Click on “Download PDF” for the PDF version or on the title for the HTML version.
If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.
Comparison of Stationary and Mobile Canopy Sensing Systems for Maize and Soybean in Nebraska, USA
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Applied Engineering in Agriculture. 38(2): 331-342. (doi: 10.13031/aea.14945) @2022
Authors: Sandeep Bhatti, Derek M. Heeren, Susan A. O’Shaughnessy, Steven R. Evett, Mitchell S. Maguire, Suresh P. Kashyap, Christopher M. U. Neale
Keywords: Center pivots, Irrigation, Multispectral, Remote sensing, Thermal, Unmanned aircraft systems.
Multispectral sensors mounted on the center pivot lateral were able to capture differences between rainfed and irrigated crop. Canopy temperature was strongly associated among stationary and pivot-mounted sensors with coefficient of determination ranging between 0.88 and 0.99. A cooling effect of about 2°C was observed in canopy temperature data collected from pivot mounted sensors for irrigated soybean crop.
Multispectral sensors mounted on the center pivot lateral were able to capture differences between rainfed and irrigated crop.
Canopy temperature was strongly associated among stationary and pivot-mounted sensors with coefficient of determination ranging between 0.88 and 0.99.
A cooling effect of about 2°C was observed in canopy temperature data collected from pivot mounted sensors for irrigated soybean crop.
Abstract. Accurate knowledge of plant and field characteristics is crucial for irrigation management. Irrigation can potentially be better managed by utilizing data collected from various sensors installed on different platforms. The accuracy and repeatability of each data source are important considerations when selecting a sensing system suitable for irrigation management. The objective of this study was to compare data from multispectral (red and near-infrared bands) and thermal (long wave thermal infrared band) sensors mounted on different platforms to investigate their comparative usability and accuracy. The different sensor platforms included stationary posts fixed on the ground, the lateral of a center pivot irrigation system, unmanned aircraft systems (UAS), and Planet (PlanetScope multispectral imager, Planet Labs, Inc., San Francisco, Calif.) satellites. The surface reflectance data from multispectral (MS) sensors were used to compute the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). The experimental plots were managed with rainfed and irrigated treatments. Irrigation was applied according to a spatial evapotranspiration model informed with Planet satellite imagery. The NDVI and SAVI curves computed from the different sensing systems exhibited similar patterns and were able to capture differences between the rainfed and irrigated treatments when the crops were approaching senescence. Strong correlations were observed for canopy temperature measurements between the stationary and pivot-mounted infrared thermometer (IRT) sensors (p-value of less than 0.01 for the correlations) when canopy were scanned with no irrigation application (dry scans). The best correlation was obtained for the irrigated maize, which yielded r2 of 0.99, RMSE of 0.4°C, and MAE of 0.3°C. The correlation for the canopy temperature data collected during dry scan between UAS and pivot-mounted thermal sensors was weak with r2 = 0.26 to 0.28, larger RMSE values of 3.7°C and MAE values of 3.4°C. Secondary analysis between thermal data from stationary and pivot-mounted IRTs collected during wet scans (during an irrigation event) demonstrated reduced canopy temperature from pivot-mounted IRTs by approximately 2°C for irrigated soybean due to wetting of the canopy by the irrigation. Understanding the performance of these sensor systems is valuable in configuring practical design and operational considerations when using sensor feedback for irrigation management.(Download PDF) (Export to EndNotes)