The planetary crisis regarding water resources means that new methods are needed to monitor large areas of water basins that are threatened by chemical and natural pollutants, together with climate change. With the aim to monitor the pollution status of some Sites of National Interest (SIN) which represent very large contaminated Italian areas classified as dangerous, we are applying new or already well established algorithms to optical satellite images. In particular, a recently introduced oil spill detection algorithm [1] was able to, consistently and reliably, confirm the presence of oil in five polluted lake waters analysed. The combination of this algorithm with metagenomic analysis of the spill areas detected by the satellite allowed to identify drivers of the microbial response to oil pollution. Based on ortholog abundances, metabolic pathway reconstruction carried out in Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) software highlighted the degradative capacity of these microorganisms. These microorganisms could be suitable candidates for treatment of crude oil, aromatic hydrocarbons and the desulfurization of persistent petroleum substances like dibenzothiophene. Building upon this recent algorithmic development, the SatellOmic project funded by the Italian Space Agency (ASI) focuses on the pre-operational application of such integrated approach combining satellite sensing and metagenomics analyses for real-time monitoring of water bodies threatened by oil spills, as well as for the design of recovery strategies based on the use of valuable hydrocarbonoclastic microorganisms.

After carbon dioxide, methane is the second most relevant anthropogenic greenhouse gas in terms of its impact on climate change. Landfill-related emissions make up 15-18% of total methane emissions. Estimating methane emissions globally is crucial for effective global warming mitigation. To this end, this study, part of the CLEAR-UP project funded by the Italian Space Agency, investigates the application of the hyperspectral satellite platform PRISMA (PRecursore IperSpettrale della Missione Applicativa) for monitoring methane emissions from landfills. The retrieval methodology is based on an improved matched filter approach, and in this paper, we intend to assess PRISMA’s capacity to detect methane emissions by focusing on large-size sites as a case study. This research not only demonstrates PRISMA's potential in environmental monitoring but also contributes to strategies aimed at mitigating climate change impacts through improved waste management.

Reliable and efficient crop field segmentation is a fundamental pre-requisite for statistical analyses of agricultural practices. Traditional methodologies such as the Canny-Watershed (CW) algorithm require expert tuning of parameters for optimal results. This paper introduces an innovative approach for crop field segmentation in high-resolution satellite images, leveraging the use of multi-temporal Canny edge detection to train convolutional neural networks (CNNs) and fully automate the segmentation process. The Canny filter, applied to Sentinel-2 multi-temporal data, provides refined input for training ResUnet models, facilitating the generation of a generalized training dataset. ResUnet allows the model to learn complex features from diverse data, encapsulating seasonal changes. The dataset was specifically designed to enable the model to make accurate predictions from a single image, significantly outperforming traditional Canny filter predictions. In addition, the ResUnet may be applied to multiple images, generating output masks that, when overlayed, produce better results with respect to the multi-temporal Canny approach, demonstrating superior ability in recognizing real field boundaries while reducing false detections.To enhance generalizability, ResUnet is trained on a varied global dataset capturing a wide range of agricultural conditions and seasonal variations. This generalized model is tested across different regions and seasons, and preliminary results indicate that the proposed approach offers operational efficiency and accuracy in automating crop field segmentation.

Live Fuel Moisture Content (LFMC) is a fundamental variable of fire meteorology, fire behavior models and fire danger indices. The possibility of creating remote sensing LFMC products by directly training machine learning algorithms onto field measurements is severely limited by the sparse geographic distribution of such datasets, which are mostly concentrated in USA, Mediterranean Europe and Australia. Therefore, the physical foundation provided by a Radiative Transfer Model (RTM) such as PROSAIL remains an irreplaceable component of any LFMC product designed for global applicability. However, radiative transfer model inversion usually requires a lot of time and computing power. A Look-Up Table (LUT) approach saves time by running the model forward only during LUT creation, but still requires each row of the LUT to be compared with each observation when searching for the optimal solution. In this paper, we trained a random forest regressor on LUTs generated using PROSAIL and the Jasinski geometric model, aiming to exploit the efficiency of ML regressors to speed up calculation time while still maintaining the foundation of a physically-based approach that enables global applicability. The regressor was trained specifically to invert the LFMC, and was tested using Globe-LFMC v2 field-collected LFMC timeseries as a ground truth. The inversion, while returning results comparable in accuracy with the ones obtained using conventional methods, is now performed virtually instantly.

The aim of this paper is to classify irrigated crop fields in Kenya during the year 2021 using Machine-Learning (ML) techniques and Sentinel-2 time-series data, identifying the best performing classifier and combinations of spectral indices in terms of accuracy scores.To observe changes due to irrigation and monitoring vegetation status, remote sensing data can provide valuable information. By combining different spectral bands, high-resolution multispectral Sentinel-2 imagery can provide various vegetation indices, sensitive to certain aspects of crop status and moisture content.To distinguish irrigated fields using these indices, ML methods for multivariate time-series classification can be applied. By training a ML model on time-series data consisting of six different vegetation indices, two different classifiers were tested: the Time Series Forest (TSF) and the Weasel-Muse algorithm (one of the most promising according to Ruiz et al., 2020).In conclusion, both algorithms demonstrated to be reliable for multi-index Sentinel-2 time-series classification, and the Normalized Multiband Drought Index and the Modified Normalized Water Index were revealed as good indicators to detect irrigation.

Agricultural areas are naturally affected by significant variations within relatively short time intervals, in accordance with the growing season. These dynamics could, in principle, be exploited to classify different types of crops. Thus, this study aims to investigate methodologies and results of crop type classification making use of phenological information extracted from high spatial resolution satellite imagery. Vegetation indices (VI) retrieved from Sentinel-2 imagery are evaluated to track the year-round vegetation behavior. Starting from a multi-temporal image series of the same scene, the phenological profiles can be extracted and introduced into a supervised classification process to detect crop fields, discriminating among different species. Following this, we propose a cross-correlation based model that, using a priori information from ground training data, searches for the best matching phenology. When compared to machine learning models for crop classification, the one proposed in this study can provide useful information about phenology that can be stored and used for better monitoring spatio-temporal variations of crops species through the future years and guiding agricultural management accordingly. Our case studies are the regions of Bothaville and Harrismith, located in South Africa, and the region of Jendouba in Tunisia. The results for the Bothaville region show 89.19% of user accuracy on the main crop type classification (maize crops). For beans and sorghum, the confusion matrix attests 93% and 74% of accuracy respectively, even if their statistics are less significant due to the limited number of available ground data for secondary crops.