The infrared spectral region is usually divided into several subdivisions based on their wavelength with approximate boundaries, such as NIR (~ 0.7 μm -1.1 μm), shortwave infrared (SWIR, ~ 1.1 μm -2.5 μm), middle (or mid-wave) infrared (MIR, ~2.5-50 μm) and far (or long-wave) infrared (FIR, >50 μm). However, another subdivision system is based on the radiation type: reflected infrared, emitted infrared[1]. In spectral regions with noticeable sun radiation (e.g. NIR-SWIR, see figure 3) the reflected energy dwarfs the emitted energy marking this region as the reflected infrared [2]. On the other hand, emitted infrared, also known as thermal infrared, is based on the black body radiation (earth’s radiation), as mentioned in the sensors section; all objects with a temperature higher than absolute zero (zero Kelvin) emit radiations proportionate to the emissivity of the surface and the surface temperature[3]. Thermal infrared wavelength starts from 3 µm and goes as high as 50 µm. However, reflected sunlight can easily tamper 3 - 5 µm region during daytime imaging. Additionally, atmospheric absorptions block most other regions leaving 8 - 14 µm the only available atmospheric windows for thermal imaging[2]. As a result, most thermal cameras are designed to be sensitive to this wavelength range. In fact, some cameras are designed to be sensitive to several regions turning them to multispectral thermal cameras. NASA’s Thermal Infrared Multispectral Scanner (TIMS), for example, measures 6 bands in the 8.2–12.2 μm spectral region[4].

Thermal cameras interpreted the surface temperature based on the energy received in two ways: direct and indirect measurements. Direct measurement cameras, also known as cooled cameras, take advantage of quantum detectors cooled to cryogenic temperatures (close to 77 K). On the other hand, uncooled cameras operate within the ambient temperature and rely on thermal detectors to indirectly measure the temperature[5]. Uncooled cameras have a lower spatial resolution, sensitivity, and shutter speed than cooled cameras [6] but price, size, weight, and operation condition make uncooled cameras more convenient on sUAS and for remote sensing. In this paper, uncooled cameras are referred to as thermal cameras. Compared to reflective region cameras (RGB and multi/hyperspectral), thermal cameras are inferior in resolution, regardless of their significant advances in recent years. For instance, sensors used at[7] that were mounted on an sUAS flying at 70 m AGL, yielded ground resolutions of 1 cm/pixel, 4 cm/pixel, and 9 cm/pixel in RGB, multispectral and thermal images, respectively, showing that thermal imaging was 9 times coarser than RGB imaging. In higher altitudes, thermal imaging becomes even more challenging due to the atmospheric disturbances. For example, environmental variability (e.g., light intensity, temperature, relative humidity, wind speed)[8] and undesired incident radiation [9] can alter the thermal data. As a result, spatial resolution of satellite-based thermal imagery is usually tens of meters (100 meters at Landsat 8, for example). In general, thermal imagery still suffers from the coarser resolution, higher price, and complexity of the calibration process. However, its beneficial information out weight its shortcoming.

Measurement of radiation within the thermal region offers unique advantages that make it an indispensable spectral region for agricultural remote sensing. For example, water and nutrient stress symptoms in some crops are detectable earlier in the thermal region than in the visible range[10]. As a result, canopy temperature can be used as a gauge for measuring the overall plant health. In general, canopy temperature is a function of atmospheric evaporative demand and crops' water/nutrition status. This fact has established a foundation for numerous studies to investigate the effects of drought, deficit irrigation, and heatwave on plants. Besides that, thermal imagery, have demonstrated a good potential for calculation and validation of the other vegetation metrics such as Leaf Area Index (LAI = leaf area / ground surface area) and chlorophyll content [11].


[1]       J. R. Jensen, Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India, 2009.

[2]       R. Ishimwe, K. Abutaleb, and F. Ahmed, “Applications of thermal imaging in agriculture—A review,” Advances in remote Sensing, vol. 3, no. 03, p. 128, 2014.

[3]       S. Khanal, J. Fulton, and S. Shearer, “An overview of current and potential applications of thermal remote sensing in precision agriculture,” Computers and Electronics in Agriculture, vol. 139, pp. 22–32, 2017.

[4]       F. D. Palluconi and G. R. Meeks, “Thermal infrared multispectral scanner (TIMS): An investigator’s guide to TIMS data,” 1985.

[5]       S. Deane et al., “Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites,” Sensors, vol. 20, no. 12, p. 3381, 2020.

[6]       FLIR, “Cooled or Uncooled?,” 2018. (accessed Mar. 05, 2021).

[7]       A. Matese and S. F. Di Gennaro, “Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture,” Agriculture (Switzerland), vol. 8, no. 7, Jul. 2018, doi: 10.3390/agriculture8070116.

[8]       J. M. Costa, O. M. Grant, and M. M. Chaves, “Thermography to explore plant–environment interactions,” Journal of Experimental Botany, vol. 64, no. 13, pp. 3937–3949, Apr. 2013, doi: 10.1093/jxb/ert029.

[9]       M. Fuchs, “Infrared measurement of canopy temperature and detection of plant water stress,” Theoretical and Applied Climatology, vol. 42, no. 4, pp. 253–261, Dec. 1990, doi: 10.1007/BF00865986.

[10]     R. Calderón, J. A. Navas-Cortés, C. Lucena, and P. J. Zarco-Tejada, “High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices,” Remote Sensing of Environment, vol. 139, pp. 231–245, 2013.

[11]     J. A. J. Berni, P. J. Zarco-Tejada, L. Suárez, and E. Fereres, “Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 3, pp. 722–738, Mar. 2009, doi: 10.1109/TGRS.2008.2010457.