Remote sensing, and particularly Sinthetic Aperture Radar (SAR) is a potential tool to monitor the presence of plastic on the ocean surface, and AI algorithms can learn to find patterns in the satellite signals that lead to authomatic detection of plastic patches. The main challenge to train AI algorithms for this purpose is the lack of insitu ground truthdata that allow to reliably label the images. This paper describes a first pilot of binary pixel classification (plastic vs. non-plastic) in SAR imagery using Random Forest, GaussianNaïve Bayes and Support Vector Machines classifiers. It also describes the strategy used to generate ground truth data using Sentinel-1 and Sentinel-2 in the Balearic Islands.

Regional mapping of Above Ground Biomass Density (AGBD) using Remote Sensing data has shown high accuracy but lacks replicability at a global scale. In contrast, global models capture AGBD variability across biomes but struggle with biome-specific accuracy. To address this gap, we develop and assess the performance of a Deep Learning model for mapping AGBD at 10-m resolution using multi-source satellite data (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI) across four biomes: Mediterranean, taiga (boreal forests), tropical rainforests, and semi-arid savannas. The model is trained and validated separately for each biome, yielding four regional models with normalized RMSEs of 0.43–0.67 and correlation coefficients (r) of 0.61–0.77 against forest inventories. We compare predictions from these models to a benchmark dataset and to a model trained on all four biomes combined. The regional models consistently outperform both, achieving better metrics than the benchmark. Additionally, an analysis of prediction drivers reveals biome-specific differences, reinforcing the importance of per-biome mapping approaches. This study highlights the advantages and limitations of regional against global modeling, creating the basis for biome-specific, replicable, scalable and multi-temporal AGBD mapping.

Quantifying tree resources is essential for effectively implementing climate adaptation strategies and supporting local communities. In the Sahel, where tree presence is scattered, measuring carbon becomes challenging. We present an approach to estimating aboveground carbon (AGC) at the individual tree level using a combination of very high-resolution imagery, field-collected data, and machine learning algorithms. We populated an AGC database from in situ measurements using allometric equations and carbon conversion factors. We extracted satellite spectral information and tree crown area upon segmenting each tree crown. We then trained and validated an artificial neural network to predict AGC from these variables. The validation at the tree level resulted in an R ² of 0.66, a root mean square error (RMSE) of 373.85 kg, a relative RMSE of 78.6%, and an overestimation bias of 47 kg. When aggregating results at coarser spatial resolutions, the relative RMSE decreased for all areas, with the median value at the plot level being under 30% in all cases. Within our areas of study, we obtained a total of 3,900 Mg, with an average carbon content per tree of 330 kg. A benchmarking analysis against published carbon maps showed that 9 out of 10 underestimate AGC stocks, in comparison to our results, in the areas of study. An additional comparison against a method using only crown area to determine AGC showed an improved performance, including spectral signature. This study improves crown-based biomass estimations for areas where unmanned aerial vehicle or height data are not available and validates at the individual tree level using solely satellite imagery.

Surface water availability is a fundamental environmental variable to implement effective climate adaptation and mitigation plans, as expressed by scientific, financial and political stakeholders. Recently published requirements urge the need for homogenised access to long historical records at a global scale, together with the standardised characterisation of the accuracy of observations. While satellite altimeters offer world coverage measurements, existing initiatives and online platforms provide derived water level data. However, these are sparse, particularly in complex topographies. This study introduces a new methodology in two steps (1) teroVIR, a virtual station extractor for a more comprehensive global and automatic monitoring of water bodies, and (2) teroWAT, a multi-mission, interoperable water level processor, for handling all terrain types. L2 and L1 altimetry products are used, with state-of-the-art retracker algorithms in the methodology. The work presents a benchmark between teroVIR and current platforms in West Africa, Kazakhastan and the Arctic: teroVIR shows an unprecedented increase from $55\%$ to $99\%$ in spatial coverage. A large-scale validation of teroWAT results in an average of unbiased root mean square error ubRMSE of $0.638$ m on average for 36 locations in West Africa. Traditional metrics (ubRMSE, median, absolute deviation, Pearson coefficient) disclose significantly better values for teroWAT when compared with existing platforms, of the order of 8 cm and $5\%$ improved respectively in error and correlation. teroWAT shows unprecedented excellent results in the Arctic, using an L1 products-based algorithm instead of L2, reducing the error by almost 4 m on average. To further compare teroWAT with existing methods, a new scoring option, teroSCO, is presented, measuring the quality of the validation of time series transversally and objectively across different strategies. Finally, teroVIR and teroWAT are implemented as platform-agnostic modules and used by flood forecasting and river discharge methods as relevant examples. A review of various applications for miscellaneous end-users is given, tackling the educational challenge raised by the community.