## 2 Literature Review
### Discussion
#### 2.2.4 Satellite Surveillance  
NASA and NOAA were two of the first organizations to observe wildfires using an extensive network of polar orbiting (Terra, Aqua) and geostationary (GOES) satellites. Polar satellites scan the entire Earth a few times each day and can monitor the entire planet for fires (Figure 6). However, each consecutive scan over the same geolocation is spaced several hours apart and, therefore, the temporal sampling rate for such satellites is low. On the other hand, geostationary satellites provide much higher temporal data for a specific area but cannot monitor other global environmental developments. The instruments on board these satellites monitor several different kinds of data, some of which are actively analyzed to detect/observe wildfires, global transport of pollutants and long-term climate impacts of fire (Table 1, [86]). Processing this data for wildfire anomalies, especially small fires, poses a challenge due to the lower spatial resolution of satellite images. In addition, smoke can easily appear identical to clouds, as shown in Figure 6 of the Camp Fire, imaged by moderate resolution imaging spectroradiometer (MODIS) on Terra. Most environment observational satellites have visible and infrared detectors on board. Among the infrared bands, the 3.8/4 \(\upmu\)m (middle infrared) and 10.8/11 \(\upmu\)m (long-wave infrared) wavelengths are used to identify active fires (Table 2). The relative brightness temperatures of fire pixels on these two bands are different from each other, with the 4 \(\upmu\)m channel (T\({}_{4}\)) registering a significantly increased radiance for high-temperature sources than the 11 \(\upmu\)m channel (T\({}_{11}\)). The difference in brightness (\(\delta\)T = T\({}_{4}\) - T\({}_{11}\)) of pixels between these two channels is compared with a pre-determined threshold to detect fires and map affected areas. This simple and intuitive approach was first proposed by [PERSON] and [PERSON] in 1981 on AVHRR data [88]. Additional IR channels are often used to improve detection, by helping reject false alarms caused by clouds, bright surfaces, sun glints, water vapor, etc. [89]. A similar approach was applied to data from MODIS, ASTER and GOES, with many iterations proposing improved sensitivity to smaller and lower-temperature fires [89; 90]. One of the major challenges of the earlier algorithms was that the threshold levels for comparing \(\delta\)T were empirically determined, and therefore varied across different kinds of biomes on Earth. In a modified approach (version 4), [PERSON] et al., proposed subsequent steps after the basic absolute threshold test has identified a fire pixel by comparing T\({}_{3}\) or T\({}_{4}\) to a certain numeric value [89]. In the following step, the pixels neighboring the pixel under consideration are used to estimate the radiance of the fire pixel in the absence of a fire. The pixel window is started at 3 \(\times\) 3 and gradually increased until sufficient ground-based, non-fire pixels without clouds have been registered to compute the background signature. If this step is successful, a third step uses this background signature to calculate threshold values that hold contextual relevance to the specific area being investigated. \(\delta\)T is now compared to this threshold for identifying a tentative fire. In the last step, further tests using another set of different threshold formulae help in rejecting  
Figure 6: MODIS image of the Camp Fire in northern California on Nov 14, 2018 [87]. Image by NASA Earth Observatory. false alarms due to sun glints, deserts and coastal boundaries, water bodies, clouds and other sources of abrupt radiance transition. In a radically alternate approach compared to the above method, deep-learning techniques were proposed for active fire identification. To facilitate this, [PERSON] et al., generated a publicly available dataset of image patches from Landsat-8 satellite covering global fire events in 2021 [91]. The database also included annotated fire pixels and is available on GitHub.