Vegetation Phenology from Remote Sensing data: Monitoring, Mapping, and Modelling

Dear Colleagues,

Plant phenology is the study of recurring life cycle stages (e.g., growth reactivation and dormancy, leaf emergence and senescence, and flowering) and is considered as one of the most sensitive indicators of climate and environmental changes. Remote sensing-based techniques, ground-based observations, and bioclimatic models have been commonly used in plant phenology studies. Remote sensing sensors on board different space-borne or airborne platforms (e.g., satellite, aircraft, UAVs) data have the advantage of providing wide coverage with varying temporal, spectral and spatial resolutions; and are therefore widely recognized tools to study phenology in a number of ecosystems such as agriculture, forests, hydrology and grasslands. Several studies have demonstrated that the Normalized Difference Vegetation Index (NDVI) derived from different remote sensing imagery (e.g., MODIS, AVHRR, MERIS; Landsat, Sentinel, UAVs) is one of the reliable spectral indices widely used to study vegetation phenology for the reconstruction of phenological transitions of different vegetation types, including croplands, forests and grasslands. Because it is related to various biophysical parameters (e.g., green-leaf biomass, LAI and FPAR), NDVI time series data are commonly used for spatiotemporal phenology studies. In the last decade, substantial advances have been made in predicting many plant phenological stages (i.e., start and end dates, and length of the growing season) from both very high remote sensing technics (e.g., UAVs, hyperspectral sensing) and ground-based observations.

For this forthcoming Special Issue, we invite manuscripts in all aspects regarding remote sensing on plant phenology and its applications, including croplands, forests and grasslands among others. Both reviews and original research articles on systems, hardware, or algorithms are welcome. Reviews should provide an up-to-date overview of the state-of-the-art technologies such as existing methods for plant phenology development stages tracking/detection and emerging new techniques based on the analysis of time-series, multispectral, hyperspectral, and thermal remote sensing imagery. Original research papers should focus on new approaches; solve an important problem in plant phenology-based remote sensing; or any other no-contact proximal plant phenology sensing topics that have experienced significant advancements in the past decade (e.g., multi-sensors on board UAVs; chlorophyll fluorescence; mapping interface; mobile devices and apps; among others). We also encourage and welcome manuscript from developing countries (i.e., African countries). If you have ideas to discuss before submission, please feel free to contact us. We look forward to receiving your manuscript submitted to this Special Issue.

Dr. Ahmed Laamrani
Dr. Abdelghani Chehbouni
Dr. Abdelghani Boudhar
Dr. Tarik Benabdelouahab
Guest Editors

Manuscript Submission Information

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