The European Space Agency's (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission's primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite's advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.

Abstract Effective communication of climate science is critical as climate‐related disasters become more frequent and severe. Translating complex information, such as uncertainties in climate model predictions, into formats accessible to diverse audiences is key to informed decision‐making and public engagement. This study investigates how different teaching formats can enhance understanding of these uncertainties. This study compares two multimodal strategies: (1) a text‐image format with interactive components and (2) an explainer video combining dynamic visuals with narration. Participants' immediate and delayed retention (one week) and engagement are assessed to determine which format offers greater saliency. Sample analysis (n = 622) displayed equivalent retention by viewers between both formats. Metrics assessing interactivity found no correlation between interactivity and information retention. However, a stark contrast was observed in the time viewers spent engaging with each format. The video format was 29% more efficient with information taught over a period of time vs. the article. Additionally, retention on the video format worsened with age (P = 0.004) while retention on the article format improved with education (P = 0.038). These results align with previous findings in literature.

Precise geospatial vegetation forecasting holds potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbon accounting. To leverage the vast availability of satellite imagery for this task, various works have applied deep neural networks for predicting multispectral images in photorealistic quality. However, the important area of vegetation dynamics has not been thoroughly explored. Our study introduces GreenEarthNet, the first dataset specifically designed for high-resolution vegetation forecasting, and Contextformer, a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe. Our multi-modal transformer model Contextformer leverages spatial context through a vision backbone and predicts the temporal dynamics on local context patches incorporating meteorological time series in a parameter-efficient manner. The GreenEarthNet dataset features a learned cloud mask and an appropriate evaluation scheme for vegetation modeling. It also maintains compatibility with the existing satellite imagery forecasting dataset EarthNet2021, enabling cross-dataset model comparisons. Our extensive qualitative and quantitative analyses reveal that our methods outperform a broad range of baseline techniques. This includes surpassing previous state-of-the-art models on EarthNet2021, as well as adapted models from time series forecasting and video prediction. To the best of our knowledge, this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle, thereby paving the way for predicting vegetation health and behaviour in response to climate variability and extremes. We provide open source code and pre-trained weights to reproduce our experimental results under https://github.com/vitusbenson/greenearthnet [10].

The SCOPE model embeds the state of art for coupling soil vegetation atmosphere transfer (SVAT) and radiative transfer models (RTM). For that reason the FLuorescence EXplorer (FLEX) mission selected this model to derive vegetation properties through inversion. However inverse problem is often ill-posed, providing equally likely solutions and hence inflating the uncertainty of the retrieved parameters. In this work we test the use of different priors based on ancillary measurements and literature to support multiple-constrain inversion of SCOPE. Results show that prior information on the relationships between variables such as leaf chlorophyll content (Cab), leaf carotenoids content (Cca), leaf water content (Cw) and/or maximum carboxylation rate (Vcmax) reduce inversion uncertainties and overfitting, and should be sampled/estimated together with optical data.

Machine learning tools and semi-empirical models have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere state and function. Yet, little work has been carried to explicitly model dynamic features accounting for memory effects, where in some cases hand-designed features (e.g. temperature sum, lagged precipitation) have been employed. Here, we explore the ability of recurrent neural network variants (RNN, LSTM) to model time series of dynamic variables 1) fPAR and NDVI, and 2) Carbon dioxide uptake and evapotranspiration, with meteorological variables as the only dynamic predictors. We show that the recurrent neural net approach excellently deals with this dynamic modelling challenge and outcompetes approaches where hand-designed features are complicated to conceive.

The goal of this study is the post-launch validation of MODIS IGBP Land Cover Product for the Mediterranean landscape. Southern Portugal, mostly a Mediterranean ecosystem due to climate and geomorphology, is characterized by large homogeneous patches. For this reason, it was chosen as the most promising region where global products could be used. The most recent 2002 MODIS Land Cover data set was used. The selection of the test sites was conducted through a method supported by Landsat5-TM images, to derive homogeneous patches. Moreover, field test sites accommodated the guidelines from the System for Terrestrial Ecosystem Parameterization (STEP) document. Confusion matrix results in an overall accuracy of about 36% and a kappa statistic of 0.09. Croplands, which constitute the majority of the study area, is the most well classified feature, with an accuracy of 77%, although the MODIS classifies it largely by excess. The evergreen broadleaf forest is the most confused theme, with several land cover classes – evergreen needleaf forest, mixed forest, woody savannas, savannas and closed shrublands - classified as such. However, there isn’t any classified evergreen broadleaf forest pixel. Therefore, it seems that there is a consistent misclassification of broadleaf evergreen forest. We strongly believe that the reason for the misclassification of the Mediterranean landscape relies in its weak representation within the training sites framework. MODIS land cover maps, produced by processing the 32-day database using decision tree and artificial neural network classification algorithms, are greatly dependent on the core sites data. Therefore, the classification of Mediterranean likely landscapes could never occur properly. Adding Mediterranean ecosystem like training sites could greatly improve the results. This study shows how careful the adoption of global products to regional studies should be. The validation of the MODIS IGBP Land Cover Product for Mediterranean landscape revealed that more regional work should be taken into account during the production of global products in order to reflect this biome.