Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass

Abstract

Remote-sensing data has become essential for site-specific farming methods. It is also a powerful tool for monitoring the agroecosystem services offered by integrating cover crops (CC) into crop rotations. This study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs). Images captured with red–green–blue cameras mounted on UAVs in two missions varying in ground sample distances were used as input for generating three-dimensional point clouds using the structure-from-motion approach. These point clouds were then compared to manual ground measurements. The results showed that the agreement between the methods was closest when CC presented dense and smooth canopies. However, stands with rough canopies or gaps showed substantial differences between the UAV method and ground measurements. We conclude that the UAV method is substantially more precise and accurate in determining CH than measurements taken with a ruler since the UAV introduces additional dimensions with greatly increased resolution. CH can be a reliable indicator of biomass yield, but no differences between the investigated methods were found, probably due to allometric variations of different CC species. We propose the presented UAV method as a promising tool to include site-specific information on CC in crop production strategies.

Cover crop cultivars and species differ in root traits potentially impacting their selection for ecosystem services

“…Canopy cover imaging provides a facile and quantitative method for assessing plant growth and frost tolerance throughout the growing season. Remote sensing technologies such as UAVs may enable breeders and researchers to easily assess and compare growth dynamics between cover crop treatments (Kümmerer et al 2023;Trout et al 2008). Predictive models of canopy cover from cumulative growing degree days may help to develop more comprehensive temporal models of cover crop impacts on soil erosion processes.…”

Finnish Farmers Feel They Have Succeeded in Adopting Cover Crops but Need Down-to-Earth Support from Research

“…Localized economic information is essential to promote adoption [29]. Even though many farmers are, in general, very interested in using satellite and drone images to aid decision-making [63][64][65], they did not highlight remote sensing in their free word answers despite the potential.…”

Assessing relationships of cover crop biomass and nitrogen content to multispectral imagery

“…Besides VI, the incorporation of satellite-derived radar (SAR) alongside red-edge NDVI in regression models improved biomass estimations, although they still plateaued at 1.9 Mg ha −1 (Jennewein et al, 2022). SAR is utilized in remote sensing to estimate plant heights, while drone-derived point clouds have enhanced stepwise linear regression models for crop height estimation (Corti et al, 2023;Kümmerer et al, 2023;Sangjan et al, 2022). Handheld cameras have also leveraged point clouds to improve biomass estimates for grass cover crops, with improvements of up to 8.0 Mg ha −1 reported (Dobbs et al, 2023).…”

“…Handheld cameras have also leveraged point clouds to improve biomass estimates for grass cover crops, with improvements of up to 8.0 Mg ha −1 reported (Dobbs et al, 2023). Drone imagery has been used to estimate cover crop biomass and canopy height, but so far, it has been predominantly based on visual (red, green, blue, commonly known as "RGB") imagery rather than modeling with VIs (Kümmerer et al, 2023).…”