Estuarine areas play a crucial role in coastal ecosystems, being transitional zones where freshwater from rivers meets and mixes with saltwater from the open sea.

These areas are dynamic and experience tidal fluctuations, allowing for a free exchange of water between land and sea. Estuarine ecosystems are characterized by a mix of fresh and saltwater that provides abundant nutrients, making estuaries highly productive habitats to support a diverse range of species, including fish, invertebrates, and birds. In the case of river Sado, seagrass meadows and marshes found in nearshore estuarine and marine ecosystems contribute to this high productivity (Beck et al., 2001) .

From the human and social perspective, governance of estuaries is a complex subject in Portugal (Fidélis & Carvalho, 2013) , with multiple interests and multiple jurisdictions that do not contribute to a holistic approach of such a rich and fragile environment.

This work analyses the efficacy of several classification methods available in ArcGIS Pro for mapping estuarine habitats exposed to different conditions of tides, using aerial photographic imagery at 20 cm ground resolution. These flights were the result of a two-day flight plan, covering the area of interest as well as possible and considering the variations and heights of the tides, and the wave delay (Khojasteh et al, 2021) .

Several authors have worked with deep learning methods and high-resolution images with similar goals; (Zhang et al., 2020) is a recent review of land cover classification and object detection approaches, in which traditional standard approaches are compared with deep learning models. The better performance of the latter is attributed to the simultaneous use of spectral and spatial information in object-based methods, while older approaches are based on pixel-by-pixel methods, which result in maps with the typical salt-and-pepper noise incorporated. In the last 20 years deep learning methods began to appear applied to land cover classification (Audebert et al., 2016; Huang et al., 2018; Kemker et al., 2018) with promising results and can be found in more applications in remote sensing and Earth sciences (Reichstein et al., 2019) , mainly in land use and land cover (LULC) classification, to which (Vali et al., 2020) provides a complete framework. A Joint Deep Learning model (Zhang et al., 2019) provides novelty using spatial and hierarchical relationships between land cover probabilities and land use classifications, applied to an urban/suburban environment. The deep learning approach is so promising to handle large amounts of data in time series that large datasets such as EuroSAT are already publicly available for benchmarking (Helber et al., 2019) , using Sentinel-2 images and 10 classes for LULC (27,000 georeferenced sub images at 10 m ground resolution in 13 spectral bands). More recently, the use of images from unoccupied aerial vehicles (UAVs) paired with deep learning algorithms (Gonzalez-Perez et al., 2022) has emerged as a tool with great potential for the study of coastal systems, both in terms of the results obtained and the associated costs (Durgan et al., 2020, Prentice et al., 2021) . The UAVs technology being stable for the last decade, they become a resourceful tool for coastal surveys (Turner et al., 2016, Liu et al., 2018) .

Aerial photography seems the best solution for estuary mapping and monitorization, catching simultaneously the detail and the context (Bendell & Wan, 2011) , with the advantage that nowadays the GIS software has built-in deep learning tools, although coverage is expensive and difficult to achieve at the best of times in terms of low tides and wave effects—perhaps these are the reasons that have made this approach rare.

The case study presented in this article focused on finding the most consistent methodology among those available in the software, to classify high-resolution images and produce thematic maps, which will form a reference base for monitoring the evolution of the most relevant habitats in the estuarine zone, allowing future assessments of the local ecosystem, as well as the identification of natural and anthropogenic changes that have occurred in the meantime.

A quantitative evaluation of results was carried out using 500 control points in the test area, with the overall kappa index (Cohen’s Kappa statistic) and the overall accuracy (OA), both based on the confusion matrix, considered to be an indicator of the ability of the algorithm to identify all classes simultaneously. In short, User's accuracy concerns false positives or errors of commission: points incorrectly classified as belonging to one class when they belong to another. Producer's accuracy reflects false negatives or errors of omission: points in a class that have not been identified as such.

The Kappa statistic (Cohen, 1960) is a metric that provides an overall assessment of the accuracy of the classification, comparing it with a random classification. Another useful number is the global or overall accuracy, which indicates the percentage of well-identified points (sum of the diagonal of the confusion matrix) in the total number of control points used. Of the three standard algorithms tested, Random Trees provided the highest overall accuracy, with a Kappa of 0.811 and correctly classifying 92.9% of the control points ( Table 1 ).

The RT classifier offers the best results both quantitatively and qualitatively, by visual inspection ( Figure 5(b) ); it has the disadvantage of having a random component, which makes the realization of a good model more difficult to ensure because it is not just a function of the chosen parameters.

Three deep-learning models have results with enough quality to be explored, and among the results obtained with this type of image, the U-Net model showed slightly better results ( Table 2 ).

The segmented images resulting from the three deep-learning options are compared in Figure 6 . The lower resolution of DLabV3 is obvious ( Figure 6(b) ), although version 3 of the algorithm has already been mentioned as an improvement in this area (Li & Dong, 2022) .

The results with the U-Net model are clearly more homogeneous and with more precise contours ( Figure 6(d) ), showing slightly better quantitative results

than the others ( Table 2 ); it’s also more accurate and coherent with the photo-interpretation of the image in the reference area, with the Bare soil areas being observed in the expected configuration, as well as a more correct identification of the boundaries of Seagrass patches available as ground truth ( Figure 7 ).

In the laptop described in 2.2, processing times ranged from around 1.5 hours to extract the data using the ground truth previously defined, 2 to 5 hours to train the model, depending on the model chosen, and 3 to 7 hours to classify each image block, depending on the model chosen and the size of the image.

As no other similar approach was found with this kind of data, we can only

discuss the results against each other, as presented above. The U-Net model gives high-quality results with aerial photography, making all the post-processing steps previously required by conventional classifications unnecessary; ArcGIS Pro provides all the tools, with a learning curve feasible for a user with some background in classification methods, without the necessity of the informatic means and skills to implement complex deep learning procedures.

From the results illustrated and many others that we have explored with less success, but which have also contributed to guide the choice of the parameters of the models tested, we conclude that the deep learning results obtained with the U-Net model with ResNet-34 as the backbone are superior to the standard machine learning methods for this type of high-resolution multispectral images, using the four bands R, G, B and Near-infrared. More compact patches and better definitions of contours are obtained in the most intricated areas, and fine elements are preserved and correctly identified. Even in the definition of Saltmarsh patches, which is a class correctly identified in general in both options, U-Net's performance is superior, simultaneously presenting well-defined contours and homogeneous patches, and managing to identify even the most problematic areas, such as the presence of salt marsh on the walls separating the tanks from the salt pans. Like (Gonzalez-Perez et al., 2022) we use machine learning algorithms and deep learning models trained with the same training set and tested with the same control points and we found a clear advantage of the U-Net model in the classification of the estuarine zone under study.

The main disadvantage is the processing time involved, but we were working with a fairly large area (18,776 ha) with a high resolution (0.2 m) on a laptop with a normal configuration, so this could probably be improved with an upgraded working configuration. The learning curve is variable but is quickly mastered with some method and a prior knowledge of supervised classification that facilitates familiarization with the various requirements of the models and the successive steps needed to complete the procedures.

This study had the support of national funds through Fundação para a Ciência e Tecnologia, under the project LA/P/0069/2020, ( https://doi.org/10.54499/LA/P/0069/2020 ) granted to the ARNET (Aquatic Research Network Associated Laboratory), UIDB/04292/2020, ( https://doi.org/10.54499/UIDB/04292/2020 ) and UIDP/04292/2020 ( https://doi.org/10.54499/UIDP/04292/2020 ), granted to MARE (Marine and Environmental Sciences Centre).