Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation

Dear Colleagues,

The Special Issue Remote Sensing for Biophysical and Biochemical Properties of Crops is intended to bring together a wide range of contributions from different scales (from leaf level to landscape level) and different EO sensors (active and passive sensors).

Recent developments in geoinformation technology and in earth observation (EO) sensors in particular, include retrieval of seasonal trends of biochemical and biophysical land surface parameters such as foliar pigments content, leaf and soil moisture, surface roughness, albedo, fAPAR, LAI, and canopy height from EO data of different active and passive sensors and with associated uncertainties (e.g., Liang 2004; Gitelson et al 2019; Yang et al 2019). Key issues in estimating biochemical and biophysical land surface parameters are upscaling from leaf to canopy, temporal gap-filling to produce gap-free time-series, and combining data from different sensors and quantification of uncertainties. In this context, EU H2020 project MULTIPLY: “MULTIscale SENTINEL land surface information retrieval Platform” (http://www.multiply-h2020.eu/project-2/) has created a toolbox for solving these problems. In addition, the COST Action CA17134 SENSECO “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (https://www.senseco.eu/) also addresses the same issues.

Physical radiative transfer models, statistical models, and machine learning algorithms are commonly used to retrieve biophysical and biochemical traits from remote sensing data. Phenotyping platforms as well as field studies at different scales provide valuable new data in this topic.

Specific topics for this Special Issue include but are not limited to the following:

• Physical radiative transfer modeling
• Statistical modeling and machine learning
• Vegetation indices and other spectral transformations
• Applicability of different active and passive EO sensors (including SAR, optical and thermal)
• Multi-sensor synergies
• Applications at different scales of proximal and remote sensing (including phenotyping platforms, drones and satellite-borne data)
• Phenology, time series and gap-filling
• Synergies of remote sensing, GIS, and crop growth models
• Downscaling and upscaling of biophysical parameters
• Uncertainty assessment of remotely sensed data
• Uncertainty assessment of ground validation data (including quantified uncertainty assessment protocols for upscaling of biophysical trait measurements to sensor pixel size)

Dr. Lea Hallik
Prof. Tiit Nilson
Dr. Leonidas Toulios
Dr. George P Petropoulos
Guest Editors

References

1. Gitelson, A.; Viña, A; Solovchenko, A.; Arkebauer, T.; Inoue, Y. Derivation of canopy light absorption coefficient from reflectance spectra. Remote Sens. Environ. 2019, 231, 111276. https://doi.org/10.1016/j.rse.2019.111276
2. Liang, S. Quantitative Remote Sensing of Land Surfaces; Wiley-Interscience: Hoboken, NJ, USA,2014
3. Yang, P.; van der Tol, C.; Verhoef, W.; Damm, A.; Schickling, A.; Kraska, T.; Muller, O.; Rascher, U. Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence. Remote Sens. Environ. 2019, 231, 110996, https://doi.org/10.1016/j.rse.2018.11.039

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.