# An Overview of Infrared Remote Sensing of Volcanic Activity
## 5 Infrared Remote Sensing of Volcanoes: A History
The concept was that, given that MIR channels (i.e., AVHRR Band 3) are more sensitive to surfaces of magmatic temperature than to those at terrestrial temperatures (see Figure 1), where this temperature difference exceeded a given threshold (applied as 10 K), the surface could be reliably interpreted as displaying a sub-pixel hotspot, which was contributing to the Band 3 signal. Automated processes using such algorithms rapidly developed, including some that were location-specific, such as the Okmok algorithm, which was named after an Alaskan volcano by the same name [96; 42]; the Okmok algorithm functioned operationally with AVHRR data, by comparing the temperature of a potentially anomalous pixel to the temperatures of pixels around it; where this temperature difference was shown to be significant, it was automatically determined as anomalous [36]. Steps in automation were also facilitated by improvements in data distribution and storage, with one of the possibilities raised being the monitoring and observation of volcanic activity over wide areas, as opposed to at just one location. [PERSON] et al. [42] showed how routine AVHRR observations, and the Okmok algorithm, could be used to monitor all volcanoes in the North Pacific, and soon after this, one of the most pervasive and widely-referenced volcanic detection systems was developed called MODVOLC [97; 64; 98]. This system, which remains operational, works on similar principles to other algorithms: that hotter surfaces will produce a higher spectral radiance at shorter wavelengths, and vice versa. For each MODIS pixel of data, the MODVOLC algorithm calculates a normalized thermal index (NTI) value by differencing the spectral radiant detection between the MIR (Band 22: 3.9 \(\upmu\)m) and TIR (Band 32: 12.00 \(\upmu\)m) bands and then normalizing the result by the sum of these radiances:  
\[NTI=\frac{R_{22}-R_{32}}{R_{22}+R_{32}} \tag{2}\]  
where \(R_{22}\) and \(R_{32}\) refer to the spectral radiance (W m\({}^{-2}\) sr\({}^{-1}\) m\({}^{-1}\)) in MODIS Bands 22 and 32, respectively. If Band 22 is saturated, data from the equivalent, low-gain Band 21 can be used instead. A greater difference between Bands 22 and 32 indicates the presence of a hotter surface, and a threshold is used to identify potential volcanic hotspots [98]. The principle of differencing MIR and TIR signals was also utilised in a later, AATSR-bespoke volcano monitoring system: \"Volcanoes Monitoring by Infra-Red\", or VoMIR [99]. Arguably the most sophisticated automated system of the period was the \"Autonomous Scinececraft Experiment System\" (later named the \"Volcano Sensor Web\") developed for the sensors of the EO-1 spacecraft [100; 101]. This system functioned by processing Hyperion SWIR data; if a potential hot surface was identified, then a trigger was generated to re-point the Hyperion sensor appropriately. The system could also be triggered by other sources, such as MODIS, or on-the-ground observations, which re-tasked the satellite to make follow-up observations within two days of notification [100; 102]. The effectiveness of this system was demonstrated during the 2010 Eyjafjallajokull eruption, when 50 such observations were made of the erupting volcano over a period of 74 days, ultimately providing data to stakeholders in Iceland to assist in risk and hazard assessments [101]. Despite early recognition of their utility in observing active volcanoes (e.g., [17; 90]), few detection algorithms were ever developed for SWIR data. This is because of the greater availability of regular MIR and TIR observations and because MIR and TIR observations were usually available at moderate to low spatial resolution. This latter point meant that large volumes of frequent, wide-area coverage data were obtained that required automated processing, as opposed to more localised, and often ad hoc, SWIR observations [44].