dr. F (Francisco) Pinto Espinosa

High-throughput phenotyping for crop resilience to climate change:

I have dedicated an important part of my career in exploring the use of remote and proximal sensing for high-throughput phenotyping of adaptive traits in breeding trials, targeting at improving the characterization of the GxE interaction and therefore the selection of genotypes better adapted to stress. In my research I combine data analysis approaches (e.g. Machine Learning) with imaging techniques (e.g. imaging spectroscopy, thermography and LIDAR), to identify and quantify physiological traits that can be used for selection and prediction of genotype adaptation to different environments. In particular, I focus in the remote sensing of traits related to radiation, water and nitrogen use efficiency. An important consideration in this research line is the effective combination of phenotypic and environmental data.

Remote sensing for upscaling crop and functional-structural plant models:

I believe that remote sensing can improve the applicability of models across different spatio-temporal scales. Therefore, I investigate how to combine remote sensing with crop models to upscale our prediction capacity of crop performance all the way from the canopy to the global level. On the one hand, the use of sensors can contribute to the improvement and validation of crop models, and on the other hand, different remote sensing platforms can be used for the parameterization of models across time and space.

I am an ecophysiologist specialized in remote sensing of vegetation. I am interested in understanding the complex interactions between crops and their environment at different spatio-temporal scales by using sensors and remote sensing techniques. Plants adapt to the environment by dynamically modifying their morphology and functioning. Processes such as photosynthesis or growth are regulated and vary in space and time depending on the scale of interest. For instance, photosynthesis is regulated differently at the molecular, leaf or canopy level. However, there are still knowledge gaps that prevent us from translating our understanding from one scale to another. In this context, the use of sensors and remote sensing platforms (e.g. UAVs) can help to close these gaps by quantifying the dynamics of physiological processes and the environment across different scales.

Remote sensing of photosynthesis:

Part of my research focuses in the use of remote sensing proxies (e.g. thermography and spectroscopy) for integrative estimations of photosynthetic-related traits at crop canopy and larger scales (e.g. regional or global). In particular, I have vast experience in working with the passive measurement of solar-induced chlorophyll fluorescence (SIF), a signal that has emerged as a potential proxy to remotely quantify spatio-temporal dynamics of photosynthetic efficiency. I am interested in exploiting SIF to quantify radiation use efficiency (RUE) and to understand adaptation mechanisms of photosynthesis to abiotic stress at crop level.