Measuring the Timing of Woody Green-Up in African Savannas - Which Modis Data to Use?

Error inherent in satellite remote sensing data due to the imaging process needs to be reduced to allow accurate measurement of vegetation phenology patterns. Compositing - and the selection of images and spectral data - is an important process to this end. Here, the success of cloud and viewing geometry masking algorithms employed to remove imaging error in the MODIS 250m 16-day vegetation index product is investigated. Green-up dates estimated using these data and those estimated by removing cloud-affected pixels manually are compared with much finer resolution Sentinel-210m and Planetscope 3m imagery for 208 sample canopies in four major vegetation types across a diverse 400km2 savanna landscape in south-eastern Zimbabwe. RMSE was reduced by 10 days and the identification of spurious early green-up largely avoided by removing cloud-affected pixels by eye. Treating data from the Aqua and Terra platforms separately also reduced spurious early green-up dates.

• Publication year

  2021

• Venue

  2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

• Publication date

  2021-07-11

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/IGARSS47720.2021.9554991
• 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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