Potentials of crowdsourcing data for retrieving total transpirable soil water in a wine appellation
Abstract
Facing the challenges posed by climate change, quantifying the resilience of wine terroirs is imperative. Total Transpirable Soil Water (TTSW) represents the maximum water volume that vines can extract from soil, a critical parameter for water balance models. Current TTSW estimation methods often require expensive protocols, limiting their use in commercial vineyards and over large regions. This research aimed to evaluate a low-cost TTSW estimation method that combines Inverse Modelling (IM) with crowdsourced data. The chosen IM method, which has been validated locally, relies on observations of vine shoot growth (iG-Apex), weather data and satellite images. More precisely, the IM relies on a process-based relationship linking the decline in plant water status expressed as a slope coefficient, primarily inferred from plant growth and weather data, to the transpiration rate under bare soil and dry conditions, that is based on the basal crop coefficient (Kcb). By integrating a crowdsourced database, the IM method estimates TTSW at numerous locations where data were collected. A case study was carried out in a Southern French wine appellation ("Côtes du Rhône"), and a TTSW map defined by local experts was used for qualitative evaluation. The method produced TTSW estimates between 60 and 190 mm. High-confidence areas, where expert opinions converged and at least four field-level TTSW estimates existed, demonstrated coherent TTSW rankings. Two distinct geographical regions were identified: one characterised by low-to-medium TTSW and another by medium-to-high levels. This spatial differentiation suggests that crowdsourced data, when combined with inverse modelling, can effectively reveal broad-scale TTSW patterns. However, inaccurate estimates persisted in some areas. A large proportion of crowdsourced observations were excluded from the inversion due to insufficient time series length. This work demonstrates the capacity of farmer-generated plant observations to quantify the spatial patterns of a critical agronomic variable at the regional scale. The case study illustrates how farmers and viticulture professionals can contribute, through crowdsourcing, to the collective quantification of TTSW, a key variable for developing coordinated adaptation strategies in Mediterranean viticulture under climate change.
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BibTeX
@article{zhang2026potentials,
title = {{Potentials of crowdsourcing data for retrieving total transpirable soil water in a wine appellation}},
author = {Yulin Zhang and Leo Pichon and Anne Pellegrino and Bruno Tisseyre},
journal = {OENO One},
year = {2026},
month = jun,
doi = {10.20870/oeno-one.2026.60.2.9921},
url = {https://hal.inrae.fr/hal-05752168v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Potentials of crowdsourcing data for retrieving total transpirable soil water in a wine appellation
AU - Yulin Zhang
AU - Leo Pichon
AU - Anne Pellegrino
AU - Bruno Tisseyre
PY - 2026
DA - 2026/06/30
JO - OENO One
DO - 10.20870/oeno-one.2026.60.2.9921
UR - https://hal.inrae.fr/hal-05752168v1
AB - Facing the challenges posed by climate change, quantifying the resilience of wine terroirs is imperative. Total Transpirable Soil Water (TTSW) represents the maximum water volume that vines can extract from soil, a critical parameter for water balance models. Current TTSW estimation methods often require expensive protocols, limiting their use in commercial vineyards and over large regions. This research aimed to evaluate a low-cost TTSW estimation method that combines Inverse Modelling (IM) with crowdsourced data. The chosen IM method, which has been validated locally, relies on observations of vine shoot growth (iG-Apex), weather data and satellite images. More precisely, the IM relies on a process-based relationship linking the decline in plant water status expressed as a slope coefficient, primarily inferred from plant growth and weather data, to the transpiration rate under bare soil and dry conditions, that is based on the basal crop coefficient (Kcb). By integrating a crowdsourced database, the IM method estimates TTSW at numerous locations where data were collected. A case study was carried out in a Southern French wine appellation ("Côtes du Rhône"), and a TTSW map defined by local experts was used for qualitative evaluation. The method produced TTSW estimates between 60 and 190 mm. High-confidence areas, where expert opinions converged and at least four field-level TTSW estimates existed, demonstrated coherent TTSW rankings. Two distinct geographical regions were identified: one characterised by low-to-medium TTSW and another by medium-to-high levels. This spatial differentiation suggests that crowdsourced data, when combined with inverse modelling, can effectively reveal broad-scale TTSW patterns. However, inaccurate estimates persisted in some areas. A large proportion of crowdsourced observations were excluded from the inversion due to insufficient time series length. This work demonstrates the capacity of farmer-generated plant observations to quantify the spatial patterns of a critical agronomic variable at the regional scale. The case study illustrates how farmers and viticulture professionals can contribute, through crowdsourcing, to the collective quantification of TTSW, a key variable for developing coordinated adaptation strategies in Mediterranean viticulture under climate change.
SN - 2494-1271
ER -