Features

rgeeExtra serves as a wrapper for the Python package named eeExtra. The creation of eeExtra was driven by a need to consolidate various third-party GEE Javascript and Python packages and projects found on GitHub in the same programming language and style, avoiding dependencies. rgeeExtra ensures a seamless integration of eeExtra within the R ecosystem

Key features of rgeeExtra include:

1. Automatic Scaling and Offsetting: Simplifying the adaptation and visualization of images and datasets.

2. Spectral Indices Computation: Harnessing the capabilities of Awesome Spectral Indices, it allows for the calculation of a wide range of spectral indices from satellite imagery.

3. Clouds and Shadows Masking: An essential tool for processing satellite images, it identifies and masks areas impacted by clouds and their shadows.

4. STAC-related Functions: Streamlining interactions with the SpatioTemporal Asset Catalog (STAC), standardizing the description of spatiotemporal datasets.

5. Operator Overloading: Provides a more direct and simplified interaction with data, enhancing code clarity and efficiency.

library(rgeeExtra)
library(rgee)

ee_Initialize()        # Initialize the Google Earth Engine API connection
extra_Initialize()     # Initialize the extended functionalities of rgeeExtra

ee$ImageCollection("NASA/GPM_L3/IMERG_V06") %>%
  ee$ImageCollection$first() %>%
  ee$ImageCollection$getOffsetParams()

ee$ImageCollection("NASA/GPM_L3/IMERG_V06") %>%
  ee$ImageCollection$first() %>%
  ee$Image$getScaleParams()

s2_indices <- ee$ImageCollection("COPERNICUS/S2_SR") %>%
  ee$ImageCollection$first() %>%
  ee$Image$preprocess()%>%
  ee$Image$spectralIndex(c("NDVI", "SAVI"))

names(s2_indices)

img <- ee$ImageCollection("COPERNICUS/S2_SR") %>%
  ee$ImageCollection$first() %>%
  ee$Image$maskClouds(prob = 75,buffer = 300,cdi = -0.5)

names(img)

# get metadata
ee$ImageCollection("NASA/GPM_L3/IMERG_V06") %>%
  ee$ImageCollection$first() %>%
  ee$Image$getSTAC() %>%
  ee$Image$getInfo()

# get citation
ee$ImageCollection("NASA/GPM_L3/IMERG_V06") %>%
  ee$ImageCollection$first() %>%
  ee$Image$getDOI()

5. Third-party repositories

5.1 Apply pansharpening

Apply panchromatic sharpening to an ee$Image or an ee$ImageCollection. Optionally, run quality assessments between the original and sharpened Image to measure spectral distortion and set results as properties of the sharpened Image.

img <- ee$Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819")
img_sharp <- ee$Image$panSharpen(img, method="HPFA", qa=c("MSE", "RMSE"), maxPixels=1e13)

Map$centerObject(img)
Map$addLayer(img_sharp, list(bands=c("B4", "B3", "B2"))) |
Map$addLayer(img, list(bands=c("B4", "B3", "B2")))