After a growing interest in remote sensing data for research during the 1960s, the launch of Landsat 1 in 1972 was the most important milestone in this field. Rapid advances in digital image processing accelerated the progress of remote sensing to the point that Landsat 4 with a new generation of sensors were launched a couple of decades later in 1982 [1]. Since then, satellite sensing and imagery, like other imaging platforms, are improving rapidly both in technical and cost aspects. Commercial satellite imagery is more affordable nowadays and available with higher spatial resolutions than before [2] for a variety of applications ranging from PA to mining, defense, and intelligence. Today, the spatial resolution of satellite imagery has reached to submeter range (For example, 0.31 m for panchromatic nadir images and 1.24 m multispectral nadir- WorldView-4) and can cover a vast area in every pass. Still, the use of satellite-based remote sensing in PA could be limited because of their low spatial resolution for some purposes and/or inflexible acquisition times [3]. Additionally, satellite imagery has some intrinsic weaknesses due to their high altitude. For example, clouds can completely block satellite imagery in visible regions, or atmospheric scattering caused by constituent gases and aerosols in the atmosphere makes some bands almost unusable for agricultural remote sensing even though they are useful for atmospheric studies [4]. In general, electromagnetic signals must pass through a considerable depth of the Earth’s atmosphere to reach the sensor on the satellite and based on the atmospheric condition some degree of signal attenuation will happen and some noise will be blended with signals. As a result, measurement time and period highly depend on the weather and atmosphere condition because of the fixed-timing acquisitions i.e. satellite revisit time [5].

Unlike sUAS platforms that are a suitable solution when a “micro” view of the land is of interest, satellites provide a relatively low-cost “macro” view of the terrains [6] that makes them an efficient method for large-scale mappings such as desertification, land cover classification, climate change, and inter-field comparisons. For instance, Jin et. al used the Landsat-8 observations to differentiate evapotranspiration (ET) levels in 160 ha pistachio orchards with different salinity levels [7]. As shown in Table 2, the average study aera based on satellite images exceed 2000 ha with the spatial resolution ranging from 3 to 30 meters in MS data and over 100 meters for TH data.

Some studies deploy manned aircraft or even sUAS to collect data simultaneous to satellite pass as a benchmark for satellite images to verify their results. For example, Chen et al. collected MS data from satellites PlanetScope (3 m), Sentinel-2 (10 m), and Landsat (30 m) to cover 1700 ha of almond orchards at California’s Central Valley for bloom phenology. At the same time, they used a manned aircraft to cover the whole area with 20 cm resolution, and an sUAS with 2.6 cm resolution for a small portion of the orchard [8]. In a similar study, for estimating actual ET and crop coefficients of 16 ha of almond and pistachio orchards, Landsat-8 imagery (30 m resolution) was used concurrently with images from a manned aircraft with 0.6-1.5 meter resolution [9].

As the literature suggests, vast areas can be mapped by satellite remote sensing, mainly for large-scale studies and monitoring. Nevertheless, even satellites with the highest image resolution lack the spatial, spectral, and temporal resolution required for precise measurements needed for most PA practices. In such cases, other platforms may provide better results.


  [1]       J. B. Campbell and R. H. Wynne, Introduction to remote sensing. Guilford Press, 2011.

[2]         LAND INFO Worldwide Mapping, LLC, “Buying Satellite Imagery: GeoEye, WorldView 1, 2, 3, QuickBird, IKONOS, Pléiades,” 2019. (accessed Jan. 01, 2020).

[3]         J. V. Stafford, “Implementing precision agriculture in the 21st century,” Journal of Agricultural Engineering Research, vol. 76, no. 3, pp. 267–275, 2000.

[4]         G. Dalu, “Satellite remote sensing of atmospheric water vapour,” International Journal of Remote Sensing, vol. 7, no. 9, pp. 1089–1097, 1986.

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

[6]         J. Barnes, “Drones vs Satellites: Competitive or Complementary? | Commercial UAV News,” 2018. (accessed Jan. 02, 2020).

[7]         Y. Jin et al., “Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates,” Agricultural and Forest Meteorology, vol. 262, pp. 178–191, Nov. 2018, doi: 10.1016/j.agrformet.2018.07.004.

[8]         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.

[9]         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.