Novel Approaches for Mapping and Monitoring of Vegetation Properties from Earth Observation (EO) Data in an Agricultural Context

Dear Colleagues,

The use of digital information for more efficient cultivation of large areas has become a part of agricultural practices worldwide. In this context, the use of satellite data allows for the regular and cost-effective quantification of biophysical and biochemical variables with increasingly fine spatial and spectral resolutions. Such monitoring is essential for understanding the state and dynamics of the cropped surface which, in turn, facilitates consideration of the environmental impacts of agricultural practices. Classical canopy traits involve leaf area index (LAI), chlorophyll content (Cab), fractional vegetation cover (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), aboveground biomass (AGB), as well as leaf/canopy water content (EWT/CWC). However, the estimation of more subtle or non-state variables, such as the contents of protein, nitrogen, leaf carotenoid, and anthocyanin, or non-photosynthetically active vegetation (lignin + cellulose), is also of great interest to agricultural service providers. Given the vast data streams of upcoming spaceborne imaging spectroscopy missions, fast and efficient retrieval techniques—exploiting the full spectral range—should be developed. Techniques which can be implemented onboard are particularly valuable as they reduce the data downlink bottleneck faced in hyperspectral satellite imaging. This can be realized, for instance, by machine learning regression algorithms (MLRAs) able to handle the strong nonlinearity between observed reflectance signals and biophysical/biochemical traits. Those MLRAs also offer an interesting linkage to radiative transfer models (RTMs), where the latter provide the necessary training databases without the requirement for in situ data. These so-called “hybrid approaches” combine the generic properties of RTMs with the flexibility and efficiency of nonparametric methods and, therefore, can be used to exploit the vast superspectral and imaging spectrometer data streams for application in agricultural areas.

This Special Issue strongly encourages contributions aimed at estimating the biochemical and biophysical quantities in an agricultural context using the full range of available optical remote sensing data, and that capitalize upon statistical nonparametric, physically based, or innovative hybrid methods.

This Special Issue was also established to collect contributions for the related Special Session “Novel Approaches for Agricultural Monitoring” of the Whispers conference, taking place in Amsterdam, the Netherlands, on 24–26 September 2019.

Dr. Katja Berger
Prof. Dr. Clement Atzberger
Dr. Uta Heiden
Dr. Stephanie Delalieux
Dr. Stefan Livens
Guest Editors

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• Cropland status and dynamics
• Crop biophysical and biochemical variables
• Precision agriculture
• Imaging spectrometer missions
• Superspectral missions
• Machine learning regression algorithms
• Radiative transfer modeling
• Onboard techniques
• Hybrid inversion techniques