Delineation of Orchard, Vineyard, and Olive Trees Based on Phenology Metrics Derived from Time Series of Sentinel-2

Abstract

This study aimed to propose an accurate and cost-effective analytical approach for the delineation of fruit trees in orchards, vineyards, and olive groves in Southern France, considering two locations. A classification based on phenology metrics (PM) derived from the Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegetation indices to delineate orchard and vineyard classes. The generated PM were introduced into a random forest (RF) algorithm for classification. The method was tested on different vegetation indices, with the best results obtained with the leaf area index. To delineate the olive class, the temporal features of the green chlorophyll vegetation index were found to be the most appropriate. Obtained overall accuracies ranged from 89–96% and a Kappa of 0.86–0.95 (2016–2021), respectively. These accuracies are much better than applying the RF algorithm to the LAI time series, which led to a Kappa ranging between 0.3 and 0.52 and demonstrates the interest in using phenological traits rather than the raw time series of the remote sensing data. The method can be well reproduced from one year to another. This is an interesting feature to reduce the burden of collecting ground-truth information. If the method is generic, it needs to be calibrated in given areas as soon as a phenology shift is expected.

Benchmarking the Reliability of Sentinel-2 Satellite Data for Estimating Vineyard NDVI and Leaf Area Index Parameters through UAV LiDAR and Multispectral Imagery

“…However, when it comes to woody crops planted in hedgerow systems, the literature indicates some challenges. Although advances have been made [3,19,20], concerns about the accuracy of Sentinel-2 data in these systems persist. A study by Sozzi et al [21] found a strong correlation between Sentinel-2 and UAV data at both the field and sub-field scales.…”

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

“…Guermazi et al [108] faced difficulties in capturing phenological changes due to local cloudiness, which limited the effectiveness of medium-spatial-resolution imagery in specific orchard types. Abubakar et al [15] reported classification challenges, particularly in young orchards and heterogeneous plots. The interference arising from sparse canopies and age differences in trees underscores the need for improved classification methodologies customized for diverse orchard conditions.…”

“…Providing non-destructive methodologies with high spatial, radiometric and temporal resolutions, RS allows the characterization and monitoring of spatiotemporal variability for multidimensional purposes [9][10][11][12][13]. Over the past few decades, RS technology has played a crucial role in the development of new agricultural applications, focusing primarily on monitoring vegetation cover [14][15][16], assessing crop vigor conditions [17][18][19], estimating nutrient and water status [20][21][22], determining crop evapotranspiration (ET c ) [23][24][25], identifying and managing invasive plants [26][27][28], detecting and monitoring pest/diseases [29][30][31] and forecasting crop yields [32][33][34]. The effectiveness of RS applications in agriculture depends on several fundamental factors, including the choice of sensing platform, which can be a satellite, aircraft, unmanned aerial vehicle (UAV) or terrestrial platform; the segment of the electromagnetic spectrum used; the number and range of spectral bands; spatial, temporal and radiometric resolutions; and the energy source (passive or active sensors) [9].…”

Grass Cover, Tree Density, and Leaf Development of Mediterranean Orchards From High Resolution Data

“…With the arrival of new data at increasingly fine spatial and temporal resolutions such as the Sentinel missions from Copernicus program or Pleiades and SkySat images, it becomes possible to monitor crops systems with more and more accuracy (Jafarzadeh and Attarchi, 2023). Thus (Abubakar et al, 2023) have shown that orchards can be well classified from Sentinel 2 data using deep learning methods. The combination of both, high temporal and spatial resolution of Sentinel 2 images (pixel 10m and time revisit 3-5 days), enables to monitor in-season vegetation phenology through the analysis of time-series.…”