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Zeiner, Niklas, 2021. Using Sentinel-2 Satellite Images to Estimate Traits of Forage Grasslands. Second cycle, A2E. Umeå: SLU, Dept. of Agricultural Research for Northern Sweden

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In this project, regression models based on data from field measurements and spectral information extracted from satellite imagery were used to estimate traits of forage grasslands; dry matter yield, canopy average height and total leaf chlorophyll. Four fields at SLUs Röbäcksdalen field station were sampled on 22 occasions and a total of 198 samples, including measurement of the highest plant, canopy height, leaf chlorophyll content, canopy spectral reflectance and biomass were collected. Two regression methods, partial least squares (PLS) and support vector machines (SVM), were used to build regression models using different subsets of the available spectral information. Model calibration was performed with 2/3 of the dataset and model validation was performed with the remaining 1/3 of the dataset. It was shown that the models built with SVM outperformed the models built with PLS, during both calibration and validation as well as for all different traits and subsets of spectral information. Field measurement and regression model results were discussed and limitations, their significance and possible improvements were considered. It was concluded that using spectral information from satellite images is a promising approach for estimation of traits in the field and could be used to build tools as a tool to support farmers’ decision making.

Main title:Using Sentinel-2 Satellite Images to Estimate Traits of Forage Grasslands
Authors:Zeiner, Niklas
Supervisor:Morel, Julien and Parsons, David
Examiner:Stenberg, Bo
Volume/Sequential designation:UNSPECIFIED
Year of Publication:2021
Level and depth descriptor:Second cycle, A2E
Student's programme affiliation:NY011 Agricutural programme - Soil/Plant, 300.0hp
Supervising department:(NL, NJ) > Dept. of Agricultural Research for Northern Sweden
Keywords:Remote sensing, Forage grasslands, Sentinel-2, Dry matter yield, Canopy average height, Total leaf chlorophyll content, Partial least squares, Support vector machines
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Subject. Use of subject categories until 2023-04-30.:Agricultural research
Plant physiology - Growth and development
Mathematics and statistics
Deposited On:26 Feb 2021 08:09
Metadata Last Modified:22 Apr 2024 08:13

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