Modelling Leaf Chlorophyll Content in Coffee (Coffea Arabica) Plantations Using Sentinel 2 Msi Data

Coffee leaf chlorophyll (ChI) is an important proxy for coffee plant photosynthetic rates, nitrogen content, leaf health and yield potential. Whereas the recently launched Sentinel 2 multi -spectral instrument (MSI) data has great potential for plant condition assessment, the value of its spectral settings at variable spatial resolutions in relation to crop canopy cover on ChI content prediction remains largely unexplored. In this study, we apply an empirical model to estimate coffee leaf ChI with Sentinel 2 MSI data. Results showed that coffee biophysical parameters (height and canopy cover) are significantly influenced by stand age while plant water concentration and total ChI are age invariant. Results further showed that the best modelling results (R2=0.69, RMSE=64.4) were achieved when all the bands at 10m spatial resolution with all data were used. We concluded that Sentinel 2 MSI is a valuable dataset for predicting coffee leaf ChI, however, based on our findings, we suggest that finer spatial resolutions of 10m on mature coffee stands should be adopted for better prediction results.

• Publication year

  2018

• Venue

  IEEE International Geoscience and Remote Sensing Symposium

• Publication date

  2018-07-01

• Fields of study

  Agricultural and Food Sciences, Mathematics, Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/IGARSS.2018.8518848
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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