College of Illinois scientists, with an assist from members of the Illinois Corn Growers Association, has developed a brand new, scalable technique for estimating crop productiveness in actual time. The analysis, revealed in Remote Sensing of Environment, combines area measurements, a singular in-discipline digital camera community, and excessive-decision, excessive-frequency satellite information, offering correct productiveness estimates for crops throughout Illinois and past.
Kimm and his colleagues used floor reflectance information, which measures gentle bouncing of the Earth, from two sorts of satellites to estimate LAI in agricultural fields. Each satellite datasets signify main enhancements over older satellite technologies; they’ll “see” the Earth at an advantageous scale (three-meter or 30-meter decision), and each return to the identical spot above the planet every day. Because the satellites do not seize LAI straight, the analysis group developed two mathematical algorithms to transform floor reflectance into LAI.
Whereas growing the algorithms to estimate LAI, Kimm labored with Illinois farmers to arrange cameras in 36 corn fields throughout the state, offering steady floor-degree monitoring. The pictures from the cameras supplied detailed floor info to refine the satellite tv for pc-derived estimates of LAI.
The true check of the satellite tv for pc estimates got here from LAI knowledge Kimm measured instantly within the cornfields. Twice weekly through the 2017 rising season, he visited the fields with a specialized instrument and measured corn leaf space by hand.
Ultimately, the satellite LAI estimates from the two algorithms strongly agreed with Kimm’s “floor-reality” knowledge from the fields. This end result means the algorithms delivered correct, dependable LAI info from space, and can be utilized to estimate LAI in fields wherever on the earth in actual time.
Having actual-time LAI information could possibly be instrumental for responsive administration. For instance, the satellite methodology may detect underperforming fields or segments of fields that might be corrected with focused administration practices akin to nutrient administration, pesticide software, or different methods. Guan plans to make actual-time information obtainable to farmers within the close to future.