Quantifying priority vegetation traits from spaceborne imaging spectroscopy data
Proposers: Katja Berger, Martin Schlerf and Jochem Verrelst
In recent years, substantial progress has been made in quantitative remote sensing of vegetation due to the developments in sensors, models, and retrieval methods. After the two initial experimental satellite missions, Hyperion/EO-1 and CHRIS/PROBA, the just-launched or near-term spaceborne missions PRISMA, DESIS and EnMAP started to pave the way for future operational missions, such as CHIME, SBG and FLEX. Thanks to the provision of contiguous spectral data in the visible, near and shortwave infrared (VIS-NIR-SWIR), these hyperspectral missions open opportunities to develop new retrieval models for the inference of multiple vegetation traits. For instance, the following traits have been identified as a priority for the CHIME mission: non-photosynthetic vegetation/biomass, canopy water content, leaf and canopy pigment content (i.e., chlorophyll, carotenoids & anthocyanins content), canopy nitrogen content, yield quality (protein content of grains/cobs, energy content, fodder quality), crop development stages/phenology, specific leaf area (SLA) and micronutrients (phosphorus, potassium, sulphur). The FLEX mission, instead, will be dedicated to the global monitoring of photosynthesis, based on chlorophyll fluorescence and additional products. With respect to retrieval methods, a large variety of leaf and canopy radiative transfer models (RTMs) address novel aspects of light interaction with vegetation. Further, innovative machine learning or hybrid methods have been demonstrated to be valuable tools in quantitative remote sensing of vegetation. Scientific studies were provided, among others, by scientists involved in the COST Action “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (SENSECO). In the context of SENSECO, various spatial and temporal scales are considered that enable synergistic multi-sensor use. Besides SENSECO, several other research groups focus on the quantification of essential vegetation traits from hyperspectral spaceborne missions, progressing towards routine mapping applications in operational frameworks.
Given these developments, we believe it is time to collect the most recent advances in the field. This special session aims to address contributions where hyperspectral data were collected (or simulated) from spaceborne platforms to derive vegetation priority traits. A particular focus will be on:
- Retrieval of CHIME / FLEX priority traits
- Algorithms using RTMs, machine learning and hybrid methods
- Scaling aspects (from leaf/plant level to satellites)
- PRISMA, DESIS, EnMAP (simulated) sensors
- Demonstration of algorithms generalisation ability
- Consistency and portability of models
- Time series and sensor synergies