Satellite Imagery in Precision Agriculture

Many national and international institutions recognize that novel agriculture paradigms are needed to address the current challenges of adaptation to and mitigation of climate change. In this sense, digital agriculture and, specifically, satellite images in precision farming allow efficient monitoring of crops to ameliorate the management impacts to the environment. These data allow estimating yields or fertilization requirements, as well as water-related aspects, such as evapotranspiration and crops hydric status. In this chapter, I aim to describe satellite imagery applications in precision agriculture, and I present the nature of remotely sensed data, the types of satellites, and data access, management, and processing in the case of precision farming applications. Landsat 9, Sentinel-2, as well as other commercial satellites orbiting the Earth are described as feature relevant characteristics for agriculture monitoring, especially regarding the visible, near-infrared, and red-edge parts of the spectrum, which can be related to biomass, canopy vigor, or chlorophyll content and subsequently be matched with agronomic features. Regarding data access and coverage, openly accessible datasets and commercial satellites are discussed. Moreover, data management and processing have also been presented in regard to the limitations that processing and analyzing such large amounts of data (i.e., images from vast agricultural regions on a daily basis) has and the potential of cloud computing and processing. I conclude that in industrial agriculture settings, openly accessible satellite imagery can contribute significantly to an overview the status of crops, guide specific and timely actions, and reduce production losses and the impacts on the environment. Satellite imagery has a spatial dimension that can be used at the field to regional level. The assessment of agricultural performance can also be matched to several agroecological and environmental levels; however, satellite imagery in precision farming has several limitations and knowledge gaps in its application in heterogenous and agricultural landscapes with small-scale fields.