Special Issue "Land Surface Phenology and Seasonality: Novel Approaches and Applications"
Deadline for manuscript submissions: closed (28 February 2017).
Interests: land surface phenology; ecological remote sensing; grasslands; croplands; urban areas; land cover/land use change
Interests: vegetation and soil dynamics; global Earth system modeling; global carbon cycle modeling; terrestrial and marine biogeochemistry; model benchmarking and model–data integration; high performance computational science; large scale Earth system data analytics and machine learning; remote sensing; ecological modeling
Special Issues and Collections in MDPI journals
Interests: remote sensing; landscape ecology; forest ecology; hydrology; clustering; classification; feature extraction; machine learning; data mining; algorithms; parallel and distributed computing
Interests: biomass burning emissions; burned area; fire seasonality; climate change; real-time monitoring; remote sensing.
Special Issues and Collections in MDPI journals
Special Issue in Remote Sensing: Remote Sensing of Biomass Burning
Special Issue in Remote Sensing: Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery
Special Issue in Remote Sensing: Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data
Special Issue in Fire: Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data
The rapid pace of land surface phenology (LSP) monitoring and modeling has positioned the field to make significant advances in the coming decade. The primary lesson to be learned from the past 15 years of LSP research is that there is no single approach to LSP that fits all situations. The community is starting to explore approaches to LSP monitoring and modeling that embrace suites of sensors and algorithms toward developing biome-tuned LSP models. Land surface seasonality (LSS) is a recent concept that could be used in tandem with LSPs to tackle biome-specific monitoring and modeling. For example, the seasonality of soil freeze/thaw is a key transition in ecosystem processes and one that can be monitored effectively using microwave frequencies with passive and active sensors.
Although most of the LSP literature has focused on green-up dynamics, it is necessary to move beyond a focus on spring. Recent work on the dynamics of autumnal senescence has demonstrated some novel approaches, but there is much more to explore in monitoring and modeling the processes of canopy coloring, nutrient retranslocation, drying, and foliage abscission.
Much of the LSP literature has focused on optical imagery and on very few vegetation indices. It is time to explore the possibilities of incorporating multiple remote sensing modalities beyond the visible to near infrared end of the spectrum.
Many LSP studies have focused on natural landscapes and ecosystems, but we should also leverage our understanding of human-managed systems, whether in croplands or urbanized areas, to advance LSP monitoring and modeling.
Very little research has been done to date on the influence of LSPs and LSSs on the spatial structure of surface characteristics and vice versa. A few field studies have shown how the spatial pattern of reflectance changes during the growing season. Spatial analyses of image time series have revealed characteristic seasonal patterns in reflectance, emittance, and backscattering that can enable the detection and evaluation of change. With the increased accessibility of the Landsat archive, this avenue of LSP research could be very fruitful area in the coming decade.
Cross-calibration of LSP metrics with other indicators of phenology has been studied since the launch of ERTS (Landsat-1) in 1972. More recent efforts to cross-calibrate estimates of phenophases have found a tendency for LSP timings to be early relative to a suite of bioclimatic. Which sorts of data constitute appropriate reference sets for ground-level phenological observations remains an open question with multiple regional solutions tuned to specific vegetation assemblages the most likely answer. However, it is clear that the community needs coordinated observations across multiple scales to link landscape heterogeneity to pixel variability. The use of flux tower observations and "phenocams" for cross-calibration are critical, but there is another source of finer spatial resolution remote sensing data that promises a rich source for cross-calibration efforts, viz., the global Landsat data record.
Validation of land surface products is the proverbial "elephant in the room". Note that we say land surface products and not land surface phenology products. The challenge facing the remote sensing community is larger than validation of just LSPs. The Land Product Validation Subgroup (LPVS) of the Committee on Earth Observation Systems (CEOS) Working Group on Calibration and Validation (WGCV) has been active in a number of areas (http://lpvs.gsfc.nasa.gov), including land surface phenology (http://lpvs.gsfc.nasa.gov/pheno_home.html). Despite an effort to self-organize, progress in bringing the LSP community together to engage in validation exercises has been slow, compared to what has been accomplished for leaf area index (LAI) retrievals. This situation is due, in large part, to a lack of funding for a validation campaign, but it is also attributable to (a) the relative scale-invariance of intensive variables like vegetation indices, (b) the sensitivity of vegetation indices to sensor band centers and bandwidths, (c) the lack of sharply defined phenometrics, and (d) the various ways to generate phenometrics from image time series.
We invite you to submit articles concerning your recent research in modeling and/or measuring and monitoring land surface phenologies and seasonalities with respect to the following topics:
- Beyond NDVI and EVI: using narrowband spectral indices to capture phenophase transitions
- Beyond VNIR: using longwave sensors (active and passive) to capture phenophase transitions
- LSPs from solar induced fluorescence (SIF)
- LSPs in croplands
- LSPs in grasslands, savannas, and shrublands
- LSPs in tropical ecosystems, including croplands
- LSPs in and around cities
- LSPs in mountain ecosystems
- LSPs at high latitudes
- LSPs in arid ecosystems
- LSPs in lotic, lentic, estuarine, and marine ecosystems
- LSPs and spatial and spatio-temporal patterning
- Cross-calibration of LSPs
- Validation of LSPs
Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.
Dr. Geoffrey M. Henebry
Dr. Forrest M. Hoffman
Dr. Jitendra Kumar
Dr. Xiaoyang Zhang
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind 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 2400 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.