Use of Sentinel-2 Derived Vegetation Indices for Estimating fPAR in Olive Groves

Abstract

Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where the increasing impact of climate change coupled with the inefficient management of olive groves is negatively affecting olive oil production and quality in some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing olive grove management and final production. However, the spatial resolution of freely-available satellite products is not yet adequate to estimate plant biophysical parameters in complex agroecosystems such as olive groves, where both olive trees and grass cover contribute to the vegetation indices (VIs) signal at pixel scale. The aim of this study is therefore to test a disentangling procedure to partition the VIs signal among the different components of the agroecosystem to use this information for the monitoring of olive growth processes during the season. Specifically, five VIs (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) as recorded by Sentinel-2 at a spatial resolution of 10 m over five olive groves in the Montalbano area (Tuscany, Central Italy), were tested as a proxy for olive tree intercepted radiation. The olive tree volume per pixel was initially used to linearly rescale the VIs signal into the relevant value for the grass cover and olive trees. The models, describing the relationship between rescaled VIs and observed fraction of Photosynthetically Active Radiation (fPAR), were fitted and then validated against independent datasets. While in the calibration phase, a greater robustness at predicting fPAR was obtained using NDVI (r = 0.96 and RRMSE = 9.86), the validation results demonstrating that GEMI and MCARI2/OSAVI provided the highest performances (GEMI: r = 0.89 and RRMSE = 21.71; MCARI2/OSAVI: r = 0.87 and RRMSE = 25.50), in contrast to MCARI2 that provided the lowest (r = 0.67 and RRMSE = 36.78). These results may be related to the VIs’ intrinsic features (e.g., lower sensitivity to atmosphere and background effects), which make some of these indices, compared to others, less sensitive to saturation effects by improving fPAR estimation (e.g., GEMI vs. NDVI). On this basis, this study evidenced the need to improve the current methodologies to reduce inter-row effects and select appropriate VIs for fPAR estimation, especially in complex agroecosystems where inter-row grass growth may affect remote sensed-derived VIs signal at an inadequate pixel resolution.

Use of remote sensing-derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub-field level

“…In this regard, one of the main results of this study is the application and validation of a methodology to disentangle the contribution of vine canopy and the inter-row to the NDVI value from RS, thus enabling the effective integration of satellite data into a crop growth model. The proposed approach, originally developed by Maselli (2001) for different soil cover classes, which was already successfully applied to olive orchards (Maselli et al, 2012;Leolini et al, 2022), is based on the use of maps that spatially describe the distribution of fraction cover. A single high-resolution UAV image (~ 3 cm) acquired around fruit-set (DOY 179) was thus used in site B to segment the grapevine canopy cover and to evaluate the actual contribution of grapevine plants to the NDVI signal at single pixel scale (Fig.…”

“…From a purely operational point of view, the model can be forced only after evaluation of the fraction cover of vine canopies, which is needed to rescale NDVI and to extract fPAR at the fruit-set stage. While this fraction can be assumed almost constant in time and space in a mature olive grove (Leolini et al, 2022;Maselli et al, 2012;Moriondo et al, 2019), this does not hold in a vineyard and must be evaluated seasonally. This implies the retrieval of detailed maps to drive NDVI signal partitioning (Fig.…”

“…GEMI, MSAVI, OSAVI, etc. ), thus being less sensitive to saturation and showing up the differences in high vine vigor zones (Haboudane et al, 2004;Leolini et al, 2022;Pinty & Verstraete, 1992;Rondeaux et al, 1996).…”

“…Considering that plant canopy development and their light interception capability assume a relevant role in physiological processes such as transpiration and photosynthesis (Mabrouk et al, 1997;Mezghani et al, 2016), many efforts focused on deriving leaf area index (LAI) values or fPAR (fraction of the Photosynthetically Active Radiation) estimates from RS vegetation indices to feed crop models. Despite the approach being successfully adopted for sowing crops such as rice (Gilardelli et al, 2019;Setiyono et al, 2018), wheat (Guo et al, 2018;Moriondo et al, 2007), soybean (Basso et al, 2001) and maize (Fang et al, 2008(Fang et al, , 2011, it has been still little applied on perennial crops with a few exceptions like olive tree (Leolini et al, 2022;Maselli et al, 2012) and jujube fruit tree (Bai et al, 2019). This is likely due to the inherent difficulty in disentangling the relative contribution of the various components of the agro-ecosystem to the vegetation index signal: the tree canopy and herbaceous layer (Maselli et al, 2012).…”

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

“…The NDVI is the most commonly used indicator of vegetation greenness/vitality, showing strong correlations with Leaf Area Index (LAI) and green biomass, providing information for estimating Net Primary Production and enabling us to distinguish vegetation from non-vegetation [ 51 ]. The OSAVI can be used to reduce the effect of soil background on sparse and dry vegetation [ 52 ]. The GNDVI is highly sensitive to chlorophyll and reduces non-photosynthetic effects, which can provide valuable information on complex landscapes [ 53 ].…”