The rapid growth of remotely sensed earth observation data presents clear opportunities for monitoring complex ecosystem change and answering fundamental ecological questions. However, large-scale automated monitoring of ecosystems faces challenges. Data-driven models require extensive datasets and often lack generalizability when training data are unrepresentative, while process-driven models, such as radiative transfer models (RTMs), can be imprecise due to gaps in knowledge, or simplified representation of physical processes. To enhance the prediction of plant functional traits and simultaneously discover where process-driven models can be improved, we explore the potential of Physics-Informed Neural Networks (PINNs) as a hybrid approach that combines the strengths of both methodologies at the leaf scale. In contrast to data augmentation approaches, our implementation directly integrates the widely-used PROSPECT5B model into the architecture of an autoencoder framework. Our results show that our PINNs approach is able to outperform data-driven techniques even when trained on very limited training data (i.e. 17 % training vs 83 % validation). We also identified weak points in the PROSPECT5B model by progressively replacing individual components of PROSPECT5B with convolutional neural networks. Our case study indicates that especially Prospect's generalized “plate model” could be refined to improve predictive ability. Hence, our framework provides a self-diagnostic capability and identifies areas for improvement in process-driven models and their components. Thus, we conclude that PINNs 1) improve data-driven predictive accuracy while maintaining physical consistency with minimal training data while 2) being able to identify limitations in process-driven models. Hence, we believe our framework could serve as a new standard for evolving and improving radiative transfer models.

Ecosystems are threatened by increasing droughts under climate change. A multitude of plant physiological regulation processes determine the overall drought resistance of ecosystems. So far, these physiological strategies to resist drought are poorly understood at large scales across different ecosystem types because the detection of these physiological regulation processes is mostly limited to in situ measurements on individual plants. In this study, by using high-resolution remote sensing data, we evaluated drought strategies of different ecosystem types throughout Europe by evaluating three key physiological regulation aspects (evapotranspiration, water content, and carbon regulation) based on their associated vegetation attributes. We found that different ecosystem types show divergent responses in these physiological attributes, suggesting different optimization strategies with respect to water saving versus spending, water content stabilizing versus fluctuating, and leaf conserving versus shedding strategies facing drought. These drought strategies from remote sensing provide timely ecosystem response information, facilitating earth system model predictions and aiding the protection against future droughts at large scales.

Satellite remote sensing (SRS) provides huge potential for tracking progress towards conservation targets and goals, but SRS products need to be tailored towards the requirements of ecological users and policymakers. In this viewpoint article, we propose to advance SRS products with a terrestrial biodiversity focus for tracking the goals and targets of the Kunming-Montreal global biodiversity framework (GBF). Of 371 GBF biodiversity indicators, we identified 58 unique indicators for tracking the state of terrestrial biodiversity, spanning 2 goals and 8 targets. Thirty-six shared enough information to analyse their underlying workflows and spatial information products. We used the concept of Essential Biodiversity Variables (EBV) to connect spatial information products to different dimensions of biodiversity (e.g. species populations, species traits, and ecosystem structure), and then counted EBV usage across GBF goals and targets. Combined with published scores on feasibility, accuracy, and immaturity of SRS products, we identified a priority list of terrestrial SRS products representing opportunities for scientific development in the next decade. From this list, we suggest two key directions for advancing SRS products and workflows in the GBF context using current instruments and technologies. First, existing terrestrial ecosystem distributions and live cover fraction SRS products (of above-ground biomass, ecosystem fragmentation, ecosystem structural variance, fraction of vegetation cover, plant area index profile, and land cover) need to be refined using a co-design approach to achieve harmonized ecosystem taxonomies, reference states and improved thematic detail. Second, new SRS products related to plant physiology and primary productivity (e.g. leaf area index, chlorophyll content & flux, foliar N/P/K content, and carbon cycle) need to be developed to better estimate plant functional traits, especially with deep learning techniques, radiative transfer models and multi-sensor frameworks. Advancements along these two routes could greatly improve the tracking of GBF target 2 (‘improve connectivity of priority terrestrial ecosystems), target 3 (‘ensure management of protected areas’), target 6 (‘control the introduction and impact of invasive alien species’), target 8 (‘minimize impact of climate change on biodiversity’), target 10 (‘increase sustainable productivity of agricultural and forested ecosystems’) and target 12 (‘increase public urban green/blue spaces’). Such improvements can have secondary benefits for other EBVs, e.g. as predictor variables for modelling species distributions and population abundances (i.e. data that are required in several GBF indicators). We hope that our viewpoint stimulates the advancement of biodiversity monitoring from space and a stronger collaboration among ecologists, SRS scientists and policy experts.