Digital Agriculture Laboratory

Manned Aircraft

Manned aircrafts usage in agriculture first started with aerial application of chemicals on crops during the 1920s[1], and then by advances in photographic devices, airplanes were used as a platform for taking aerial imagery. By the 1950s, some analog photographic devices, initially developed for military use, were employed for agricultural monitoring such as vegetation mapping[2]. The National Aeronautics and Space Administration (NASA) in the United States is one of the pioneers at using aircraft for remote sensing. During the 1970s, NASA used P-3A aircraft, equipped with multiple remote sensing sensors, operating at an altitude of about 300m for agricultural purposes such as mapping soil moisture [3] [4]. However, using aircraft for the research community was very limited, mainly due to operation and sensor costs. Nevertheless, some studies were carried out with the help of private and governmental sectors, and their results were promising enough to attract the attention of other researchers in these areas. With an increasing demand for remote sensing data, some companies, such as Galileo Group, started supplying aircrafts and sensors for researchers. Airplanes with varying working altitude and endurance can be used as a remote sensing platform that enables fast and flexible mapping of large farms. On the other hand, higher cost, need for infrastructure and trained pilots, operational complexity of flights, and lower repeatability due to lack of precise autopilot systems such as terrain-following are their main disadvantages [5]. In the last few years, the function of manned aircraft for aerial mapping has been overtaken by UASs, mainly because of their affordability and operational cost that is continually decreasing, while the expense of aerial imaging with manned aircraft will likely remain the same if not increase. However, some research demand specific data that can be obtained by an airplane better than any other platform. AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) [6] is one of the most famous examples of remote sensing instruments based on airplane platforms. AVIRIS, which has been implemented on four different aircraft, can record 224 contiguous calibrated spectral bands (400 - 2500 nm) at different altitudes covering the United States, Canada, and Europe. It has a variety of applications, including ecology, geology, and agriculture. For instance, AVIRIS data were used to study 3470 km2 orchards, including almond and walnut, during an intense drought period in California's Central Valley (2013–2015). Flight altitude for this dataset was 20 km with a spatial resolution of 18 m[7]. Such a vast area is beyond drones' capability, yet the resolution is not comparable with that of drones. However, changing flight altitude in manned aircraft can be readily done in a wide span to adjust the spatial resolution. In this regard, the aircraft (ER-2 and Twin Otter International) carried the AVIRIS sensor at an altitude of as low as 4 km to reach spatial resolution of 3.3 m and collect data from the April 2010 Deepwater Horizon oil spill. These data were used in an study to analyze the effects on marsh plant community change and mapping distributions of dominant species post-oil spill[8]. Other common airplanes equipped with remote sensing sensors reported even less altitudes: 2300 m AGL by 3I Sky Arrow (50 cm-resolution multispectral images) [9], 1000 m AGL by Spanish Aerospace Institute (INTA,  below 2m resolution hyperspectral images)[10], [11], Cessna 172 Skyhawk aircraft , 150 m AGL (25 cm thermal imagery) [9].

According to table 2, around 39% (9 out of 23) of nut studies have used data collected from manned aircraft platforms. However, less than half of them ([12], [13], [14], and [15]15 % total) used the platform merely for their particular study purpose. The rest of the studies are based on the governmental provided data at a national or international level prepared for multi-purpose applications. This is due to the exorbitant costs of deploying manned aircraft on a large scale.

References

  [1]       “National Agricultural Aviation Association,” 2001. https://www.agaviation.org/industryhistory (accessed Jan. 23, 2020).

[2]         W. G. Rees, Physical principles of remote sensing. Cambridge University Press, 2013.

[3]         T. Schmugge, B. Blanchard, A. Anderson, and J. Wang, “SOIL MOISTURE SENSING WITH AIRCRAFT OBSERVATIONS OF THE DIURNAL RANGE OF SURFACE TEMPERATURE 1,” JAWRA Journal of the American Water Resources Association, vol. 14, no. 1, pp. 169–178, 1978.

[4]         T. J. Schmugge, T. J. Jackson, and H. L. McKim, “Survey of methods for soil moisture determination,” Water Resources Research, vol. 16, no. 6, pp. 961–979, 1980.

[5]         J. Barnes, “Drones vs Satellites: Competitive or Complementary? | Commercial UAV News,” 2018. https://www.commercialuavnews.com/infrastructure/drones-vs-satellites-competitive-complimentary (accessed Jan. 02, 2020).

[6]         Sarah Lundeen, Ken Gowey, “AVIRIS - Airborne Visible / Infrared Imaging Spectrometer - Data Processing,” 2021. https://aviris.jpl.nasa.gov/aviris/data_facility.html (accessed Feb. 26, 2020).

[7]         S. W. Shivers, D. A. Roberts, and J. P. McFadden, “Using paired thermal and hyperspectral aerial imagery to quantify land surface temperature variability and assess crop stress within California orchards,” Remote Sensing of Environment, vol. 222, pp. 215–231, Mar. 2019, doi: 10.1016/j.rse.2018.12.030.

[8]         M. Beland et al., “Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy,” Remote Sensing of Environment, vol. 182. p. 192207, 2016. doi: 10.1016/j.rse.2016.04.024.

[9]         J. Bellvert, P. J. Zarco-Tejada, J. Marsal, J. Girona, V. González-Dugo, and E. Fereres, “Vineyard irrigation scheduling based on airborne thermal imagery and water potential thresholds,” Australian Journal of Grape and Wine Research, vol. 22, no. 2, pp. 307–315, Jun. 2016, doi: 10.1111/ajgw.12173.

[10]       J. A. J. Berni, P. J. Zarco-Tejada, G. Sepulcre-Cantó, E. Fereres, and F. Villalobos, “Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery,” Remote Sensing of Environment, vol. 113, no. 11, pp. 2380–2388, Nov. 2009, doi: 10.1016/j.rse.2009.06.018.

[11]       V. Gonzalez-Dugo, P. Zarco-Tejada, J. A. J. Berni, L. Suárez, D. Goldhamer, and E. Fereres, “Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent,” Agricultural and Forest Meteorology, vol. 154–155, p. 156, 2012, doi: 10.1016/j.agrformet.2011.11.004.

[12]       B. Chen, Y. Jin, and P. Brown, “An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 156, pp. 108–120, 2019.

[13]       J. Bellvert, K. Adeline, S. Baram, L. Pierce, B. L. Sanden, and D. R. Smart, “monitoring crop evapotranspiration and crop coefficients over an almond and pistachio orchard throughout remote sensing,” Remote Sensing, vol. 10, no. 12, Dec. 2018, doi: 10.3390/rs10122001.

[14]       M. López-López, R. Calderón, V. González-Dugo, P. Zarco-Tejada, and E. Fereres, “Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery,” Remote Sensing, vol. 8, no. 4, p. 276, Mar. 2016, doi: 10.3390/rs8040276.

[15]       C. Camino, P. Zarco-Tejada, and V. Gonzalez-Dugo, “Effects of heterogeneity within tree crowns on airborne-quantified SIF and the CWSI as indicators of water stress in the context of precision agriculture,” Remote Sensing, vol. 10, no. 4, p. 604, 2018.