Abstract
Climate change will exacerbate environmental threats, which will, in turn, affect agricultural production patterns. This study addresses the feasibility of using Sentinel-2 (S2) optical remote sensing data to map the consequences of heatwaves on vineyard plots. The proposed method to map damaged and undamaged vineyards is based on the use of an inter-annual (C1) and an intra-annual (C2) criterion derived from the Normalized Difference Vegetation Index (NDVI) calculated on S2 data. While the inter-annual criterion compares the NDVI of the heatwave year to the average NDVI of the previous years with no heatwave, the intra-annual criterion compares the NDVI values before and after the heatwave in the same year. Predictions from either criteria or both combined were tested against two datasets collected during two field surveys performed in 2020 and 2021, with different ways of recording the damage caused on the grapevine by the 2019 heatwave in southeastern France. Results showed that within the reference vineyard plots with heat damage, 46 %, 62 % and 40 % of the S2 pixels were correctly predicted as damaged using C1, C2 and their combination, respectively. Within undamaged plots, 91 %, 88 % and 99 % of the S2 pixels were correctly predicted as undamaged using C1, C2 and the combination, respectively. Results also showed that only severe leaf damage was detected using the S2 NDVI. The combination of C1 and C2 provides the most accurate detection of heatwave consequences on vineyards.
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Claims 8
Claim · model-written summary
The inter-annual criterion correctly detected 46 % of damaged vineyard pixels.
Verbatim
e0“within the reference vineyard plots with heat damage, 46 %, 62 % and 40 % of the S2 pixels were correctly predicted as damaged using C1, C2 and their combination, respectively”
t0“vineyards”
Claim · model-written summary
The intra-annual criterion correctly detected 62 % of damaged vineyard pixels.
Verbatim
e0“within the reference vineyard plots with heat damage, 46 %, 62 % and 40 % of the S2 pixels were correctly predicted as damaged using C1, C2 and their combination, respectively”
t0“vineyards”
Claim · model-written summary
The combined criteria correctly detected 40 % of damaged vineyard pixels.
Verbatim
e0“within the reference vineyard plots with heat damage, 46 %, 62 % and 40 % of the S2 pixels were correctly predicted as damaged using C1, C2 and their combination, respectively”
t0“vineyards”
Claim · model-written summary
The inter-annual criterion correctly detected 91 % of undamaged vineyard pixels.
Verbatim
e0“Within undamaged plots, 91 %, 88 % and 99 % of the S2 pixels were correctly predicted as undamaged using C1, C2 and the combination, respectively”
t0“vineyards”
Claim · model-written summary
The intra-annual criterion correctly detected 88 % of undamaged vineyard pixels.
Verbatim
e0“Within undamaged plots, 91 %, 88 % and 99 % of the S2 pixels were correctly predicted as undamaged using C1, C2 and the combination, respectively”
t0“vineyards”
Claim · model-written summary
The combined criteria correctly detected 99 % of undamaged vineyard pixels.
Verbatim
e0“Within undamaged plots, 91 %, 88 % and 99 % of the S2 pixels were correctly predicted as undamaged using C1, C2 and the combination, respectively”
t0“vineyards”
Claim · model-written summary
S2 NDVI detected only severe leaf damage.
Not reportedComparator
Verbatim
e0“only severe leaf damage was detected using the S2 NDVI”
t0“vineyards”
Claim · model-written summary
The combined criteria gave the most accurate detection of heatwave effects on vineyards.
Verbatim
e0“The combination of C1 and C2 provides the most accurate detection of heatwave consequences on vineyards”