Currently, methods for mapping agricultural crops have been predominantly developed for a number of the most important and popular crops. These methods are often based on remote sensing data, scarce information about the location and boundaries of fields of a particular crop, and involve analyzing phenological changes throughout the growing season by utilizing vegetation indices, e.g., the normalized difference vegetation index. However, this approach encounters challenges when attempting to distinguish fields with different crops growing in the same area or crops that share similar phenology. This complicates the reliable identification of the target crops based solely on vegetation index patterns. This research paper aims to investigate the potential of advanced techniques for crop mapping using satellite data and qualitative information. These advanced approaches involve interpreting features in satellite images in conjunction with cartographic, statistical, and climate data. The study focuses on data collection and mapping of three specific crops: lavender, almond, and barley, and relies on various sources of information for crop detection, including satellite image characteristics, regional statistical data detailing crop areas, and phenological information, such as flowering dates and the end of the growing season in specific regions. As an example, the study attempts to visually identify lavender fields in Bulgaria and almond orchards in the USA. We test several state-of-the-art methods for semantic segmentation (U-Net, UNet++, ResUnet). The best result was achieved by a ResUnet model (96.4%). Furthermore, the paper explores how vegetation indices can be leveraged to enhance the precision of crop identification, showcasing their advanced capabilities for this task.

Wind speed at the sea surface is a key quantity for a variety of scientific applications and human activities. For its importance, many observation techniques exist, ranging from in situ to satellite observations. However, none of such techniques can capture the spatiotemporal variability of the phenomenon at the same time. Reanalysis products, obtained from data assimilation methods, represent the state-of-the-art for sea-surface wind speed monitoring but may be biased by model errors and their spatial resolution is not competitive with satellite products. In this work, we propose a scheme based on both data assimilation and deep learning concepts to process spatiotemporally heterogeneous input sources to reconstruct high-resolution time series of spatial wind speed fields. This method allows to us make the most of the complementary information conveyed by the different sea-surface information typically available in operational settings. We use synthetic wind speed data to emulate satellite images, in situ time series and reanalyzed wind fields. Starting from these pseudo-observations, we run extensive numerical simulations to assess the impact of each input source on the model reconstruction performance. We show that our proposed framework outperforms a deep learning–based inversion scheme and can successfully exploit the spatiotemporal complementary information of the different input sources. We also show that the model can learn the possible bias in reanalysis products and attenuate it in the output reconstructions.

Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes; yet, existing approaches suffer from synchronization overhead in this setting. In this article, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.