Remote Sensing


The field of remote sensing is growing at a breathtakingly unprecedented rate. Such technologies run the gamut from sensors installed in situ to detect soil moisture levels to the use of cameras and sensors to spot-apply herbicides to the use of satellite imagery to quantify the adoption of sustainable agricultural strategies such as cover crop use. The rise of highly flexible low-cost technologies such as the Raspberry Pi has made economically-feasible the opportunity to implement these tools across the farms and research stations in the PSA network. Data collection across a range of climates, soil types, and management regimes is the key to understanding how cover crop genetics, the environment, management, and social factors interact, which in turn allows us to provide farmers with better recommendations and tools for sustainably managing their cropping systems.


The remote sensing team works at multiple levels to support multiple PSA goals. The team develops and refines tools (e.g., soil moisture sensors) to support the data collection of the field experiment teams. The remote sensing team is working with tractor-mounted cameras and sensors to estimate cover crop biomass in the hope of one day minimizing or eliminating the need to destructively harvest cover crop biomass, which is the current (time-consuming and labor intensive) way to quantify cover crop performance. The remote sensing team is also interested in the use of cameras to identify weeds so that precision technologies such as spot sprayers can be employed to control weeds while minimizing the amount of herbicide applied. Finally, the remote sensing team is investigating the use of satellite imagery to quantify cover crop acreage and performance at the landscape level.

Data Collection

Data is collected with a variety of in situ sensors, tractor-mounted sensors, and through access to drone and satellite imagery. These sensors often work in tandem with Raspberry Pi technology. Data types range from soil moisture to cover crop percent ground cover to cover crop biomass and beyond.

Photo of field sampling and remote sensing with a camera.
Top: photos of crops and weeds in the field. Bottom: photos with algorithms applied to distinguish weeds (green) from crop plants (red). Photo credit: P. Ramos-Giraldo.
Satellite imagery of Maryland in April 2021, used to estimate acreage of late-terminated cover crops. Vegetation is in green. Photo credit: J. Jennewein.