UAS-based multi-angular remote sensing of the effects of soil management strategies on grapevine

Abstract

<p style="text-align: justify;"><strong>Aims</strong>: The present investigation in a Luxembourgish vineyard aimed at evaluating the potential of multispectral, multi-angular UAS (unmanned aerial system) imagery to separate four soil management strategies, to predict physiological variables (chlorophyll, nitrogen, yield etc.) and to follow seasonal changes in grapevine physiology in relation to soil management.</p><p style="text-align: justify;"><strong>Methods and results</strong>: Multi-angular (nadir and 45° off-nadir) multispectral imageries (530-900 nm) were taken in the years 2011 and 2012. Image grey values and reflectance-derived vegetation indices were computed and canopy and vigour properties were monitored in the field. All four soil management strategies could be significantly discriminated (box-plots, linear discriminant analysis) and vegetation properties estimated (linear regression) in 2011. For 2012, global models predicted chlorophyll contents and nitrogen balance index values with a R²<sub>cv</sub> of 0.65 and 0.76, respectively.</p><p style="text-align: justify;"><strong>Conclusions</strong>: Soil management strategies strongly affect plant vigour and reflectance. Differences were best detectable by oblique visible/near-infrared (Vis/nIR) UAS data of illuminated canopies.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: UAS imaging is a flexible tool for applications in precision viticulture.</p>

Nutrients Assimilation and Chlorophyll Contents for Different Grapevine Varieties in Calcareous Soils in the Somontano DO (Spain)

“…This technique has been successfully used in other species [23] and, in the case of grapevine, several authors have shown that it can be used to estimate N content in the leaves [24,25]. Finally, other non-destructive techniques for vigor, LIC and other phytopathologies assessment based on drones (UAVs) or satellites are also receiving increasing attention [26,27].…”

Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

“…Despite an increased use of UAS imagery in viticulture [57], few studies have been conducted to assess the grapevine nutrient status in vineyards and fewer with remote sensing imagery [58][59][60], specifically. Most studies involving the nutrient content in vineyards limit regression modeling to the associated chlorophyll content [59,60] or nitrogen [38,60].…”

“…Despite an increased use of UAS imagery in viticulture [57], few studies have been conducted to assess the grapevine nutrient status in vineyards and fewer with remote sensing imagery [58][59][60], specifically. Most studies involving the nutrient content in vineyards limit regression modeling to the associated chlorophyll content [59,60] or nitrogen [38,60]. Moghimi et al [38] recently observed grapevine canopy reflectance with a multispectral imager aboard an unmanned aerial system (UAS) and were able to estimate the N content to within 0.23% among 150 samples with 2-4% N. Similarly, using UAS-based multispectral imagery, Retzlaff et al [60] found an RMSE of 0.26% N for samples with 1.5-3.5% N. Our slightly improved RMSE of 0.17% for leaf nitrogen among samples with a similar range of N contents suggests that there is only a modest potential for a tuned multispectral system to significantly improve the nitrogen status monitoring.…”

“…Most studies involving the nutrient content in vineyards limit regression modeling to the associated chlorophyll content [59,60] or nitrogen [38,60]. Moghimi et al [38] recently observed grapevine canopy reflectance with a multispectral imager aboard an unmanned aerial system (UAS) and were able to estimate the N content to within 0.23% among 150 samples with 2-4% N. Similarly, using UAS-based multispectral imagery, Retzlaff et al [60] found an RMSE of 0.26% N for samples with 1.5-3.5% N. Our slightly improved RMSE of 0.17% for leaf nitrogen among samples with a similar range of N contents suggests that there is only a modest potential for a tuned multispectral system to significantly improve the nitrogen status monitoring. Indeed, the R 2 found in these analogous studies (Moghimi: R 2 = 0.56 and Retzlaff: LOOCV R 2 = 0.52) were slightly higher than the model presented here (LOOCV R 2 = 0.44).…”

Recent Advances in UAS based Soil Erosion Mapping

“…Soil erosion negatively impacts crop yield, water quality, and resilience to natural disasters [1,2]. It also causes considerable soil loss and soil degradation, directly influencing crop growth, product quality, and field management [3]. As the soil provides such ecosystem services critical for life on earth, practical monitoring tools to minimize soil erosion are required.…”