About this Research Topic
More than 30 years ago in 1989, the ground-breaking paper “Remote Sensing of Foliar Chemistry” was published by Paul Curran and meanwhile it is getting close to 1000 citations with numbers increasing every year. This paper kicked-off a new field in quantitative remote sensing with many application areas where foliar biochemical traits are required, for example in rangeland monitoring, biodiversity assessment and agricultural management. Since its publication, substantial progress has been made due to developments in sensors, models, and retrieval methods. VSWIR (400-2500 nm) hyperspectral sensors have been deployed (and still are) to piloted aircraft (AVIRIS, HyMap, etc.) and have recently become available at UAVs which allow cheap and flexible acquisitions. After the initial experimental satellite missions Hyperion/EO-1 and CHRIS/PROBA, recently launched and upcoming satellite missions PRISMA and EnMAP started to pave the way for future operational missions, such as CHIME.
In recent years, a large variety of leaf and canopy radiative transfer models (RTMs), for instance, PROSPECT-PRO, the SAIL family (4SAIL2, 2MSAIL, SLC, etc.) addressing novel aspects of light interaction with vegetation (e.g. protein absorption features, clumping at various scales, etc.) have been developed. Then, retrieval methods grouped into parametric and non-parametric regression, physically based (including RTMs) and hybrid methods, which combine RTM simulations with machine learning regression methods have been proven to be valuable tools in quantitative remote sensing of vegetation. Finally, the state-of-the-art RTMs and retrieval models have been made available to the community via toolboxes such as ARTMO and EnMap-Box and also contribute to a more widespread application of retrieving, mapping, and monitoring of foliar biochemical traits. The overall goal of this collection is to exploit these new data opportunities using advanced retrieval methods and tools for the estimation of foliar biochemical traits within ecological and agricultural contexts.
In view of these developments, specifically the new space-based high-fidelity imaging spectrometers, we believe it is time to collect the most recent advances in the field including research articles and review papers. This Research Topic will encompass studies where hyperspectral data collected from field-based, airborne (piloted aircraft, drones) and satellite platforms have been used to derive foliar biochemical traits such as photosynthetic pigments, water, nitrogen, cellulose, lignin, protein, phenols, and leaf mass per area at the leaf or canopy level using dedicated retrieval methods, with a particular focus on:
- Scaling aspects (from leaf/plant level to satellites);
- Multiple ecological scales;
- Algorithms in RTM, machine learning and hybrid methods;
- Novel biochemical traits, e.g. leaf protein content;
- Retrieval of biochemical traits under varying canopy architecture;
- Demonstration of algorithms generalization ability;
- Uncertainty and error propagation analysis;
- Consistency and portability of models.
We encourage the submission of both research and review studies.
Keywords: Imaging spectroscopy, functional traits, inversion methods, radiative transfer modelling, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.