Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

Abstract

In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant.

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…For a smaller observation area, such as a farm block, an irrigation zone, a single row of the horticultural crop, or a single canopy, this spatial resolution is considered sub-optimal. Often, a pixel of the satellite image comprises of multiple rows and multiple canopies of horticultural crops [42,56]. Thus, the spectral response on a single pixel of the satellite image includes a mixed spectral signal from the canopy, inter-row vegetation and/or bare soil.…”

“…Thus, the spectral response on a single pixel of the satellite image includes a mixed spectral signal from the canopy, inter-row vegetation and/or bare soil. The mixed-pixel is particularly unavoidable in horticultural crops with large inter-row surfaces, introducing errors in satellite-based estimations [42,56]. Improving the spatial resolution from freely available Landsat/Sentinel satellites (spatial resolution 10-15 m) to such as WorldView-3 (spatial resolution 0.3 m), does not necessarily resolve single canopies of many horticultural crops.…”

Vineyard Variability Analysis through UAV-Based Vigour Maps to Assess Climate Change Impacts

“…There have been some related works done in this area. Khaliq et al [51] compared satellite imagery with UAV-based multispectral data in four different epochs of the grapevines' vegetative cycle. Different comparisons were made by considering: (i) the whole vineyard, (ii) only the grapevines' vegetation, and (iii) only inter-row areas.…”

“…With reference to multi-temporal studies, there are those whose aim is to compare different growing seasons by evaluating biophysical grapevines parameters [54][55][56]. Furthermore, studies utilizing intra-season multi-temporal data, considered the whole vineyard information [57] or vineyard changes were not the main focus [51]. As found in Primicerio et al [22], vigour maps using only grapevines' vegetation showed a better representation of the variability within the vineyard.…”

Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

“…UAV data creates new opportunities in agriculture [18][19][20], e.g., assessment of plant health status [21], water stress [22], management techniques [23], erosion of soils [24], and detection of individual plants [25], among many others. The advantages of VHR data sets compared to common satellite data sets have also been analyzed, e.g., for vegetation indices (VI) [26,27]. Further, UAVs have the ability to acquire data in a very flexible manner, e.g., by considering changing weather conditions, and they are cheap to operate compared to other carrier systems [17].…”