# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 2 Method  
Our method for the estimation of flared gas volume is based on the multispectral night-time observations of flares from space with the VIIRS radiometer onboard the Suomi NPP and NOAA-20 meteorological satellites. For every minute of a night-time (descending) satellite half-orbit, a stack of images in visible and nine infrared spectral bands (we call it a granule) are processed in near-real time with the VIIRS Nightfire algorithm (VNF) [5]. The algorithm can independently detect subpixel IR emitters in SWIR and mid-wavelength infrared (MWIR) bands and characterize them by location, size and temperature, if the signal exceeds the noise level in two or more infrared bands.  
VNF detections for the observation period from April 2012 to present are stored in a relational database and indexed by coordinates and time to accelerate the spatio-temporal queries. Currently, only Suomi NPP detections are used in annual gas flaring catalogs. The VNF algorithm also reports cloud state at the location and time of each detection based on the VIIRS cloud mask and atmosphere optical thickness data products [33; 34]. Earlier, we observed a flaw in the VIIRS cloud mask algorithm for large gas flares, as it detects a small cloud above the flare even if the atmosphere is all clear in the local proximity [6]. The VNF algorithm corrects this flaw by clearing cloud mask for small isolated clouds above the IR emitters.  
The method for estimating annually and monthly flared volumes involved the following steps. First, we defined approximate locations (centroids) and vector boundaries of individual persistent IR emitters by clustering VNF detections in the database. For each cluster centroid, we derived cloud state at every night-time satellite pass, regardless of whether the IR emitter was detected at this location or not. This information was used later to normalize for detection frequency of the flare in cloud-free conditions.  
Second, we aggregated the time series of the VNF detections inside the cluster boundaries for the given time interval to estimate the mean temperature, flare shape and radiative heat (combustion energy). To avoid the signal bias through the clouds, we aggregated only cloud-free observations and normalized the mean radiative heat for the detection frequency. In addition, we corrected the source area of individual detections involved in the aggregation for the satellite view angle, based on the flare shape.  
Third, we classified the persistent IR emitters into upstream gas flares, downstream refineries, LNG terminals, industrial sites, landfills, etc., based on our analysis of flaring in the past for the existing flares and using high spatial resolution daytime satellite imagery and geographical metadata for the new flares (see Section 2.2).  
Finally, we aggregated the estimated flared volumes for individual flares by their type (upstream, downstream) and region (oilfield or country). Here, we used an empirical calibration regression model to derive flared gas volume from the mean radiative heat (see Section 2.5).  
The method flowchart is presented in the Supplementary Materials text version in Figure S1.  
VIIRS Nightfire ad hoc data were processed in close to real time mode (expected delivery lag is 6 h). At the end of each month, the ad hoc data were revised for potentially missing imagery; such imagery was processed and added to the ad hoc data comprising the definitive VIIRS Nightfire dataset, which was then used for gas flare analysis. The resulting annual gas flaring dataset is available online (requires registration) at the Colorado School of Mines Earth Observation group webpage ([[https://eogdata.mines.edu/download_global_flare.html](https://eogdata.mines.edu/download_global_flare.html)]([https://eogdata.mines.edu/download_global_flare.html](https://eogdata.mines.edu/download_global_flare.html)) (accessed on 27 July 2021)).