# Out-of-year parcel identification using adversarial domain adaptation and multi-temporal satellite imagery to enhance vineyard health monitoring

Anna Roussel, Samuel Alleaume, Adrien Billotte, Frédéric Fabre, Dino Ienco

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Anna Roussel, Samuel Alleaume, Adrien Billotte, Frédéric Fabre, Dino Ienco. Out-of-year par-cel identification using adversarial domain adaptation and multi-temporal satellite imagery to enhance vineyard health monitoring. International Journal of Digital Earth, 2026, 19 (1), 10.1080/17538947.2026.2634468. hal-05523426

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Submitted on 23 Feb 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Out-of-year parcel identification using adversarial domain
adaptation and multi-temporal satellite imagery to enhance
vineyard health monitoring

Anna Roussel¹, Samuel Alleaume ¹ID, Adrien Billotte², Frédéric Fabre ³ID, and Dino Ienco

ID 1,4

¹INRAE, UMR TETIS, Univ. Of Montpellier, Montpellier, France

²GDON des Sauternais et des Graves, Podensac, France

³INRAE, SAVE, Villenave-d'Ornon, France

⁴INRIA, EVERGREEN, Univ. of Montpellier, Montpellier, France

Abstract

Accurate monitoring of vineyard parcel status is essential for precision viticulture and phytosanitary risk management, particularly for detecting abandoned parcels that may act as disease reservoirs for pathogens such as Flavescence dorée. Conventional monitoring methods are limited by restricted spatial coverage and low temporal frequency, making satellite remote sensing a key alternative for large-scale vineyard monitoring. This study addresses the challenging task of out-of-year vineyard parcel classification, which requires models to generalize across years with varying environmental and climatic conditions. We propose a deep learning framework based on conditional adversarial learning to address temporal domain adaptation in multi-year satellite data. The approach relies on freely available Sentinel-1 (radar) and Sentinel-2 (optical) imagery. The framework is evaluated through a real-world case study covering the Graves and Sauternes vineyards in western France. The dataset includes ground truth observations spanning five consecutive years (2020–2024), provided by the GDON du Sauternais et des Graves. Results show that the proposed method outperforms standard approaches in handling temporal domain shifts, with weighted F1-score improvements exceeding 5 percentage points overall and more than 10 percentage points for minority class detection, despite class imbalance and interannual environmental variability.

**Keywords**— Out-of-Year prediction, Vineyard parcel classification, Deep Learning, Domain Adaptation
# 1 Introduction

The Digital Earth paradigm, through the access of satellite remote sensing data collected by modern Earth observation platform, has fundamentally transformed our approach to understanding and managing terrestrial systems through the integration of multi-dimensional geospatial data, advanced computational methods, and enhanced monitoring capabilities [4]. Within this context, precision viticulture represents an important application domain where satellite remote sensing technologies can address critical challenges for sustainability, environmental monitoring, and risk management [13].

Precision viticulture has emerged as a critical approach for sustainable vineyard management, enabling farmers to optimize practices while addressing environmental and economic challenges [3]. Among the various applications of precision agriculture, the accurate monitoring of vineyard parcel status plays a fundamental role in maintaining both productivity and ecosystem health [26]. Building integrated vineyard, and more generally crop, health surveillance requires access to realistic plant hosts maps with high spatial and temporal resolution. Such maps should represent key features, such as the size, shape and relative locations of individuals parcels and wild patches of host plants [25]. More specifically, the identification of potential inoculum reservoirs, whether cultivated or non-cultivated plants, acting as shelters for pathogens and pests into disease surveillance strategies and risk assessment is of particular interest [37].

Among the operational categories to recognize in the vineyards, the monitoring of abandoned parcels has gained particular importance due to their role as potential reservoirs for plant diseases [27]. The management of Flavescence dorée (FD) is particularly affected by this issue. FD is a severe disease of grapevine that spreads in European vineyards since the 1950’s. It is caused by a phytoplasma, a small bacterium which multiplies within the phloem sap of infected grapevines. FD phytoplasma is transmitted between grapevines by the leafhopper vector *Scaphoideus titanus* Ball [6]. Due to the epidemic spread of FD, the absence of curative treatments and the resulting yield losses, FD phytoplasma has been classified as a quarantine organism in Europe since 1993. The management of the disease relies principally on four compulsory measures: (i) planting disease-free material, (ii) applying insecticides to control the vector population, (iii) monitoring plant infections through annual vineyard surveys, and (iv) removing infected plants [28]. This strategy, applied to the cultivated parcels, can be undermined by the presence of abandoned vines [21]. Abandoned vineyard plots can serve as refuges for the phytoplasma and its insect vectors [6]. Being unmanaged, they are creating focal points for pathogen spread to cultivated vineyards, at least in their close neighboring as most infections occur within a few dozen metres of an infected plant [1].

Traditional monitoring approaches for vineyard parcel classification, based on either ground surveys or airborne remote sensing data, face significant limitations in terms of spatial coverage, temporal frequency, and operational costs [38, 30]. Ground surveys, while providing high accuracy at the local scale, are time-consuming and economically prohibitive for large-scale monitoring programs. Similarly, airborne remote sensing data acquired by airplane or UAVs (unmanned aerial vehicle) provide very high spatial resolution imagery but only cover limited and specific geographical
areas [38, 9], with additional uncertainty regarding the acquisition of consistent data across multiple years. Moreover, vineyard landscapes are inherently dynamic, as individual plots may be newly planted, uprooted, abandoned, or converted to other crops from one year to the other. Capturing these temporal changes in parcel status requires systematic monitoring, making both ground surveys and airborne remote sensing approaches increasingly impractical for comprehensive regional assessments.

Satellite remote sensing technologies offer a compelling alternative for large-scale vineyard monitoring, providing spatial coverage, temporal consistency, and cost-effective data acquisition [18]. Modern Earth observation platforms, particularly the European Space Agency's Copernicus programme ¹, have revolutionized agricultural monitoring capabilities through the acquisition of complementary information, both radar (Sentinel-1) and optical (Sentinel-2), at high spatial (10 m) and temporal (every 5/6 days) resolution.

Recent advances in machine learning and deep learning approaches have demonstrated remarkable potential for the analysis and understanding of satellite imagery collected by remote sensors [40]. These approaches can effectively exploit the rich spectral, temporal, and spatial information contained in satellite time series to distinguish between different land cover types [16]. However, the practical implementation of machine learning-based vineyard classification systems faces significant challenges, particularly when dealing with rare land use classes such as abandoned parcels. While in the general context of agricultural monitoring systems, the use of satellite remote sensing data is gaining more and more attention to characterize crop and farmland abandonment [34, 35, 10, 15, 12], it is still under-explored in the context of vineyard classification systems, more precisely abandoned vineyard parcels detection.

The inherent class imbalance in vineyard landscapes, where cultivated parcels typically dominate the spatial extent, creates substantial difficulties for standard supervised learning algorithms [43]. This challenge is further compounded by the limited availability of labeled training data for under-represented classes, necessitating costly and time-consuming field campaigns and multi-annual data collection strategies.

The integration of multi-year satellite data, while addressing the sample size limitations for rare classes, introduces additional complexity through temporal heterogeneity in acquisition conditions. Variations in atmospheric conditions, sensor calibration, plant phenological timing, and environmental factors between different years can induce significant distribution shifts in the underlying data characteristics. These domain shifts pose substantial challenges for conventional machine learning approaches [39], which typically assume that training and test data are drawn from the same distribution [36]. When this assumption is not met, model performance can degrade significantly, particularly affecting the classification accuracy of minority classes.

Domain adaptation techniques [41] have emerged as a promising solution to address these challenges by explicitly modeling and compensating for distribution shifts between different domains. These advanced methods aim to learn domain-invariant representations that maintain classification performance across varying acquisition conditions while preserving the discriminative power necessary for accurate land cover classification [5]. Recent developments in adversarial domain adaptation [14] offer new possibilities for robust multi-year vineyard monitoring systems.

In contrast to previous research studies that primarily relied on airborne remote sensing data [38] and single-year datasets [9], this research study investigates the potential of satellite multi-source remote sensing data for the
challenging task of out-of-year vineyard parcel status classification. In this setting, remote sensing and ground truth
data from multiple years are available, and the objective is to build a classification model capable of generalizing to
new, unseen time periods that may be characterized by different environmental and climatic conditions.

To address this temporal domain adaptation challenge, we design a deep learning framework inspired by conditional
adversarial learning mechanisms [23]. This approach, referred to as *ConDA-Vine* (Conditional Domain Adaptation
for Vineyard Parcel Classification), is specifically designed to bridge the temporal gap between training and target
datasets, enabling robust out-of-year predictions.

We evaluate our methodology using a real-world case study covering approximately 6400 hectares in the Graves and Sauternes region, located in the south-east of Bordeaux (France). The study area is monitored for FD management by the GDON du Sauternais et des Graves (Groupement de Défense contre les Organismes Nuisibles du Sauternais et des Graves), a regional professional organization. The GDON surveys the entire study area with systematic annual field campaigns spanning the period 2020-2024, resulting in extensive ground truth data with three-class categorization (*cultivated*, *uprooted*, and *abandoned*) for each vineyard parcel. This comprehensive temporal dataset provides an ideal testbed for testing the quality of our proposed domain adaptation approach and compare its behaviour against several standard approaches usually employed in the field of land use and land cover classification from remote sensing data.

The rest of this manuscript is organized as follows. Section 2 presents the case study and the associated remote sensing and ground truth data. Section 3 describes the out-of-year classification problem setting and introduces the proposed *ConDA-Vine* framework. The quantitative and qualitative experimental evaluation is reported in Section 4 while Section 5 proposes a discussion summary of the obtained results. Section 6 draws final conclusions on the work.

2 Study area and Data description

2.1 Study Area

The case study covers the Graves and Sauternes vineyard area, located in south-east part of the county of Gironde,
close to Bordeaux (France). The area spans an extent of 6 414 ha over 10,152 parcels. This area has been prone to the
development of *Flavescence Dorée* (FD) disease [29]. Legislation requires winemakers to uproot infected grapevine
plants (and even to uproot field with FD prevalence ≥ 20%)². While cultivated parcels are regularly surveyed for
monitoring plant infection, abandoned parcels are not included in monitoring schemes, partly because they rapidly
become inaccessible to surveyors, yet they may constitute reservoirs for the FD phytoplasma and its insect vector.
Figure 1 presents the study area with the 2024 parcel registry superposed on a Sentinel-2 image of April 19th, 2024.

## 2.2 Satellite data

We collected satellite image time series of Sentinel-1 and Sentinel-2 imagery via the Microsoft Planetary Computer
platform ³ from the period 2020 to 2024.
<figure><img src="image_2.png" /><figcaption>Figure 1: View and location of the Graves and Sauternes vineyard area. The 2024 parcel registry is superposed to a Sentinel-2 image covering the entire area. In the blue box (middle right) a zoom on a detail of the study area is depicted.</figcaption></figure>

### 2.2.1 Sentinel-2 imagery

Optical data from Sentinel-2 (S2) spanning the period 2020-2024 was utilized, specifically level-2A Sentinel-2 products. All bands at 10 m and 20 m of spatial resolution for a total of 10 bands per image were considered (B02, B03, B04, B05, B06, B07, B08, B8A, B11, B12). To maintain consistent spatial resolution across all spectral bands, the SWIR 20 m bands were upsampled to 10 m resolution. Given Sentinel-2's five-day revisit period, approximately 73 images are typically available for any given location each year. However, cloud coverage necessitated the removal of certain images from the dataset. To minimize cloud contamination, a two-stage filtering approach was implemented.

First, a tile-level filter was applied using the cloud cover percentage recorded in the image metadata, retaining only images with less than 75% cloud coverage (Figure 4). Second, a more selective region-specific filter was implemented to limit cloud coverage over the study area. Using the Scene Classification Layer (SCL) associated to the level-2A Sentinel-2 product, the cloud cover percentage was calculated specifically within the region of interest for each image. Images exhibiting cloud coverage exceeding 20% over the study area were subsequently discarded. Figure 2 illustrates the final number of Sentinel-2 images retained for each year and their temporal distribution throughout the study period.
<figure><img src="image_3.png" /><figcaption>Figure 2: Temporal distribution of Sentinel-2 images retained after the two-stage filtering approach. Per year details are reported over the five years considered in our research study: 2020, 2021, 2022, 2023 and 2024.</figcaption></figure>

### 2.2.2 Sentinel-1 imagery

Acquisitions from Sentinel-1 (S1) sensor at 10 m spatial resolution and Radiometrically Terrain Corrected (RTC) were utilized for this analysis. Given the temporal period from 2020 to 2024 and the cessation of Sentinel-1B operations in 2021, only data from Sentinel-1A (S1A) was employed to ensure temporal consistency across all years. With Sentinel-1A's 12-day revisit period, between 28 and 29 images are typically available per year for any given location. However, due to processing constraints, only the first 22 images for 2024 were available at the time of analysis. To maintain consistency across the five-year study period, the first 22 radar images from each year were selected for processing as illustrated in Figure 3. Both vertical-vertical (VV) and vertical-horizontal (VH) polarizations were incorporated for each image acquisition.

<figure><img src="image_4.png" /><figcaption>Figure 3: Temporal distribution of Sentinel-1 images for each year. Per year details are reported over the five years considered in our research study: 2020, 2021, 2022, 2023 and 2024.</figcaption></figure>
2.3 Optical temporal resolution and satellite data pre-processing

Acquisition dates of optical images (Sentinel-2) varied between years, and cloud coverage resulted in data gaps during certain winter months. To achieve temporal homogeneity within each annual period, two scenarios were considered, each providing different temporal resolutions for the time series analysis. A first scenario employed multi-temporal linear interpolation preprocessing as described in [17], generating gap-filled images at regular 5-day intervals. This resulted in a sequence of 48 images for each year, spanning the period from April to October, and is referred to as the *Whole TS* (Annual/Whole Time Series) scenario. The total data volume of gap-filled imagery, over the period 2020-2024, has a size of 50Gb. A second scenario involved computing monthly composite images based on the average of all available images within each given month. This corresponds to the data processing level L3A defined by international space agencies for releasing aggregated Earth Observation products ⁴ The final collection of optical images for each year comprised 6 images spanning the period from April to October, with September excluded. September was omitted because all September 2024 images were discarded due to excessive cloud coverage. The total data volume of monthly time series imagery, spanning the period 2020-2024, has a size of 6.25Gb. This approach is referred to as the *Monthly TS* (Monthly Time Series) scenario and serves as the default scenario adopted throughout the remainder of this study. The rationale for selecting this latter scenario as the default one are introduced in Section 4.3. Given that the analysis was conducted at the parcel level, aggregated descriptors were extracted for each parcel, with each descriptor representing the mean pixel values of a particular radiometric band within that parcel. Alternative statistics such as median and standard deviation were tested but provided no additional value; therefore, to reduce computational burden, only mean statistics were retained. The same aggregation process was employed for the radar Sentinel-1 multi-temporal data. A visual summary of the Sentinel-1 and Sentinel-2 data pre-processing workflow is illustrated in Figure 4.

<figure><img src="image_5.png" /><figcaption>Figure 4: Visual summary of the Sentinel-1 and Sentinel-2 data pre-processing, from the data access phase to the construction of the multi-source dataset associated to the considered case study.</figcaption></figure>
## 2.4 Ground Truth Data

Field observations and labeling were conducted by the GDON du Sauternais et des Graves (Organization for the Defense Against Pests) over a five-year field campaign covering the period from 2020 to 2024.

Sentinel-2 products are delivered as image tiles. The tiling system divides the Earth into fixed 110 km × 110 km tiles identified by a unique alphanumeric code. Each tile is referenced to a UTM projection and is invariant over time, enabling consistent spatial indexing and multi-temporal analysis across acquisitions ⁵. Therefore, only parcels intersecting the same Sentinel-2 tile (30TYQ) were utilized, representing 94% of all available GDON data.

Within this dataset, parcels with extents smaller than 100 m² were excluded, as these correspond to areas smaller than a single Sentinel-2 pixel. Following this filtering process, the final labeled dataset comprised 2537 parcels for 2020, 6176 parcels for 2021, 8320 parcels for 2022, 10018 parcels for 2023, and 10152 parcels for 2024. Parcel sizes ranged from 105 m² to 75529 m², with 80% of fields being smaller than 10 000 m².

The GDON classification system distinguishes three main parcel types: *cultivated*, *uprooted*, and *abandoned*. *Cultivated* parcels represent plots where vines are actively grown. *Uprooted* parcels correspond to plots where vines have been recently removed for various reasons, including disease, land inheritance issues, and other factors. *Abandoned* parcels represent plots that are no longer actively cultivated and monitored, though they may contain residual vines alongside other vegetation types.

The class distribution across different years is presented in Table 1. While the *cultivated* class remains over-represented across all years, the *abandoned* class is significantly under-represented, exhibiting extreme class imbalance with a representation ranging from 2% in 2020 to 3.5% in 2024.

<table><caption>Table 1: Class distribution of the vine parcel across the different years.</caption><thead><tr><td>Year</td><td>Cultivated</td><td>Uprooted</td><td>Abandoned</td><td>Total</td></tr></thead><tbody><tr><td>2020</td><td>2314</td><td>167</td><td>56</td><td>2537</td></tr><tr><td>2021</td><td>4850</td><td>1039</td><td>287</td><td>6176</td></tr><tr><td>2022</td><td>6696</td><td>1320</td><td>304</td><td>8320</td></tr><tr><td>2023</td><td>8243</td><td>1470</td><td>305</td><td>10018</td></tr><tr><td>2024</td><td>7964</td><td>1833</td><td>355</td><td>10152</td></tr></tbody></table>

## 3 Methodology

In this section we introduce our proposed framework, *ConDA-Vine*, a neural network based approach especially designed for out-of-year vineyard parcel status classification allowing to combine multi-source satellite data coming from different years, together. More precisely, we firstly introduce the problem setting related to the out-of-year classification task, secondly we supply an overview of *ConDA-Vine*. Subsequently, we provide details about the different components on which *ConDA-Vine* is built on.
## 3.1 Out-of-year classification problem

In this work, we address the problem of out-of-year classification. We have access to a source dataset $\mathcal{S} = \{\mathcal{D}_i\}_{i=1}^{|\mathcal{M}|}$ that spans $|\mathcal{M}|$ years of data, with $\mathcal{M}$ the set of years covered by $\mathcal{S}$. Each $\mathcal{D}_i$ represents the set of labeled data associated with year $i$, defined as $\mathcal{D}_i = \{(x_j^i, y_j^i)\}_{j=1}^{|\mathcal{D}_i|}$. According to the domain adaptation literature [41], we also refer to each $\mathcal{D}_i$ as a *domain*. Additionally, we have a target dataset $\mathcal{D}_t$ that covers data from a single year and contains only unlabeled samples: $\mathcal{D}_t = \{x_j^t\}_{j=1}^{|\mathcal{D}_t|}$, where $\mathcal{D}_t \notin \mathcal{S}$. Our objective is to learn a classifier $f(\cdot)$ using the source labeled datasets $\mathcal{S}$ that can generalize to the target data $\mathcal{D}_t$. The main challenge lies in combining data from multiple heterogeneous years (domains) that may be affected by shifts in the underlying data distributions. The goal is to build a classification model robust enough to generalize to a new target dataset from a different year. In machine learning, computer vision, and pattern recognition, this setting is commonly known as multi-source unsupervised domain adaptation [42].

## 3.2 ConDA-Vine overview

In this section, we provide an overview of our framework, describing how *ConDA-Vine* operates and the underlying principles that guide its design and learning process. Figure 5 illustrates the *ConDA-Vine* framework, which consists of two main parts: a training phase and an inference phase. The *ConDA-Vine* architecture comprises three key components: an encoder $g(\cdot)$, a task classifier head $h_c(\cdot)$, and a domain classifier head $h_d(\cdot)$. The task classifier head identifies which of the three vineyard classes (*cultivated*, *uprooted*, and *abandoned*) an input sample belongs to, with output dimension $h_c(\cdot) \in \mathbb{R}^3$. Meanwhile, the domain classifier head distinguishes the year from which the input sample originates among $|\mathcal{M}| + 1$ possible values, where $\mathcal{M}$ represents the set of years spanned by the labeled source dataset $\mathcal{S}$, plus one additional year for the target data $\mathcal{D}_t$. Thus, $h_d(\cdot) \in \mathbb{R}^{|\mathcal{M}|+1}$.

The goal is to enable the vineyard parcel status classification process ($h_c(g(\cdot))$) to operate independently of the year from which the input sample originates in order to capitalize on out-of-year field campaigns and data collection efforts. To achieve this, we leverage recent advances in domain adaptation [23], where data from different domains (years in our case) are combined through an adversarial learning process [8]. The goal is to extract data representations $z = g(\cdot)$ that are invariant to the specific domain (year).

More specifically, adversarial domain adaptation achieves this through a minimax optimization framework inspired by Generative Adversarial Networks [11] (GANs). The core principle involves training two competing components: a feature extractor that aims to produce domain-invariant representations, and a domain discriminator that attempts to identify the domain of origin from these representations. In standard adversarial domain adaptation, the domain discriminator operates solely on the learned feature representations $z = g(\cdot)$, attempting to identify the domain of origin. However, this approach can fail when different classes exhibit distinct domain shifts, a common scenario in remote sensing where land cover classes respond differently to seasonal and environmental variations.

To this end, we took inspiration from the Conditional Domain Adversarial Network (CDAN) strategy [23], where the domain classifier head $h_d(\cdot)$ is conditioned on both the feature representation $z$ extracted by the encoder and
the task classifier output $h_c(\cdot)$. Rather than using only $z$ to distinguish domains, CDAN leverages the relationship between features and class predictions through their cross-covariance (computed via outer product). This conditioning mechanism captures class-conditional domain shift patterns, enabling the model to align distributions in a class-aware manner and ensuring that samples from the same class but different domains become indistinguishable. Domain-invariant representations are enforced via Gradient Reversal Layer (GRL) during backpropagation. This creates a two-player adversarial game between the task classifier and domain classifier, forcing the encoder $g(\cdot)$ to extract representations that are both discriminative for the vineyard parcel status classification task and invariant to the year of origin.

Finally, the complete framework is trained by optimizing the following loss function:

$$ \mathcal{L} = \mathcal{L}_{cl} + \mathcal{L}_{adv} \qquad (1) $$

where $\mathcal{L}_{cl}$ represents the classification loss associated with the task classifier output ($h_c(g(\cdot))$), and $\mathcal{L}_{adv}$ represents the adversarial loss from the domain classifier output ($h_d(GRL(g(\cdot)))$). Both loss terms are implemented using categorical cross-entropy, as is standard practice in multi-class classification problems [20]. During inference, the domain classifier head is discarded, and only the encoder and task classifier head are retained to perform class predictions on the unlabeled data from the target dataset $\mathcal{D}_t$.

Importantly, ConDA-Vine extends the CDAN approach to address the specific challenges of multi-year vineyard parcel classification. While CDAN was originally designed for single-source to single-target domain adaptation, ConDA-Vine operates in a multi-source setting where $M$ source domains (years) must be jointly aligned with one target domain (the out-of-year test data). The domain classifier head $h_d(\cdot)$ is therefore designed to distinguish between $|M| + 1$ domains rather than two, allowing the framework to simultaneously learn invariant representations across all available training years while adapting to the target year. This extension specifically addresses the temporal nature of domain shifts in satellite imagery, where inter-annual variations in phenological timing, climatic conditions, and sensor characteristics create complex distribution shifts that vary across vineyard classes.

## 3.3 Encoder and Classification Heads

The encoder is implemented as a Multi-Layer Perceptron (MLP) consisting of two hidden blocks. Each block comprises a linear layer with 256 neurons followed by batch normalization layer, a Rectifier Linear Unit (ReLU) activation function to introduce non-linearity during the learning process and dropout for regularization. The domain classification head follows a similar architecture with two hidden blocks that share the same structure of the encoder blocks. Additionally, it includes a final linear layer with $|M| + 1$ neurons, corresponding to the number of domains to be predicted in our setting. In contrast, the task classifier head employs a simpler design with a single linear layer containing three neurons, matching the number of vineyard parcel classes in our classification problem.
<figure><img src="image_6.png" /><figcaption>Figure 5: *ConDA-Vine* overview. The training phase (top) learns the model parameters of the three modules: encoder, task classification head and domain classification head. Conditional adversarial learning is used to enforce feature invariance property in the multi-task setting. The inference phase (bottom) deploy the encoder and the task classification head to predict labels for the unlabelled target dataset.</figcaption></figure>

# 4 Experimental evaluation

In this section, we describe the experimental evaluation we have conducted on the case study and data presented in Section 2. With the aim to assess the behaviour of our proposed approach, *ConDA-Vine*, for the task of out-of-year vineyard parcel status classification, we examine different aspects: i) we conduct a quantitative evaluation comparing *ConDA-Vine* against different standard machine learning competitors; ii) we analyze how our approach behaves varying the satellite data sources (multi-source vs. single-source) and how the temporal frequency of the time series data influences the final performances and; iii) we perform a qualitative assessment of the vineyard parcel maps generated by *ConDA-Vine*.

## 4.1 Experimental settings

All the satellite image time series, from 2020 to 2024, are re-scaled, per band, considering the 2nd and 98th percentile of the data distribution as minimum and maximum values. For our experimental assessment, we consider several standard classification algorithms, more precisely: Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP). To ensure a fair comparison, the MLP approach has the same architecture of *ConDA-Vine*. Due to the out-of-year classification setting, *n*-1 years are used for the training phase while the remaining year is used to assess the model performance on the test year. The procedure is repeated for each available year. Under this setting, for each competing approach we design two strategies: i) All the data from *n*-1 years are used together to train the model that is successively deployed on the leave out test year. We name such strategy *AllData*. ii) A model per
training year is derived and, than, the prediction on the test year is obtained combining the *n*-1 models together in an ensemble strategy, via a majority voting mechanism [33]. We name such strategy *Ensemble*. For instance, RF-*AllData* (resp. RF-*Ensemble*) indicates a Random Forest model trained under the first (resp. second) strategy. Additionally, we include a version of our framework implementing the *Ensemble* strategy, namely *ConDA-Vine-Ensemble*. This variant of *ConDA-Vine* provides further insights into our design choice to combine together data from different years within a single training process.

The assessment of the model performances is done using *Precision*, *Recall*, and *Weighted F1-score* metrics [19]. For simplicity, we refer to the weighted F1-score as *W-F1-score* throughout the remainder of this discussion. We selected these metrics, particularly the W-F1-score, because they provide robust evaluation under severe class imbalance ratio, such as those present in our vineyard parcel status classification task (as reported in Section 2, Table 1). Furthermore, when not specified, all the reported results are averaged over the five out-of-year cases, where, for each case, a different year is used as target year.

**Details of the implementation:** For the RF and the XGBoost approaches we use the open-source implementations available via the scikit-learn python library [32]. Both Random Forest and Extreme Gradient Boosting used 100 trees. The maximum tree depth was fixed to 8 and 1 for the Random Forest and the Extreme Gradient Boosting methods, respectively. For Multi-Layer Perceptron, *ConDA-Vine-Ensemble* and *ConDA-Vine*, we use the pytorch library [31] to implement and train the neural network architectures. The training stage has been conducted for 500 epochs, with a learning rate of $10^{-4}$, a batch size of 32 and AdamW [24] as parameters optimizer.

## 4.2 Comparison with competing approaches

Tables 2 and 3 report the overall and the per-class results, respectively, for all the competing methods.

<table><thead><tr><th>Method</th><th>Precision</th><th>Recall</th><th>W-F1-score</th></tr></thead><tbody><tr><td>RF-AllData</td><td><strong>0.78</strong></td><td>0.44</td><td>0.46</td></tr><tr><td>RF-Ensemble</td><td>0.57</td><td>0.49</td><td>0.48</td></tr><tr><td>XGBoost-AllData</td><td>0.63</td><td>0.52</td><td>0.54</td></tr><tr><td>XGBoost-Ensemble</td><td>0.41</td><td>0.41</td><td>0.35</td></tr><tr><td>MLP-AllData</td><td>0.61</td><td>0.59</td><td>0.57</td></tr><tr><td>MLP-Ensemble</td><td>0.47</td><td>0.44</td><td>0.33</td></tr><tr><td>ConDA-Vine-Ensemble</td><td>0.60</td><td><u>0.65</u></td><td><u>0.60</u></td></tr><tr><td>ConDA-Vine</td><td><u>0.67</u></td><td><strong>0.66</strong></td><td><strong>0.65</strong></td></tr></tbody></table>

Table 2: Overall quantitative results in terms of precision, recall and W-F1-score. Results are obtained by averaging the performances over the five years covered by the case study, using each year, from 2020 to 2024, as a target year. Best results are highlighted in **bold** while second-best are *underlined*.

Table 2 summarizes the result for the overall assessment. We can note that RF-*AllData* achieved the best precision. However, the value of this metric is influenced by the severe degree of class imbalance in our study case: RF classifies nearly all samples as *cultivated* vineyard parcels, which represent the majority class, while evidently struggling to identify both *uprooted* and *abandoned* vineyard parcels. RF-*Ensemble* follows a similar pattern but demonstrates a lower precision. Both XGBoost and MLP exhibit comparable performances, with similar results across
precision, recall, and W-F1-score metrics. For both competing approaches, the *AllData* strategy clearly outperforms the *Ensemble* variant, highlighting the benefit of using a single model with access to a large amount of training data, despite its internal heterogeneity. This is also the case for our proposed framework. *ConDA-Vine* outperforms all the other competing approaches achieving the second best precision and the best recall and W-F1-score. This latter two metrics clearly indicate the ability of our approach to better identify samples of under-represented classes (*uprooted* and *abandoned*) compared to all the other approaches. Furthermore, this general behaviour demonstrates that our approach efficiently exploits data from different years through its domain adaptation capability. This advantage is particularly evident when comparing *ConDA-Vine* with the MLP method, since the latter can be viewed as an ablation of our approach with the adversarial learning stage deactivated.

<table><caption>Table 3: Per-Class quantitative results in terms of W-F1-score. Results are obtained by averaging the performances over the five years covered by the case study, using each year, from 2020 to 2024, as a target year. Best results are highlighted in **bold** while second-best are **underlined**.</caption><thead><tr><th>Method</th><th>Cultivated</th><th>Uprooted</th><th>Abandoned</th></tr></thead><tbody><tr><td>RF-AllData</td><td><strong>0.92</strong></td><td>0.40</td><td>0.05</td></tr><tr><td>RF-Ensemble</td><td>0.85</td><td>0.45</td><td>0.15</td></tr><tr><td>XGBoost-AllData</td><td>0.91</td><td>0.47</td><td>0.24</td></tr><tr><td>XGBoost-Ensemble</td><td>0.68</td><td>0.29</td><td>0.07</td></tr><tr><td>MLP-AllData</td><td>0.88</td><td>0.49</td><td>0.34</td></tr><tr><td>MLP-Ensemble</td><td>0.60</td><td>0.36</td><td>0.04</td></tr><tr><td>ConDA-Vine-Ensemble</td><td>0.89</td><td><u>0.54</u></td><td><u>0.37</u></td></tr><tr><td>ConDA-Vine</td><td><strong>0.92</strong></td><td><strong>0.60</strong></td><td><strong>0.45</strong></td></tr></tbody></table>

Table 3 presents the per-class evaluation results. The trends observed here are consistent with the overall results discussed previously. For the RF approach, performance is comparable between the *AllData* and *Ensemble* strategies. In contrast, for XGBoost and MLP, the *AllData* strategy yields the best results. This is also the case for our framework where the joint exploitation of data coming from different years provides higher, per class, performances. Across all the competitors, the highest W-F1-scores are obtained for the *cultivated* class, whereas the minority classes (*uprooted* and *abandoned*) remain challenging, with performance closely tied to the degree of class imbalance. *ConDA-Vine* achieves the highest per-class performance across all classes, with particularly large gains for the under-represented classes *uprooted* and *abandoned*—exceeding 10 percentage points over the closest competitor. These findings clearly demonstrate that our approach for out-of-year vineyard parcel status classification not only provides overall performance improvements, but also achieves systematic gains across all classes, including those most affected by imbalance.

Finally, we assess the statistical significance of the differences between the behaviour of *ConDA-Vine* and each competing approaches. We consider each of the competing approach under the *AllData* strategy since it systematically outperforms the *Ensemble* strategy. For this purpose, we employ the Stuart Maxwell test [44], a non-parametric statistical test that extends McNemar's test to multi-class classification problems. While McNemar's test is limited to binary outcomes (correct vs. incorrect predictions), the Stuart-Maxwell test evaluates marginal homogeneity in contingency tables with multiple categories, making it well-suited for multi-class classifier comparison. The test examines whether two classifiers exhibit systematic differences in their error patterns by analyzing the contingency
table of their predictions on the same test set. The test statistic follows a chi-squared distribution with $k-1$ degrees of freedom, where $k$ represents the number of classes. The null hypothesis assumes that the two classifiers have identical marginal distributions, meaning they produce similar error patterns across all classes. Rejection of the null hypothesis indicates statistically significant differences in classification behavior. We perform pairwise comparisons between *ConDA-Vine* and each competitor (RF, XGBoost, and MLP) combining predictions across all five years, resulting in three statistical tests. Using a significance threshold of 0.05, we obtain p-values of approximately zero in all cases, well below the significance threshold. These results clearly reject the null hypothesis, confirming that *ConDA-Vine* achieves statistically significant performance differences compared to each competing method.

## 4.3 Satellite source ablation analysis and sensitivity to the temporal resolution of the time series

In this section, we assess our approach's performance at both overall and per-class level by varying the satellite data sources used as input. Subsequently, we investigate the sensitivity of our pipeline to different temporal resolutions at which satellite image time series information is sampled (see Section 2 for more details about time series temporal resolution).

Table 4 and Table 5 present results for the input data source analysis, while Table 6 and Table 7 report findings for the temporal resolution study.

<table><thead><tr><td>Input Source</td><td>Precision</td><td>Recall</td><td>W-F1-score</td></tr></thead><tbody><tr><td>S1</td><td>0.6</td><td>0.44</td><td>0.47</td></tr><tr><td>S2</td><td>0.68</td><td>0.67</td><td>0.66</td></tr><tr><td>S1 + S2</td><td>0.65</td><td>0.69</td><td>0.66</td></tr></tbody></table>

Table 4: Overall quantitative results of *ConDA-Vine* in terms of precision, recall and W-F1-score varying the satellite input sources: Sentinel-1 (S1), Sentinel-2 (S2) and Sentinel-1 + Sentinel-2 (S1+S2). Results are obtained by averaging the performances over the five years covered by the case study, using each year, from 2020 to 2024, as a target year.

<table><thead><tr><td>Input Source</td><td>Cultivated</td><td>Uprooted</td><td>Abandoned</td></tr></thead><tbody><tr><td>S1</td><td>0.91</td><td>0.35</td><td>0.14</td></tr><tr><td>S2</td><td>0.93</td><td>0.62</td><td>0.44</td></tr><tr><td>S1 + S2</td><td>0.92</td><td>0.60</td><td>0.45</td></tr></tbody></table>

Table 5: Per-Class quantitative results of *ConDA-Vine* in terms of W-F1-score varying the satellite input sources: Sentinel-1 (S1), Sentinel-2 (S2) and Sentinel-1 + Sentinel-2 (S1+S2). Results are obtained by averaging the performances over the five years covered by the case study, using each year, from 2020 to 2024, as a target year.

Concerning the input source ablation analysis shown in Table 4, Sentinel-1 SAR data (S1) alone demonstrates the lowest performance across all evaluated configuration. Using either Sentinel-2 optical data (S2) alone or the combined Sentinel-1 and Sentinel-2 data (S1+S2) yields comparable overall W-F1-scores, though with some differences in terms of precision and recall. While Sentinel-2 alone achieves higher precision by reducing false positives, the multi-source
approach (S1+S2) demonstrates better recall performance by minimizing false negative rate. The per-class analysis in Table 5 reveals that the *cultivated* class consistently achieves high W-F1-scores regardless of the input satellite source. Notably, the multi-source approach (S1+S2) delivers the best performance for the more challenging *abandoned* class, which is particularly difficult to identify due to severe class imbalance.

<table><caption>Table 6: Overall quantitative results of *ConDA-Vine* in terms of precision, Recall, W-F1-score and training time varying the temporal sampling of the satellite image time series information using monthly averages (Monthly TS) or the whole time series (Whole TS). Results are obtained by averaging the performances over the five years covered by the case study, using each year, from 2020 to 2024, as a target year.</caption><thead><tr><th>Temporal sampling</th><th>Precision</th><th>Recall</th><th>W-F1-score</th><th>Training Time (s)</th></tr></thead><tbody><tr><td>Monthly TS</td><td>0.65</td><td>0.69</td><td>0.66</td><td>79</td></tr><tr><td>Whole TS</td><td>0.66</td><td>0.67</td><td>0.65</td><td>83</td></tr></tbody></table>

<table><caption>Table 7: Per-Class quantitative results of *ConDA-Vine* in terms of W-F1-score varying the temporal sampling of the satellite image time series information using monthly averages (Monthly TS) or the whole time series (Whole TS). Results are obtained by averaging the performances over the five years covered by the case study, using each year, from 2020 to 2024, as a target year.</caption><thead><tr><th>Temporal sampling</th><th>Cultivated</th><th>Uprooted</th><th>Abandoned</th></tr></thead><tbody><tr><td>Monthly TS</td><td>0.92</td><td>0.60</td><td>0.45</td></tr><tr><td>Whole TS</td><td>0.92</td><td>0.60</td><td>0.42</td></tr></tbody></table>

The temporal resolution assessment presented in Table 6 shows that monthly temporal resolution (Monthly TS) and full temporal resolution (Whole TS) achieve comparable overall performance considering both classification metrics (precision, recall and W-F1-score) and required training time for the whole training process.

We remind that the monthly time series comprises only 6 images per year, while the full time series includes 48 images per sample, both temporal resolution spanning the period April to October, for each year. This finding demonstrates that increased temporal frequency does not necessarily improve performance, suggesting that monthly resolution captures sufficient phenological information to distinguish between the three vineyard classes. The per-class analysis in Table 7 provides additional insights into the temporal resolution comparison. Both approaches achieve the same W-F1-scores for the *cultivated* and *uprooted* classes. However, monthly sampling delivers better performance on the challenging *abandoned* class. This improvement likely stems from the reduced noise present in monthly acquisitions compared to the full time series. The monthly approach appears to capture more essential phenological signals while filtering out potentially confounding information that may be introduced when processing the complete 48-image time series. To conclude, while achieving overall comparable performance to the *Whole TS* scenario, the *Monthly TS* scenario offers two key advantages. First, it reduces the computational burden associated with the data pre-processing stage by decreasing the data volume from 50GB to 6.25GB, a reduction factor of 8. Second, it may facilitate the framework's transferability to other study areas across different biogeographical regions, as monthly time series are operationally easier to extract than full time series. This is because optical Sentinel-2 data may be affected by cloud cover that can disrupt acquisitions. Therefore, monthly aggregates represent a reasonable compromise in temporal resolution, providing approximately the same information about vineyard dynamics as the full temporal resolution,
as demonstrated by our results. For all these reasons, as indicated in Section 2.3, Monthly TS has been chosen as the
standard scenario.

## 4.4 Visual Inspection of the vineyard parcel maps

In this section, we present a qualitative analysis to further evaluate the performance of *ConDA-Vine* by comparing
its mapping behavior against competing methods on two representative map extracts, using 2024 as the target data.
The selected extracts are shown in Figure 6 and Figure 7.

<figure><img src="image_7.png" /><figcaption>Figure 6: Extracts from the maps derived by the different competing approaches on the target data associated to 2024. This extract is associated to a portion of the study site that is representative of the underlying class distribution. Ground truth information is provided in the red box at the beginning of the image sequence. Parcels shown in black indicate incorrectly classified (misclassified) parcels by the corresponding method.</figcaption></figure>

For each extract, we present the Ground truth reference map alongside the prediction maps generated by each
competing approach. Parcels shown in black indicate misclassified parcels by the corresponding method.

**Extract 1: Mixed Land Area.** The first extract (Figure 6) covers a representative portion of the study area dominated by *cultivated* parcels, with scattered clusters of *uprooted* and *abandoned* parcels. The mapping performance of all competing methods aligns with the quantitative results discussed previously. While all approaches achieve high accuracy on the majority *cultivated* class, their primary errors occur in predicting the under-represented classes. Notably, *ConDA-Vine* demonstrates more balanced predictions across all classes, correctly identifying a greater number of *uprooted* and *abandoned* parcels compared to competing methods.

**Extract 2: Abandoned Land Area.** The second extract (Figure 7) focuses on a region predominantly charac-
<figure><img src="image_8.png" /><figcaption>Figure 7: Extracts from the maps derived by the different competing approaches on the target data associated to 2024. This extract is associated to a region predominantly characterized by *abandoned* parcels. Ground truth information is provided in the red box at the beginning of the image sequence. Parcels shown in black indicate incorrectly classified (misclassified) parcels by the corresponding method.</figcaption></figure>

terized by *abandoned* plots, providing insights into method performance on the minority class. Two key observations emerge from this analysis. First, *ConDA-Vine* exhibits the lowest misclassification rate, both in terms of the number of incorrectly classified parcels and the total misclassified spatial area. Second, *ConDA-Vine* successfully identifies nearly all parcels belonging to the *abandoned* class, while competing methods struggle to distinguish *abandoned* parcels from *cultivated* and *uprooted* classes. This behaviour is particularly evident in the Random Forest results, which predominantly classify parcels as belonging to the majority *cultivated* class, demonstrating a clear bias toward the most frequent class in the training data.

# 5 Discussion

This study presents a framework for out-of-year vineyard parcel status classification using multi-source and multi-temporal remote sensing data from Sentinel-1 and Sentinel-2 sensors. The approach employs conditional adversarial learning to address data distribution shifts that occur when applying models trained on multiple years of data to a different year. To our knowledge, this represents the first application of recent deep learning methods for out-of-year multi-source analysis in viticultural monitoring.

The implementation of domain adaptation mechanisms to address temporal data distribution shifts demonstrated clear improvements in terms of classification behaviour. The proposed framework achieved overall performance gains
of more than 5 percentage points compared to the best competing approach when applied to out-of-year data. This improvement was particularly pronounced for challenging under-represented classes (*uprooted* and *abandoned*), where performance increased by more than 10 percentage points relative to competing approaches. These results indicate that domain adaptation can effectively mitigate the temporal variability that typically degrades model performance when transferring between years and it allows to capitalize and recycle previous efforts related to field data collection making possible to combine multiple years of information together.

Regarding the multi-source data integration aspect, the combined use of both Sentinel-1 and Sentinel-2 data provided modest overall improvements while it slightly ameliorates the results on the *abandoned* vineyard class. In our specific case where the *abandoned* vineyard class was the main class of interest, the identification of such class benefits from the joint use of SAR and optical information. More generally, the analysis suggests that Sentinel-2 data alone may be sufficient, depending on the specific monitoring objectives. Using only Sentinel-2 would reduce computational requirements during data pre-processing, as only one data source would need to be managed. The decision between single-source and multi-source approaches should therefore be guided by the balance between classification requirements and computational constraints.

The analysis of temporal resolution requirements revealed that high temporal resolution does not provide sig-
nificant advantages for vineyard parcel status classification. Monthly time series aggregation proved adequate for
the classification task while reducing computational burden during model training through decreased input data di-
mensionality. This finding suggests that operational implementations could utilize more lightweight models without
compromising performances. While monthly aggregation requires the same pre-processing effort as higher temporal
resolution data, the reduced data volume facilitates more efficient model deployment. Here, we move from 524 features
(22 × 2 SAR features + 48 × 10 optical features) for the *Whole TS* scenario to 82 features (22 × 2 SAR features +
6 × 10 optical features) for the *Monthly TS* scenario as input of the classification models.

The generated classification maps corroborated the quantitative performance metrics. Competing approaches exhibited clear difficulties with under-represented classes, particularly the *abandoned* vineyard category. In contrast, the proposed method demonstrated more consistent performance across all classes regardless of their level of representation in the training data. Analysis of maps from multiple years revealed consistent spatial patterns, suggesting the framework’s potential for operational deployment in viticultural management systems. The demonstrated robustness across different years and class imbalance situations indicates potential for practical implementation, where the framework could serve as a preliminary screening tool at the beginning of the field campaign season, directing human efforts and time toward specific target areas and reducing the need for extensive field surveys.

While developed specifically for vineyard applications, the framework may be adaptable to other crop types monitored at similar spatial and temporal resolutions. The demonstrated effectiveness with imbalanced datasets suggests potential applications in other agricultural contexts where under-represented classes pose challenges. For instance, identification of agricultural fallow/waste land [2, 7, 22], which is typically under-represented and poorly documented at large scales, represents a potential application domain for future evaluation.
# 6 Conclusion

The conducted research addressed the challenging problem of out-of-year vineyard parcel status classification, where models must generalize across different temporal periods characterized by varying environmental and climatic conditions. To this end, we introduced a novel deep learning framework, *ConDA-Vine*, that leverages conditional adversarial learning to tackle temporal domain adaptation challenges in multi-year satellite data classification. Our approach used freely available remote sensing data from Sentinel-1 (radar) and Sentinel-2 (optical) sensors, making it accessible for widespread implementation. Experimental results on a real-world case study in the Graves and Sauternes region of western France, involving multi-year field campaign data, demonstrate the effectiveness of our approach compared to competing methods across both overall and per-class performance metrics. Notably, *ConDA-Vine* exhibits superior performance on the crucial and challenging minority class of *abandoned* vineyard parcels, achieving improvements of more than 10 percentage points in weighted F1-score compared to the best competitors. The conducted research underscored the critical value of collaborating with stakeholders, as exemplified by the GDON du Sauternais et des Graves in our case study, who can provide high-quality, well-annotated ground truth data. Furthermore, it may pave the way for scalable, cost-effective screening tools that enable sustainable vineyard monitoring, support precision viticulture practices, and enhance regional vine health management strategies. Future research directions include evaluating the framework's performance on other out-of-year agricultural classification tasks and extending the framework to incorporate additional remote sensing data sources, such as very high spatial resolution imagery or other large-scale geospatial information available through modern Earth observation platforms.

**Disclosure statement.** No potential conflicts of interest are reported by the authors.

**Data Availability Statement.** The datasets supporting the conclusions of this article are available upon reasonable request. Interested researchers should contact the GDON des Sauternais et des Graves partner to discuss data access procedures and any applicable terms of use. Requests will be evaluated on a case-by-case basis to ensure compliance with data protection requirements and partner agreements.

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