Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F21%3A00562849" target="_blank" >RIV/86652079:_____/21:00562849 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0034425720305411?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0034425720305411?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rse.2020.112168" target="_blank" >10.1016/j.rse.2020.112168</a>

Result language

angličtina

Original language name

Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring

Original language description

For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (52) satellites constellation offers the possibility to estimate brown LAI (LAI(B)) next to green LAI (LAI(G)). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAI(G) and LAI(B), providing alongside associated uncertainty estimates, which allows to mask out unreliable LAI retrievals with higher uncertainties. A processing chain was implemented to apply both models to 52 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with insitu data from various European study sites (LAI(G): R-2 = 0.7, RMSE = 0.67 m(2)/m(2), LAI(B): R-2 = 0.62, RMSE = 0.43 m(2)/m(2)). Thanks to the 52 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B12: 2190 nm) a distinction between LAI(G) and LAI(B) can be achieved. To demonstrate the capability of LAI(B) to identify when crops start senescing, 52 time series were processed over multiple European study sites and seasonal maps were produced, which show the onset of crop senescence after the green vegetation peak. Particularly, the LAI(B) product permits the detection of harvest (i.e., sudden drop in LAI(B)) and the determination of crop residues (i.e., remaining LAI(B)), although a better spectral sampling in the shortwave infrared would have been desirable to disentangle brown LAI from soil variability and its perturbing effects. Finally, a single total LAI product was created by merging LAI(G) and LAI(B) estimates, and then compared to the LAI derived from 52 L2B biophysical processor integrated in SNAP. The spatiotemporal analysis results confirmed the improvement of the proposed descriptors with respect to the standard SNAP LAI product accounting only for photosynthetically active green vegetation.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10618 - Ecology

Project

—

Continuities

—

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Remote Sensing of Environment

ISSN

0034-4257

e-ISSN

1879-0704

Volume of the periodical

255

Issue of the periodical within the volume

MAR 15

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

112168

UT code for WoS article

000619232500004

EID of the result in the Scopus database

2-s2.0-85096520859