlantic using MODIS and CALIOP , 2013 .

[6]  Gareth Roberts,et al.  Development of a multi-temporal Kalman filter approach to geostationary active fire detection & fire radiative power (FRP) estimation , 2014 .

[7]  Y. Kaufman,et al.  Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release , 2005 .

[8]  Gareth Roberts,et al.  Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Simon D. Jones,et al.  Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8 , 2016, Remote. Sens..

[10]  José A. Sobrino,et al.  Satellite-derived land surface temperature: Current status and perspectives , 2013 .

[11]  Louis Giglio,et al.  Application of the Dozier retrieval to wildfire characterization: a sensitivity analysis , 2001 .

[12]  Zhao-Liang Li,et al.  A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data , 1997, IEEE Trans. Geosci. Remote. Sens..

[13]  J. Morisette,et al.  Validation analyses of an operational fire monitoring product: The Hazard Mapping System , 2008 .

[14]  C. Justice,et al.  The collection 6 MODIS active fire detection algorithm and fire products , 2016, Remote sensing of environment.

[15]  C. Justice,et al.  Evaluation of global fire detection algorithms using simulated AVHRR infrared data , 1999 .

[16]  D. Roy,et al.  Satellite Remote Sensing of Fires , 2013 .

[17]  J. Casanova,et al.  Fire detection and monitoring using MSG Spinning Enhanced Visible and Infrared Imager (SEVIRI) data , 2006 .

[18]  M. Wooster,et al.  Major advances in geostationary fire radiative power (FRP) retrieval over Asia and Australia stemming from use of Himarawi-8 AHI , 2017 .

[19]  Arata Okuyama,et al.  Preliminary validation of Himawari-8/AHI navigation and calibration , 2015, SPIE Optical Engineering + Applications.

[20]  C. Justice,et al.  Effect of wavelength selection on characterization of fire size and temperature , 2003 .

[21]  Xiaoguang Jiang,et al.  Comparison of the Thermal Sensors of SEVIRI and MODIS for LST Mapping , 2013 .

[22]  Li Na,et al.  Himawari-8 Satellite Based Dynamic Monitoring of Grassland Fire in China-Mongolia Border Regions , 2018, Sensors.

[23]  C. Justice,et al.  Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER) , 2008 .

[24]  W. Schroeder,et al.  On the use of fire radiative power, area, and temperature estimates to characterize biomass burning via moderate to coarse spatial resolution remote sensing data in the Brazilian Amazon , 2010 .

[25]  W. Schroeder,et al.  Active fire detection using Landsat-8/OLI data , 2016 .

[26]  Offer Rozenstein,et al.  Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm , 2014, Sensors.

[27]  Wei Li,et al.  The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm , 2016 .

[28]  D. E. Hall,et al.  Integrated Active Fire Retrievals and Biomass Burning Emissions Using Complementary Near-Coincident Ground, Airborne and Spaceborne Sensor Data , 2014 .

[29]  Luke Wallace,et al.  Large area validation of Himawari-8 fire active fire products , 2017 .

[30]  G. Roberts,et al.  New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America , 2010 .

[31]  W. Schroeder,et al.  The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment , 2014 .

[32]  Simon D. Jones,et al.  A Broad-Area Method for the Diurnal Characterisation of Upwelling Medium Wave Infrared Radiation , 2017, Remote. Sens..

[33]  T. Nightingale,et al.  Sentinel-3 SLSTR active fire detection and FRP product: Pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets , 2012 .

[34]  J. Cihlar,et al.  Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm , 2000 .

[35]  Jun Wang,et al.  A sub-pixel-based calculation of fire radiative power from MODIS observations: 1 Algorithm development and initial assessment , 2013 .

[36]  Yunyue Yu,et al.  Evaluation of GOES-R Land Surface Temperature Algorithm Using SEVIRI Satellite Retrievals With In Situ Measurements , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[37]  John P. Kerekes,et al.  Radiative transfer in the midwave infrared applicable to full spectrum atmospheric characterization , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[38]  F. V. D. Bergh,et al.  Robust Fitting of Diurnal Brightness Temperature Cycle , 2022 .