Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques

Abstract

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures

“…The spectral information of the orthomosaics was used to calculate a set of common VIs. Thirteen VIs using visible and red edge as well as NIR reflectance were selected (S2 Table), which were reported in literature for structural or biochemical characteristics of vegetation and grasslands [31,35]. VIs were calculated in R (R 3.5.1, R Foundation for Statistical Computing, Vienna, Austria) based on the mean value of the original reflectance of the spectral bands for each plot.…”

“…RF, introduced by Breiman [59], builds multiple decision trees for regression with a random selection of sub-datasets as input variables. Both regression algorithms, PLS and RF, are common techniques for spectral analysis, including highly correlated independent variables, to estimate biomass yield and quality, also in the field of grassland and forage production [33,35,60].…”

“…In the recent decades more than 100 VIs were developed, which vary in calculation and proportion of different spectral bands for specific applications and utilization for biophysical and -chemical features of vegetation [31]. For estimating different vegetation features in grassland, VIs are already well-studied tools like for biomass yield [32,33], leaf area index (LAI) [34] and quality [35].…”

Comparison of Spectral Reflectance-Based Smart Farming Tools and a Conventional Approach to Determine Herbage Mass and Grass Quality on Farm

“…For this study, the platform was employed between 2 January 2019 and 2 July 2019. The MSI-model development was based on data of UAV surveys above field plots and grazed paddocks containing mainly perennial ryegrass and clover mixtures at Moorepark (Teagasc Research Centre, Fermoy, Cork, Ireland) on six days in 2017 and 2018 [31]. The calibration range for HM is 304.6 to 2435.7 kg dry matter (DM) ha −1 and for crude protein (CP) 126.3 to 247.3 g kg −1 DM.…”

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

“…However, the parch marks in survey 1 appear to lie mainly in areas of depression where historic robbing of the walls following ruination of the priory has in all probability resulted in a slight lowering of the mean Visual assessment of the multispectral imagery, and the derived NDVI, reveal the southern part of the west range with considerable clarity and some of the other features, visible as parch marks during dry conditions in the RGB data for the first survey, are visible to some extent ( Figure 13). The contrast in the appearance of the grass above the masonry walls is most likely due to variation in chlorophyll displaying differential reflectance in the red and near-infrared wavelengths [115,116]. Thus, vegetation above masonry walls which is adversely affected by lower water availability, and therefore may contain less chlorophyll, is revealed in the multispectral data with far greater clarity than in the RGB region alone.…”