In mapme-initiative/mapme.vegetation: Tool chain for Sentinel-2 L2A processing

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 5,
  fig.height = 5
)

In the following, the most important steps of the processing workflow of {mapme.vegetation} are described; the figure guides the reader through these descriptions. The overall goal here is to download the matching Sentinel-2 tiles a spatio-temporal extent of interest and process the downloaded data to derive a dense time series of surface reflectance bands and vegetation indices. On the other end of this process, there are optional additional analysis steps such as the calculation of pixel-wise trends or the extraction of zonal statistics.

• download only relevant bands for a given spatio-temporal extent of Sentinel-2 L2A data
• prepare a custom cloud mask based on the sen2cor Scene Cover Classification (SCL)
• calculate indices and extract surface reflectance bands and aggregate values to a regular sparse timeseries
• apply an interpolation and smoothing strategy to fill in missing data, reduce noise, and create a dense timeseires
• optionally, calculate pixel-wise trends through the time dimension
• optionally, extract zonal statistics of areas of interest, e.g. for diff-in-diff analysis
• optionally, extract pixel time-series, e.g. for later usage with the {mapme.classification}

library(DiagrammeR)

mermaid("
  graph TB

    classDef default fill:#c2dddd,stroke-width:2px,stroke:#034858;

    A(Query & Download)
    B(Apply Cloud Mask)
    C(Calculate VIs and SR)
    D(Dense Time Series)
    E(Trends)
    F(Zonal Statistics)
    G(Pixel Extraction)
    A-->B
    B-->C
    C-->D
    D-->E
    D-->F
    D-->G

")

mapme-initiative/mapme.vegetation documentation built on Feb. 10, 2023, 10:51 p.m.