Rgee example

1. Get data

# load packages
library(raster)
library(dplyr)
library(sf)
library(rgee)

# load delhi administrative boundaries
y<-read_sf("delhi_administrative/new_delhi_wards.json") %>%
  st_transform(4326)

# Move that geometry from local to earth engine
dat_poly <- as_Spatial(y)

# Initialize Earth Engine
ee_Initialize()

# Use a few days in 2020
startDate = ee$Date('2020-01-01');
endDate = ee$Date('2020-01-02');

# Polygons collection
coords <- as.data.frame(raster::geom(dat_poly))
polygonsFeatures <- coords %>% split(.$object) %>% purrr::map(~{  
  ee$Feature(ee$Geometry$Polygon(mapply( function(x,y){list(x,y)} ,.x$x,.x$y,SIMPLIFY=F)))
})

# Open the ERA5 dataset
ImageCollection = ee$ImageCollection('ECMWF/ERA5_LAND/HOURLY')$
  filter(ee$Filter$date(startDate, endDate))$
  filter(ee$Filter$listContains("system:band_names", "temperature_2m"))$
  filterBounds(polygonsCollection)

polygonsCollection = ee$FeatureCollection(unname(polygonsFeatures))

# Function to calculate means for all dates
calcMean <- function(image) {
  image$reduceRegions(collection = polygonsCollection,reducer= ee$Reducer$mean())
}

# calculate means for the images in collection
DayMeans <- ImageCollection$map(calcMean)$flatten()

# output
task_vector <- ee_table_to_drive(
  collection = DayMeans,
  fileFormat = "CSV",
  fileNamePrefix = "DayMeans"
)
task_vector$start()
ee_monitoring(task_vector)

3. Plot

# load
library(sf)

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

f<-read.csv("C:/Users/mark.cherrie/Downloads/test_2021_01_06_21_33_08 (1).csv")

# append to the sf data
LSsf = st_as_sf(cbind.data.frame(f,geojsonsf::geojson_sf(f$.geo)))

# plot
plot(LSsf["temperature_2m"])

I modified code from here: https://stackoverflow.com/questions/58980048/using-r-to-process-google-earth-engine-data and others.

Rgee example

Mark Cherrie

04/08/2022

1. Get data

2. Manually download from google drive

3. Plot