Mapping malnutrition from space: insights from ECOSTRESS thermal infrared and evapotranspiration
The topic is open and also suitable for GEM students in track 3 – GEM for Ecosystems & Natural Resources.
Suggested elective courses (optional)
- Thermal Infrared Remote Sensing: from Theory to Applications
- Advanced Image Analysis
- Remote Sensing and Modelling of Primary Productivity and Plant Growth
Statistics, agronomy and programming background/experience recommended.
ET is the water lost to the atmosphere through plant stomata during photosynthesis and from the surface following a wetting event. It is an important component of the global water cycle and the energy cycle (as latent heat). Satellite-driven ET models are widely used to monitor crop growth and development. Changes in crop growth and development impact nutrient concentrations in the canopy and grain, but the link with satellite-driven ET remains unexplored. The novelty of this work therefore is not in the method developed, but the application of ET to an important research gap in food security analysis. Time varying information of nutrient levels in crops derived over large spatial areas with satellite data could be integrated into agricultural monitoring platforms to update food consumption tables, inform biofortification research, and address other important interventions to global malnutrition.
The main aim of this topic is to evaluate the performance of ECOSTRESS thermal infrared and related level-3 products, namely evapotranspiration (ET) in estimating the nutrient concentration of macro- and micro-nutrients important to human health (N, Ca, Fe, K, Mg, P, S, Se, and Zn). Nutrient concentrations and biophysical/biochemical indicators were measured in the canopy and grain for three global staple crops (maize, rice, and wheat) in a large commercial farm near Jolanda di Savoia, Italy in 2023 and 2024. These measurements coincide with ECOSTRESS acquisitions during the same period. The topic can be broken down into three main tasks: (i) collect and process ground data; (ii) extract ECOSTRESS and related level-3 products in the sample frames of the nutrient measurements; and (iii) evaluate satellite data performance with multivariate regression or other simple statistical technique. The sample sizes are too small to justify more sophisticated data mining techniques. The research supports an ongoing European Space Agency project: https://www.eo4nutri.nl/.
Zhao, C., Liu, L., Wang, J., Huang, W., Song, X., & Li, C. (2005). Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress. International Journal of Applied Earth Observation and Geoinformation, 7(1), 1-9.