remote sensing Review Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities Sami Khanal * , Kushal KC , John P. Fulton, Scott Shearer and Erdal Ozkan Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH 43210, USA; [email protected] (K.K.); [email protected] (J.P.F.); [email protected] (S.S.); [email protected] (E.O.) * Correspondence: [email protected] Received: 5 October 2020; Accepted: 11 November 2020; Published: 19 November 2020 Abstract: Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability. Keywords: remote sensing; satellite; UAS; precision agriculture 1. Introduction Digital agriculture or precision agriculture (PA), concepts that are often used interchangeably, represent the use of large data sources in conjunction with advanced crop and environmental analytical tools to help farmers adopt the right management practices at the right rates, times and places, with the goal of achieving both economic and environmental targets. In recent years, there has been growing interest in PA globally as a promising step towards meeting an unprecedented demand to produce more food and energy of higher qualities in a more sustainable manner by optimizing externalities. Remote sensing (RS) is one of the PA technologies that allows growers to collect, visualize, and evaluate crop and soil health conditions at various stages of production in a convenient and cost-effective manner. It can serve as an early indicator to detect potential problems, and provide opportunities to address these problems in a timely fashion. Application of RS technologies in agriculture started with the first launch of the Landsat Multispectral Scanner System (MSS) satellite in 1972. Bauer and Cipra [1] used Landsat MSS to Remote Sens. 2020, 12, 3783; doi:10.3390/rs12223783 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, x FOR PEER REVIEW 2 of 28 recently, the use of satellite-based data for PA has been sparse and limited only to the large-scale monitoring and mapping of agricultural health due to the limited availability of high spatial (>5 m) and temporal (daily) resolution satellite data. With technological advancements in global positioning systems (GPS), machinery, hardware and software, cloud computing, and Internet of Things (IoT), RS technologies can now be used at a scale much smaller than a field. Some of this is evident from a long list of satellite sensors with high spatial and temporal resolutions that have been deployed on earth orbits over the decades since 1999 (Figure 1). Various RS platforms are currently used, including handheld, aircraft and satellite, which can be used to collect data at different spatial, temporal, and spectral resolutions. The most appropriate Remoteresolutions Sens. 2020 required, 12, 3783 for PA depend on multiple factors, including management objectives, crops2 ofand 29 their growth stages, the size of a field, and the ability of a farm machinery to vary inputs (fertilizer, pesticides, irrigation). For instance, ability to detect crop emergence is highly dependent on higher classifyspatial resolution the Midwestern data (<0.1 US agricultural m) that can landscapes help differentiate into corn crop or soybean characteristics fields. However, (i.e., leaves, until area) recently, at a thestand use level of satellite-based [2–5]than that datarequired for PA for has crop been yield sparse estimation and limited (1–3 m) only [6]; to multispectral the large-scale imagery monitoring helps andassess mapping crop health of agricultural patterns that health visible due (VIS) to the imag limitedery cannot availability detect of [7], high and spatial thermal (>5 imagery m) and temporalis useful (daily)for detecting resolution pest satellite pressure data. [8,9], With soil technological moisture [10], advancements and crop water in global stress positioning [11] that the systems naked (GPS), eye machinery,cannot detect. hardware Unlike andvisible software, and infrared cloud (IR) computing, -based RS, and microwaves Internet of Thingsare less (IoT),prone RS to technologiesatmospheric canattenuation now be usedand can at a help scale determine much smaller the thanbiophysical a field. Someproperties of this of is crops evident and from soil aunder long list any of day satellite and sensorsnight conditions with high [12,13]. spatial and temporal resolutions that have been deployed on earth orbits over the decades since 1999 (Figure1). Figure 1. List of satellite sensors and their spatial resolutions since 1999. Note: Satellites with a multispectralFigure 1. List sensor of satellite typically sensors provide and visual their orspatial panchromatic resolutions bands. since NIR: 1999. near Note: infrared. Satellites with a multispectral sensor typically provide visual or panchromatic bands. NIR: near infrared. 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