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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.

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[1] The potential for regional climate change arising from adoption of policies to increase production of biofuel feedstock is explored using a regional climate model. Two simulations are performed using the same atmospheric forcing data for the period 1979-2004, one with presentday land use and monthly phenology and the other with land use specified from an agro-economic prediction of energy crop distribution and monthly phenology consistent with this land use change. In Kansas and Oklahoma, where the agro-economic model predicts 15-30% conversion to switchgrass, the regional climate model simulates locally lower temperature (especially in spring), slightly higher relative humidity in spring and slightly lower relative humidity in summer, and summer depletion of soil moisture. This shows the potential for climate impacts of biofuel policies and raises the question of whether soil water depletion may limit biomass crop productivity in agricultural areas that are responsive to the policies. We recommend the use of agronomic models to evaluate the possibility that soil moisture depletion could reduce productivity of biomass crops in this region. We conclude, therefore, that agro-economic and climate models should be used iteratively to examine an ensemble of agricultural land use and climate scenarios, thereby reducing the possibility of unforeseen consequences from rapid changes in agricultural production systems.