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Integrating Copernicus LMS with ground measurements data for leaf area index and biomass assessment for grasslands in Poland and Norway

The integration of satellite data from the Copernicus Land Monitoring Service (CLMS) with ground-based measurements represents a pioneering approach to the assessment of leaf area index (LAI) and in situ biomass at grasslands in Poland and Norway. The aim of this study was to develop a method for as...

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Bibliographic Details
Published in:International journal of digital earth 2024-12, Vol.17 (1)
Main Authors: Dąbrowska-Zielińska, Katarzyna, Wróblewski, Konrad, Goliński, Piotr, Malińska, Alicja, Bartold, Maciej, Łągiewska, Magdalena, Kluczek, Marcin, Panek-Chwastyk, Ewa, Ziółkowski, Dariusz, Golińska, Barbara, Markowska, Anna, Paradowski, Karol
Format: Article
Language:English
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Summary:The integration of satellite data from the Copernicus Land Monitoring Service (CLMS) with ground-based measurements represents a pioneering approach to the assessment of leaf area index (LAI) and in situ biomass at grasslands in Poland and Norway. The aim of this study was to develop a method for assessing grass growth conditions and predicting biomass yield based on Sentinel-2 data and CLMS products. LAI values derived from CLMS were compared with in-situ measurements on grassland plots in the two regions of Podlaskie (PL84) and Wielkopolskie (PL41). Small random statistical errors observed in the differences represent a significant opportunity to use Copernicus data to develop a relationship model for biomass prediction. The NDII index calculated using Sentinel2 was considered important for biomass assessment in the humid areas of Norway. This relational biomass prediction model could provide valuable information to farmers, improving their ability to manage grasslands effectively. As a result of the research, relational models were developed has been developed to predict grassland fresh biomass yield with an R2 accuracy of 0.72 for the first cut, 0.81 for the second cut and 0.91 for the third cut. This allows farmers to effectively manage and monitor grassland throughout the growing season.
ISSN:1753-8947
1753-8955
DOI:10.1080/17538947.2024.2425165