get.pixel.data {spectralR}

Obtain Sentinel-2 spectral reflectance data for user-defined vector polygons

Description

The function takes sf polygon object, created through prepare.vector.data function, and retrieves data frame with brightness values for each pixel intersected with polygons, for each optical band of Sentinel-2 sensor, marked according to the label of surface class from the polygons.

Usage

get.pixel.data(sf_data, startday, endday, cloud_threshold, scale_value)

Arguments

sf_data

polygons of surface classes as a sf object, created through prepare.vector.data

startday

starting day for Sentinel image collection, as "YYYY-MM-DD". See Note 1 below

endday

final day for Sentinel image collection, as "YYYY-MM-DD"

cloud_threshold

maximum percent of cloud-covered pixels per image by which individual

scale_value

the scale of resulting satellite images in meters (pixel size). See Note 2 below

Value

A dataframe (non-spatial) with unscaled reflectance data for each pixel of median satellite image, for each optical band of Sentinel-2 sensor, marked according to the label of surface class from the polygons.

Note 1. Particular satellite imagery is typically not ready for instant analysis - it contains clouds, cloud shadows, athmospheric aerosols, and may cover not all the territory of your interest. Another issue is that each particular pixel slightly differs in reflectance between images taken on different days due to differences in atmospheric conditions and angle of sunlight at the moments images were taken. Google Earth Engine has its own build-in algorithms for image pre-processing, atmospheric corrections and mosaicing, which allows to obtain a ready-to-use, rectified image. The approach used in this script is to find a median value for each pixel between several images within each of 10 optical bands and thereby make a composite image. To define a set of imageries between which we will calculate the median, we should set a timespan defining starting and final days. Sentinel-2 apparatus takes a picture once a 5 days, so if you set up a month-long timesnap, you can expect each pixel value to be calculated based on 5 to 6 values.

Note 2. You may set up any image resolution (pixel size) for satellite imagery with GEE, but this is hardly reasonable to set the finer resolution than the finest for satellite source. The finest resolution for Sentinel data is 10 m, while using higher scale_value requires less computational resources and returns a smaller resulting dataframe. Although sampling satellite data performs in a cloud, there are some memory limitations placed by GEE itself. If you are about to sample really large areas, consider setting a higher 'scale' value (100, 1000). More about GEE best practices: https://developers.google.com/earth-engine/guides/best_practices

Examples

## Not run: 
# Downlad spectral reflectance data
reflectance <-  get.pixel.data(
  sf_data = sf_df,
  startday = "2019-05-15",
  endday = "2019-06-30",
  cloud_threshold = 10,
  scale_value = 100)

head(reflectance)

## End(Not run)