# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 4 Discussion
### Seasonality in Observation and Effect of COVID-19 Pandemic on Gas Flaring
However, flaring at the other fields in Russia has increased and thus compensated for the negative trend. VIIRS Active Fire Products Data Applicability for Daytime Gas Flaring Observations  
Compared with VIIRS Nightfire, wildfire detection algorithms such as the MODIS C6 or VIIRS Active Fire Detection algorithms [62; 63] are significantly less sensitive in cases of gas flare detection [16], as they ignore SWIR band signals and use MWIR channels as their principal means of hotspot detection. It should be noted that the \(FRP\) assessment methodology has been recently adapted to VIIRS right-time SWIR observations for gas flare detection [64]. Unlike Nightfire, however, Active Fire Products' daytime observations (which are also applicable to summer-time midnight sun conditions) remain relevant. Here, VIIRS Active Fire Product's (VAFP) hotspot data (available at [[https://firms2.modaps.eosdis.nasa.gov/country/](https://firms2.modaps.eosdis.nasa.gov/country/)]([https://firms2.modaps.eosdis.nasa.gov/country/](https://firms2.modaps.eosdis.nasa.gov/country/)) (accessed on 27 July 2021)) were examined using the case of hotspot detection in Russia in 2019 within gas flare contours obtained with VIIRS Nightfire. From the available dataset, 116,686 valid Active Fire Product detections (26,404 daytime and 90,281 night-time detections) fell within the VIIRS Nightfire gas flare contours. This number is exaggerated, however, as in some cases, lower spatial resolution M13 night-time estimates in VAFP are split equally between I4 pixels [65]; on top of that, large gas flares may be split between several pixels or produce a thermal \"halo\" around themselves (especially in higher spatial resolution I4 observations), resulting in multiple same-time VAFP detections within the same Nightfire contour ID. Thus, all detections within the same Nightfire ID with the time difference between them being less or equal to 1 s had their respective \(FRP\) estimates summed up into a single \(FRP\) estimate, resulting in 71,537 unique VAFP observations. Among the 1176 contours of gas flares detected with Nightfire in Russia in 2019, 927 (79%) have at least a single corresponding detection in VAFP with 718 having \(\geq\)4 unique VAFP detections. The largest gas flares missing in the Active Fire  
Figure 13: VIIRS Nightfire monthly estimated flaring volumes (in BCM) in Russia and USA in 2020. Product dataset had Nightfire-estimated 16 MCM flared volumes. Such flares have relatively high detection frequency (c. 60-70%) via Nightfire, indicating relatively small but persistent routinely operated flares which presumably fall outside the sensitivity range of VIIRS AFP. For the _FRP_ estimates to be comparable with Nightfire _RH_ estimates in MW, a monthly averaged daily _FRP_ mean was later calculated (with daily _FRP_ means accounting for multiple pixel VAFP footprint of single flares within the same scan or several detections occurring the same day) for the unique VAFP observations coinciding within the same Nightfire contour ID. _RH'_, due to the exponent applied to observed flare area, \(S^{D}\) (see Equation (4) in Section 2.5), results in worse correlation with _FRP_ estimates and was thus replaced with linear _RH_. As cloud cover data were not considered in this analysis, annual detection frequency was approximated as the number of days with observations divided by 365 (or the number of days in the month for monthly estimates). The resulting detection frequency was later multiplied by monthly mean _FRP_, resulting in weighted monthly mean _FRP_ (_FRP\({}_{\text{WMM}}\)_) in MW. Figure 14a presents an annual correlation plot of _FRP\({}_{\text{WMM}}\)_ with Nightfire _RH_ estimates. Initially, a strong correlation (R2 = 0.91) is observed; however, a single point, ID 7117 (67.81\({}^{\circ}\)N 72.53\({}^{\circ}\)E), appears to be a conspicuous outlier.