Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F23%3A00572902" target="_blank" >RIV/86652079:_____/23:00572902 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14310/23:00131110 RIV/00216208:11310/23:10468045
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/01431161.2023.2216847?scroll=top&needAccess=true&role=tab" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/01431161.2023.2216847?scroll=top&needAccess=true&role=tab</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/01431161.2023.2216847" target="_blank" >10.1080/01431161.2023.2216847</a>
Alternative languages
Result language
angličtina
Original language name
Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods
Original language description
Plant functional traits (e.g. leaf pigment and water contents, specific leaf area) serve as important indicators of plant condition, both within a given vegetation season and between years. Remote sensing-based methods allow for non-destructive and repeatable monitoring of the Earth's surface and thus offer an efficient way to map and monitor these traits. In our study, we used a large database of ground survey data sampled at several contrasting phenological phases of vegetation to develop and compare different machine learning models trained to estimate selected plant functional traits at two different sites: mixed floodplain forest at Lanzhot and beech forest at Stitna, both in the Czech Republic. Empirical models were trained as predictors using 1) Sentinel-2 satellite data (a data set with higher spatial and spectral resolution), and 2) Harmonized Landsat Sentinel-2 (HLS) product (a data set with higher temporal resolution). The most successfully retrieved traits were chlorophyll and carotenoid content (R-2 = 0.78 and 0.65, respectively). Although models trained with Sentinel-2 predictors proved to be slightly better in terms of validation statistics compared to HLS predictors, the HLS product may be preferable for some applications requiring analysis at a high frequency. The best-performing machine learning algorithm, canonical correlation forest, was then applied per pixel to all cloud-free images from the HLS product at both study sites for the years 2019-2021. This allowed us to create a time series of plant functional traits useful for observing differences between the two sites, as well as between growing seasons, and also to observe patterns of spatial behaviour using map outputs.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
<a href="/en/project/LTAUSA18154" target="_blank" >LTAUSA18154: Assessment of ecosystem function based on Earth observation of vegetation quantitative parameters retrieved from data with high spatial, spectral and temporal resolution</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Remote Sensing
ISSN
0143-1161
e-ISSN
1366-5901
Volume of the periodical
44
Issue of the periodical within the volume
10
Country of publishing house
GB - UNITED KINGDOM
Number of pages
23
Pages from-to
3083-3105
UT code for WoS article
000997638600001
EID of the result in the Scopus database
2-s2.0-85162070165