A comparison of proximal and remote optical sensor platforms for N status estimation in winter wheat

Abstract

Monitoring crop N status by means of proximal and remote sensing data can help enhancing N use efficiency at various farm scales. This study compares five optical sensor platforms, commonly used in practice and research, based on their usability and accuracy in measuring crop N status at field level. The data were gathered in 2019 in two sites in northeast Switzerland that were cropped with winter wheat (Triticum aestivum). The optical sensor platforms employed included a Sentinel-2 satellite, two different unmanned aircraft systems (UAS fixed-wing and quadcopter), a tractor-mounted system, and a handheld field spectrometer. We used a power regression to compare the measured crop N uptake with spectral vegetation indices computed from the different sensors. The reported normalized difference red-edge (NDRE) index values were distributed in a broad range from 0.17 to 0.74, with the Sentinel-2 satellite records in the higher part of the range (0.59–0.74) and those of the handheld spectrometer in the low range (0.17–0.29). The study’s key finding was the information collected was significantly different across the five sensing platforms, in terms absolute values from the sensors. However, the correlations between NDRE values from all sensors and the measured N uptake were comparably robust, with r > 0.8 a root mean square error ranging from 29 to 37 kg N/ha. Furthermore, the N application maps produced for the satellite and UAS platforms showed that the best compromise between detailed spatial resolution and matching of the working width of the machinery used was achieved by resampling the UAS-based maps at 10 m resolution with the calculation used in this study. We concluded that sensor-based N status assessment across different sensing levels can support the improvement of N use efficiency by allowing a more precise management of in-field variability, with the precondition of having a good calibration for climatic location and variety. However, factors such as the degree of detail needed to capture in-field variability while matching the working width should be evaluated for each specific case.