Global radiation is a key climate input in process-based models (PBMs) for forests, as it determines photosynthesis, transpiration and the canopy energy balance. While radiation is highly variable at a fine spatial resolution in complex terrain due to shadowing effects, the data required for PBMs that are currently available over large extents are generally at a spatial resolution coarser than ∼9 km. Downscaling large-scale radiation data to the high resolution available from digital elevation models (DEMs) is therefore of potential importance to refine global radiation estimates and improve PBM estimations. In this study, we introduced a new downscaling model that aims to refine sub-daily global radiation data obtained from climate reanalysis data or projections at large scales to the resolution of a given DEM. First, downscaling involves splitting radiation into a direct and diffuse fraction. The influences of surrounding mountains' shade on direct radiation and the “bowl” (deep valley) effect (or sky-view factor) on diffuse radiation are then considered. The model was evaluated by comparing simulated and observed radiation at the Mont Ventoux study site (southeast of France) using the recent ERA5-Land hourly data available at a 9 km resolution as input and downscaled to different spatial resolutions (from 1 km to 30 m resolution) using a DEM. The downscaling algorithm improved the reliability of radiation at the study site, in particular at scales below 150 m. Finally, by using two different PBMs (CASTANEA, a PBM simulating tree growth, and SurEau, a plant hydraulic model simulating hydraulic failure risk), we showed that accounting for fine-resolution radiation can have a great impact on predictions of forest functions.

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R2 of 0.72, RMSE 0.41) and FCOVER (R2 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management.

Soil water content (SWC) is a key variable in many ecosystem processes as the vegetation dynamic, the biogeochemical cycles, water balance, soil physical properties. However, SWC presents very strong spatial variations linked to the heterogeneity of the soil, the vegetation,climatic conditions and relief. This variability is also temporal, linked to the temporal dynamics of plant cover and climate. In situ measurement methods are very local, requiring large, expensive and intrusive sampling. Modelling therefore remains an essential tool for providing a representation of soil moisture at scales of interest for many applications (hydrology, ecology, agronomy). Models are nevertheless relatively complex and require a large number of contextual variables, such as the local climate, plant cover and its rooting, soil properties and topography. It is therefore important to be able to surround the soil moisture measurements by the acquisition of contextual variables that allow the interpretation of the measurements and feed the models. For this purpose, research infrastructures offer favourable framework that is complementary to existing networks by producing both quality soil moisture measurements and by describing the context variables. A use case dedicated to SWC was conducted in the frame of the ENVRI-FAIR project involving the main environmental Research Infrastructures having sites measuring SWC: AnaEE, eLTER, LifeWatch, ICOS, DANUBIUS and SIOS. A first step of the use case was to identify users’ needs, by defining criteria to identify relevant datasets and useful metadata to document the datasets. A survey was done with about 100 answers. The main foreseen uses are environmental model calibration, data assimilation, remote sensing product calibration, global change studies and environmental monitoring. To use SWC data, the main information expected by the users are soil characteristics, the geolocation and the ecosystem type. Concerning the availability of contextual variables, the climate, the soil physical characteristics and ecosystem managements were mentioned as the most important. From that survey, a semantic model was proposed to determine and name the main metadata that are used by the querying tools and the dataset description. The EML standard was used for metadata discovery and the LifeWatch ERIC Metatada Catalogue was used to collect a dataset from each involved Research Infrastructure and ensure the interoperability. Even if the metadata fields provided by the EML standard sufficiently describe the SWC datasets, they have shown limitation for advanced queries on the existence of contextual variables or on the datasets exploitation metadata and thus a dedicated portal was developed for advanced searches. DCAT model and its extension developed in the frame of ENVRI-FAIR was preferred whenever possible and all metadata were gathered in a RDF triple store. The metadata collection flows and the alignment of the vocabularies were important issues. The availability of the metadata through machine to machine process was analysed and Recommendations to data providers to annotate their datasets were also given. These concerns the dataset description itself but also the site description that holds part of the useful information. An evaluation of the vocabulary alignment effort was assessed considering both automatic and the remaining manual alignments.

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.

Generation of soil water content data takes place in numerous research institutions across Europe, each arranging, describing and implementing its data collection in ways that fit purposes, conventions, standards, terminologies, and limitations specific to their case. This practice drives data consumers into a maze of portals, services, people, and ad hoc approaches, while data – when eventually obtained - is often interpretable in different ways, depending on where it comes from.The situation could improve by making data and metadata more FAIR across institutions. Hence, the effort of ENVRI-FAIR to harmonise data and services, part of which is the presented use case that gathered the contribution of six research entities: LifeWatch ERIC, AnaEE, ICOS, SIOS, DANUBIUS-RI, and eLTER. A means to improve FAIRness is the stack of semantic web technologies, which add semantics to metadata schemata, dataset structures, and data itself. Semantics allow data consumers to search for data using their preferred terms, to interpret search results correctly, and to link together data of different provenance in order to reuse it. Data querying and processing also become more meaningful with semantics, as all fields and values now refer or map to a common semantic model, creating a single network of (meta)data (Knowledge Graph). Eventually, all nodes and links of the graph carry meaning and are potentially queryable from a single endpoint, even if they may partly reside in separate repositories.The solution we implemented makes use of a semantic model, a set of queries to retrieve information from the model, and a web application to run the queries and serve the output. The model is constructed as a mash-up of new and existing semantic entities and relationships. Entities of the graph were initially defined based on the netCDF structure of the AnaEE data series, a number of complex search queries submitted in text by the project participants, and a list of concepts to describe soil data context. The entities were then connected structurally, either with OWL and RDFS properties or with ad hoc relationships, working principally in agreement with the participants, who acted as domain experts, data producers, and data consumers. Eventually, entities were mapped to or replaced by externally defined ones from well-established and recognised semantic artefacts. The reused components of the graph include ontologies, controlled vocabularies for application interoperability and for domain expertise (e.g., the AnaEE thesaurus, DCAT, GeoSPARQL), and the SKOS model for linking domain thesauri.The final service is a desktop web application – a dashboard - built with Angular framework and with a semantic graph database (GraphDB) at its backend, and serves as an entry point for (meta)data in the model. Among others, users may search for datasets by type of soil texture or pedological class in the site where data was collected, or they may search using terms from the thesaurus of their preference. Spatial search and dataset location are also enabled, while results are aggregated in tables and histograms and they are visually rendered on an interactive map.

The characteristics of the Sentinel-2 mission with a decametric resolution and frequent acquisitions allow to improve the identification of crops. The majority of the studies on crop classification using RS were targeted at herbaceous and gramineous crop classes while fewer results were obtained on woody crops which present a strong variability in management practices that make their identification difficult. Thus, this study aimed to propose a rapid, accurate, and cost-effective analytical approach for the delineation of fruit orchards (OC), vineyards (VY), and olive groves (OL) in the Mediterranean (Southern France) considering two locations. A classification based on phenology metrics (PM) de-rived from temporal Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegeta-tion indices to delineate OC, VY, and a DC class gathering all remaining surfaces. The generated PM were introduced in a random forest (RF) algorithm to identify woody crops across the two sites. The method was tested on different vegetation indices, the best results being obtained with the leaf area index (LAI). To delineate OL in the DC class, the tem-poral features of the green chlorophyll vegetation index (GCVI) were found to be the most appropriated with a typical drop of the signal during the mid-season (DOY 150-250). As a final result, we obtained an overall accuracy ranging from 89-96% and Kappa of 0.86-0.95 by considering each study site and year (2016-2021), separately. This accuracy is much better than applying the RF algorithm on the LAI times series, which led to a Kappa rang-ing between 0.3 and 0.52 and demonstrates the interest of using phenological traits rather than the raw time series of the RS data. The method can be well reproduced from one year to another. Moreover, it is possible to apply the classification model of a given year to an-other, keeping good accuracy. This is an interesting feature to reduce the burden of col-lecting ground truth information. On the contrary, the use of a classification model cali-brated in one site and applied to another led to a strong degradation of the classification accuracy. Woody crop phenology is dependent on site climatic conditions as well as the cultivar and management practices that can differ from one site to another.

Accurate data on crop canopy are among the prerequisites for hydrological modelling, environmental assessment, and irrigation management. In this regard, our study concentrated on an in-depth analysis of optical satellite data of Sentinel-2 (S2) time series of the leaf area index (LAI) to characterise canopy development and inter-row management of grapevine fields. Field visits were conducted in the Ouveze-Ventoux area, South Eastern France, for two years (2021 and 2022) to monitor phenology, canopy development, and inter-row management of eleven selected grapevine fields. Regarding the S2-LAI data, the annual dynamic of a typical grapevine canopy leaf area was similar to a double logistic curve. Therefore, an analytic model was adopted to represent the grapevine canopy contribution to the S2-LAI. Part of the parameters of the analytic model were calibrated from the actual grapevine canopy dynamics timing observation from the field visits, while the others were inferred at the field level from the S2-LAI time series. The background signal was generated by directly subtracting the simulated canopy from the S2 LAI time series. Rainfall data were examined to see the possible explanations behind variations in the inter-row grass development. From the background signals, we could group the inter-row management into three classes: grassed, partially grassed, and tilled, which corroborated our findings on the field. To consider the possibility of avoiding field visits, the model was recalibrated on a grapevine field with a clear canopy signal and applied to two fields with different inter-row management. The result showed slight differences among the inter-row signals, which did not prevent the identification of inter-row management, thus indicating that field visits might not be mandatory.

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France), which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May–October period, which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016–2020) was used. The detection mowing number was conducted at the pixel level, and then the results were aggregated at the plot level. An evaluation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94 and 0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands.

In the Mediterranean zone, the available water resources are subject to increasing tensions between users and call for a better assessment of the water consumption and use over territories, especially for agriculture. Sentinel missions now provide free remote sensing data with a high spatial and temporal resolution delivering regular information over large areas both on crop development and on the water status of various land surfaces. This study makes use of Sentinel data for two main purposes : -i) to evaluate the accuracy of new soil moisture products obtained from Sentinel 1 & 2 delivered from the THEIA platform by using soil moisture data from the monitoring of an agricultural plot located in Avignon (France) where measurement are available from several years with different sensor types; -ii) to assess the potentialities of Sentinel 1 and 2 for monitoring soil moisture/irrigation in irrigated cherry orchards in the Ouveze basin (France). Results show that THEIA derived soil moisture values are significantly correlated with in situ measurements (at 5cm depth in soil), but with variation of the relationship between years, not linked to variation in soil roughness, leading to dispersion when all years are pooled (r² ~0.25-0.36) and under-estimation of higher water contents (>0.3 m3/m3). A normalization of signal data with the yearly amplitude could improve this correlation (r²=0.36-0.55). In relation with aim (ii), temporal profiles of spectral indices obtained for several orchards with Sentinel 2 allowed to clearly identify the trees’ phenology and the impact of the inter-row management. First results also showed a medium but significant correlation (r²=0.36) between the VV polarization extracted from Sentinel 1 data at the highest incidence angle and soil moisture measured under irrigated cherries orchards. These soil measurements, combined with spectral data on a longer time interval, available for two main irrigation practices (drip and microsprinkler) will allow for a better understanding of the input of irrigation water and its use by trees at the plot scale, useful for future modeling approaches of the water balance in orchards in relation with irrigation.

Many decisions in the field of agriculture, forestry and/or hydrology can get profit from seasonal forecast. However, the skill of such forecast is a critical issue to promote their use in operational context and get profitable decisions. If many methods to assess meteorological forecast performances are available, they are mostly implemented on raw climate variables, while their implementation in sectorial application remains limited to some case studies. In this study a wide range of indicators covering most of the decision-making needs in agriculture, forestry and in some extent to hydrology were considered. These indicators are either direct climate variables, a combination of climate variables, or variables calculated by dynamic models (e.g. a crop model). The study was implemented in southern France using the Méteo-France system 6 1993-2016 hindcast, downscaled using the UERRA reanalysis and the ADAMONT methods available in CS-Tools R package developed in the frame of the MEDSCOPE project. These computed indicators need various climate variables as wind speed, radiation and air humidity while most of the downscaling methods were designed for air temperature and precipitation. The main results are the following. * We showed that all variables led to comparable level of accuracy. Seasonal forecasts provide added value compared to climatological forecasts with Brier Skill Scores between 0.05 and 0.20. * The predictability of the number of rainy days or the number of days with temperature above a threshold is comparable to those of the corresponding scalar quantities such as cumulative precipitation or mean air temperature. However seasonal forecast of extreme events such as heat waves or drought episodes was not possible. * Indicators combining several climatic information such as potential evapotranspiration or fire weather index have comparable predictability than the individual climate variables used in the calculation. * With indicators based on dynamic models, the memory effect, i.e. the effect of the system state at the beginning of the forecast period, has a strong impact on the skill scores. We propose a methodology based on an ANOVA to qualify this memory effect by using the F-value. It is shown that when the memory effect is strong (F-value >10) the seasonal forecast does not bring any added value compared to the climatological forecast. * An evaluation of the interest of a seasonal forecast in a decision-making framework was carried out by an economic approach. We have based our analysis on the decision making based on the forecast of an event. We show that there is a generic relationship between the AUC score and the gain from the forecast. We show that this relationship depends on the frequency of the decision event, the rarer the event the higher the AUC value must be to have a profitable decision. In our case, a decision based on the detection of a tercile leads to a profitable decision in more than half of the indicators while no indicator leads to a profitable decision when it is based on the detection of a quintile.

The study of ecosystem characteristics and functioning requires multidisciplinary approaches and mobilises multiple research teams. Data are collected or computed in large quantity but are most often poorly standardised and therefore heterogeneous. In this context the development of semantic interoperability is a major challenge for the sharing and reuse of these data. This objective is implemented within the framework of the AnaEE (Analysis and Experimentation on Ecosystems) Research Infrastructure dedicated to experimentation on ecosystems and biodiversity. A distributed Information System (IS) is developed, based on the semantic interoperability of its components using common vocabularies (AnaeeThes thesaurus and OBOE-based ontology extended for disciplinary needs) for modelling observations and their experimental context. The modelling covers the measured variables, the different components of the experimental context, from sensor and plot to network. It consists in the atomic decomposition of the observations, identifying the observed entities, their characteristics and qualification, naming standards and measurement units. This modelling allows the semantic annotation of relational databases and flat files for the production of graph databases. A first pipeline is developed for the automation of the annotation process and the production of the semantic data, annotation that may represent a huge conceptual and practical work without such automation. A second pipeline is devoted to the exploitation of these semantic data through the generation i) of standardized GeoDCAT and ISO metadata records and ii) of data files (NetCDF format) from selected perimeters (experimental sites, years, experimental factors, measured variables...). Carried out on all the data generated by the experimental platforms, this practice will produce semantically interoperable data that meets the linked opendata standards. The work carried out contributes to the development and use of semantic vocabularies within the ecology research community. The genericity of the tools make them usable in different contexts of ontologies and databases.

Recurrent droughts and water restrictions occur more and more often in Southeastern France. In this context, the evaluation of available resources according to the crop development and needs is becoming a priority. This study uses Sentinel data, for two main purposes: -i) evaluating the accuracy of new soil moisture products obtained from Sentinel 1 & 2 (S2MP) delivered from the THEIA platform, by using continuous soil moisture measurements of an agricultural plot located in Avignon (France) -ii) assessing the potential of Sentinel 1 & 2 for monitoring soil moisture, irrigation and phenology of irrigated orchards. Significant correlations were found between in situ measurements and soil moisture products normalized with the yearly amplitude $(\mathrm{r}^{2} =0.76\ \ \text{rmse}=0.15\%/\%)$ . Time series of the green fractional cover fCOVER and the fraction of absorbed radiation (fAPAR) computed from Sentinel 2 permitted to identify full flowering and end of flowering phenological stages when irrigation start.

Irrigation has a strong impact on water resources as groundwater. Grassland irrigation was often done using flooding technics, which mobilize large amount of water that might have effect on groundwater recharge and discharge. Mapping those irrigated grassland is therefore a crucial information to assess ground-water dynamic. Here we propose a land use classification approach based on the temporal patterns, that are specific to grassland to avoid the use of training data sets. Thanks to the frequent acquisition allowed by recent satellite missions as Sentinel 2, we used time series of leaf area index (LAI) to identify grass cuts. This approach was applied to identify irrigated permanent grasslands in the Crau area (south of France). These are regularly mown with two to four cuts during the May-October period that leads to a specific temporal pattern of LAI. An algorithm was designed to detect the number of cuts in the temporal LAI signal (see Figure 1). The algorithm includes some filtering to remove noise in the signal that might lead to false cut detection. A pixel is considered as a grassland if the number of detected cuts ranges from 2 to 4 while intensive alfalfa sometimes led to 5 cuts. A data set covering five years (2016-2020) was used. The cut number detection was done at the pixel level and then results are aggregated at the field level (120000 fields over the area). A validation data set including 800 fields was used to assess the performances of the classification. We computed the Cohen Kappa index, and obtained results ranging between 0.93-0.99 according to the year (see Table 1). These results are slightly better than other supervised classification methods that include training data sets. Grassland detection obtained with different years was used to evaluate the capacity to detect land use change. Moreover, mowing calendar can be derived and used for farming practices analysis or crop modelling over large areas than can be used to spatialize the groundwater recharge.

The study of ecosystem functioning requires multidisciplinary approaches and mobilises numerous research teams. The data produced are very abundant but their reuse and integration is often difficult due to their low level of standardisation. The development of semantic interoperability is a major challenge for the sharing and reuse of these data. This objective is implemented within the framework of the AnaEE research infrastructure dedicated to experimentation on ecosystems. The modelling of the experimental system is based on the OBOE ontology extended for disciplinary needs. It covers the measured variables, the different components of the experimental context, from sensor and plot to network, by the atomic decomposition of the observed entities, their characteristics and their qualification, the units and naming standards. This modelling allows the semantic annotation of relational databases and flat files for the production of graph databases. Carried out on all the data generated by the experimental platforms, this practice produces semantically interoperable data that meets the linked opendata standards. The work carried out contributes to the development and use of semantic vocabularies within the ecology research community.

Irrigation Advisory Services (IAS) are powerful management instruments aiming to achieve the best efficiency in irrigation water use. So far the literature on farmers’ preferences for a specific scheme design of IAS’ characteristics and the related willingness to pay (WTP) is scant. This study provides evidence on farmers’ preference towards six attributes related to the IAS configuration by using a hypothetical choice experiment. Data were collected from an original survey among 108 farmers from Spain, The Netherlands, Italy, Poland and South Africa. Moreover, we investigated the interplay between these preferences and the individual risk attitude (elicited through a lottery task) as a novel contribution. On average, the results suggest a clear farmers’ preference, especially for receiving weather forecasts from the service and for the feature related to water data recording; as the opposite, on average, crop water requirement seems irrelevant. Finally, we found that farmers’ WTP for the different IAS services varies across countries and, in some cases, also according to the individual risk attitude.

Aquifer recharge may depend mainly on the difference between precipitation and evapotranspiration. Hydrological models used to estimate groundwater reserves use evapotranspiration models that are mainly determined by climate demand. In particular, mechanisms of plant transpiration are neglected, although transpiration constitutes 70% of evapotranspiration. This is problematic when considering karst watershed, which are poorly documented at the interface between soil and atmosphere where vegetation and soil properties control water flows. To fill this gap, we propose an evapotranspiration model that integrates the processes of plant transpiration and soil evaporation. The dynamics of vegetation is evaluated using the Enhanced Vegetation Indexes from the Terra and Aqua Moderate Resolution Imaging Spectroradiometers. The soil evaporation calculation account for the impact of coarse elements at soil surface. The “Simple Crop coefficient for Evapotranspiration” (SimpKcET) model is tested at flux tower sites over forest of Font-Blanche, Puechabon and the agricultural area of Avignon. The simulated daily evapotranspirations are very close to the observations (RMSE ~0.5 mm.d-1), while the model is simple compared to other models proposed in the literature. The SimpKcET is implemented in a karst hydrological model to evaluate the impact of evapotranspiration estimation on the aquifer flow rate simulation. This approach is applied to the vast watershed of Fontaine de Vaucluse. In comparison to the water bucket model that is frequently used in karst models, SimpKcET provide ET simulations that are more in line with ET processes. A cross wavelet analysis highlighted the improvement of the simulated recharge and observed flow rate relationship brought by the consideration of evaporation and transpiration processes. The use of remote sensing data related to plant activity makes it possible to propose a parsimonious model that can be applied to all types of vegetation (agricultural, natural, mixed forest) and that can be transferred to other karst models.