Retrieval of Harmonized LAI Product of Agricultural Crops from Landsat OLI and Sentinel-2 MSI Time Series

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F22%3A00567215" target="_blank" >RIV/86652079:_____/22:00567215 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11310/22:10455414

Result on the web

<a href="https://www.mdpi.com/2077-0472/12/12/2080" target="_blank" >https://www.mdpi.com/2077-0472/12/12/2080</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/agriculture12122080" target="_blank" >10.3390/agriculture12122080</a>

Result language

angličtina

Original language name

Retrieval of Harmonized LAI Product of Agricultural Crops from Landsat OLI and Sentinel-2 MSI Time Series

Original language description

In this study, an approach for the harmonized calculation of the Leaf Area Indices (LAIs) for agronomic crops from Sentinel-2 MSI and Landsat OLI multispectral satellite data is proposed in order to obtain a dense seasonal trajectory. It was developed and tested on dominant crops grown in the Czech Republic, including winter wheat, spring barley, winter rapeseed, alfalfa, sugar beet, and corn. The two-step procedure harmonizing Sentinel-2 MSI and Landsat OLI spectral data began with deriving NDVI, MSAVI, and NDWI_1610 vegetation indices (VIs) as proxy indicators of green biomass and foliage water content, the parameters contributing most to a stand's spectral response. Second, a simple linear transformation was applied to the resulting VI values. The regression model itself was built on an artificial neural network, then trained on PROSAIL simulations data. The LAI estimates were validated using an extensive dataset of in situ measurements collected during 2017 and 2018 in the lowlands of the Central Bohemia Region. Very strong agreement was observed between LAI estimates from both Sentinel-2 MSI and Landsat OLI data and independent ground-based measurements (r between 0.7 and 0.98). Very good results were also achieved in the mutual comparison of Sentinel-2 and Landsat-based LAI datasets (rRMSE < 20%, r between 0.75 and 0.99). Using data from all currently available Sentinel-2 (A/B) and Landsat (8/9) satellites, a dense harmonized LAI time series can be created with high potential for use in precision agriculture.

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

40101 - Agriculture

Project

<a href="/en/project/TH02030248" target="_blank" >TH02030248: Use of Copernicus satellite data for an effective monitoring of a status and management of selected agricultural crops</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Agriculture-Basel

ISSN

2077-0472

e-ISSN

2077-0472

Volume of the periodical

12

Issue of the periodical within the volume

12

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

2080

UT code for WoS article

000900236700001

EID of the result in the Scopus database

2-s2.0-85144735552