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 detection of burnt areas from satellite imagery seems one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images [1]. This change detection analysis is usually carried out by comparing the values of specific spectral indices such as: NBR (Normalized Burn Ratio) [2], BAI (Burned Area Index) [3], MIRBI (Mid Infrared Burn Index) [4]. However, some potential sources of error arise, in particular when near-real-time automated approaches are adopted. An automated approach is needed when the burnt area monitoring should operate systematically on a given area of large sizes (country). Potential sources of error are: clouds on the pre- or post-event images, clouds or topographic shadows [5], agricultural practices, image pixel size, level of damage, etc. [6]Sources of difference between different existing global datasets of burned areas based on satellite images could be related to the spatial resolution of the images used, the land cover mask adopted to avoid false alarms, the quality of the cloud and shadow masks. This paper aims at comparing several burned area databases (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective of identifying and characterizing their approximations. In order to do so, ground survey data provided by CUFA (Comando unità forestali, ambientali e agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) have been used. In using ground data, the accessibility of the burned area has been also considered in order to account for the approximations due to the difficulties in reaching the affected area.

The incidence of wildfires and megafires with their disastrous consequences is increasing all over the planet, both in terms of burned area surface and in terms of power released by the fires. For this reason, the strategic importance of wildfire prevention is greater than ever, and providing the decision makers with powerful and state-of-the-art tools is an utmost priority. To this end, satellite observations offer a privileged platform to cover large spatial scales with a high time frequency. However, the most popular fire danger products tend to cover very large spatial scales at a coarse resolution, and inherently lack the capability to provide a level of detail which is very useful at the local scale. At the same time, using different products at different spatial scales would be impractical, and would increase the workload and training requirements of the personnel. To this end, this paper proposes a scalable-resolution fire danger index, named Daily Fire Danger Index, based on Sentinel-2 L2A and Sentinel-3 Synergy products. This novel index exploits both weather and satellite data to estimate all the main fire weather variables, and is calibrated using the historical records of wildfire occurrence in the area of interest.

Live Fuel Moisture Content is strongly related to the proneness of live vegetation to ignite and burn, and as such, it is one of the fundamental variables in fire ignition and fire behavior models. While in-situ measurements of this physical variable remain irreplaceable and invaluable, long-term historical records are mostly concentrated in select regions of Mediterranean Europe, United States and Australia. For this and other reasons, there is a strong interest in obtaining estimates based on remotely sensed data on a large spatial scale. To this end, this paper proposes a Live Fuel Moisture Content product based on optical satellite imagery acquired by the European satellites Sentinel-2 and Sentinel-3, and an inversion procedure based on the PROSAIL family of radiative transfer models. An early validation of the product is presented at the S2 and S3 spatial scales using field data provided by the Portuguese Association for the Development of Industrial Aerodynamics of the University of Coimbra and the Institute of BioEconomy of the Italian National Research Council.

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.

The recent surge in interest towards hyperspectral imagery has the potential to unlock a new range of applications for the scientific community. However, compared to traditional multi-spectral images, the workload required to process such high-dimensional data is dramatically increased, to the point that new and more flexible strategies must be developed in order to properly monitor the quality of this type of products. In the particular case of sharpness assessment, traditional procedures based on the edge method tend to be extremely time-consuming due to their reliance on visual analysis performed by human operators, and would make proper processing of all bands a daunting task to perform on a large scale. In this paper we propose a flexible and fully automatic approach to edge method-based sharpness assessment that can be applied inde-pendently from the number of spectral bands. We then present the results of the application of the methodology on the visible and near-infrared and shortwave infrared spectral cubes of a selection of PRISMA L2D images, which confirm the relia-bility of the methodology and suggest further improvements.

The main objectives of the Sentinel-3 mission are to support ocean forecasting systems, environmental and climate monitoring. However, the coverage of the visible, near-infrared and short-wave infrared portion of the electromagnetic spectrum with a 300 meter resolution and a revisit period of less than 2 days make it very appealing also for vegetation monitoring. In this paper we explore the possibility of using the Sentinel-3 Synergy surface directional reflectances and the PROSAIL model to reliably estimate biophysical variables in general and live fuel moisture content in particular. The latter is a fundamental variable in fire behaviour models and in fire danger assessment, and consequently of high interest in fire management activities. We performed a Global Sensitivity Analysis to identify the most significant PROSAIL parameters in each Synergy channel, and tested the results by implementing a simple Look-Up Table based retrieval algorithm. The outcome shows the potential of biophysical parameter estimation based on this Sentinel-3 product.

The metrics traditionally used for assessing the sharpness level of optical imagery acquired by spaceborne sensors (e.g., relative edge response, point spread function, full width at half maximum of the line spread function, modulation transfer function) are usually measured before launch using a set of standard simulated inputs. However, vibrations occurring during launch and satellite deployment, as well as sensor degradation through time, may alter the nominal characteristics significantly. Therefore, post-launch assessment analyses are necessary for monitoring data quality. To this end, on-orbit measurement techniques for sharpness assessment - e.g., the edge method (EM) - were developed. Selection of suitable targets to be used for such techniques is a crucial step for carrying out the assessment successfully. The objective of this paper is to describe an automatic method for identification of suitable edges to be used for on-orbit sharpness assessment via the EM by taking into account the widespread presence of natural targets - like agricultural fields - on Earth. The presented approach is expected to ease the continuous monitoring of the sharpness level of optical data acquired by spaceborne sensors.