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.
Highlights
Over the last two decades, precision agriculture (PA) has received significant attention in the agricultural community [1,2]
The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability
The decametric resolution satellite imagery showed some limitations in directly providing reliable information regarding the status of vineyards where the crop radiometric information can be altered by other sources that, in the case of crops grown by rows, could be predominant and could negatively affect the overall assessment
Summary
Over the last two decades, precision agriculture (PA) has received significant attention in the agricultural community [1,2]. A proper knowledge of the spatial variability between and within crop parcels is considered as a key factor for vine growers to estimate the outcomes in terms of yield and quality [5,6,7]. In this context, remote sensing (RS) has already proved its potential and effectiveness in spatiotemporal vegetation monitoring [8,9,10,11]. Among the wide set of defined spectral indices, the normalized difference vegetation index (NDVI) is one of the most extensively used, since it is strictly related to crop vigour and, to the estimated quality and quantity of field production [21,22,23,24,25]
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