class: title-slide, left, middle <h1> Google Earth Engine <br> for hydrology in R <br> <h3> Abdou Khouakhi <br> <em> Cranfield University </em> </h3> <br> <br> <svg aria-hidden="true" role="img" viewBox="0 0 512" style="height:1em;width:em;vertical-align:-0.125em;margin-right:0.2em;font-size:inherit;fill:lightgrey;overflow:visible;position:relative;"></svg> [@ab_khouakhi](https://twitter.com/ab_khouakhi) .title-logo-box[] ************************************************************ --- # Outline .middle[ - #### Introduction to Google Earth Engine (GEE) and `rgee` Package - #### Quick demo 1: hydrological data retrieval using `rgee` - #### Quick demo 2: Simple example of machine learning in GEE **Notes:** - This presentation is accompanied by an [R script](https://github.com/hydrosoc/rhydro_vEGU21/blob/main/presentations/05.GEE_AK/GEE_AK.R) to execute in Rstudio - You need to sign up for a [Google Earth Engine account](https://signup.earthengine.google.com/#!/) to be able to access data and computing capabilities. ] -- --- layout: false class: inverse, center, middle # Google Earth Engine -- --- # Google Earth Engine - What is it? Google Earth Engine is a computing platform that allows users to run geospatial analysis on Google's infrastructure. The main components of GEE: - [Datasets](https://developers.google.com/earth-engine/datasets/): A petabyte-scale archive of publicly available remotely sensed imagery and other data. - [Compute power](https://reader.elsevier.com/reader/sd/pii/S0034425717302900?token=2AC58EA43ED5306A13DA0E6AF50A5F286D59B455B45F52F9932FE71845E5C75C706C78E079BC3769B23B256612A225D0): Google’s computational infrastructure optimized for parallel processing of geospatial data. - [APIs](https://developers.google.com/earth-engine/guides): APIs for JavaScript and Python (hosted on GitHub) for making requests to the Earth Engine servers. - [Code Editor](https://code.earthengine.google.com/): An online Integrated Development Environment (IDE) for rapid prototyping and visualization of complex spatial analyses using the Javascript API. - [Apps](https://www.earthengine.app/): A dynamic, publicly accessible user interface for Earth Engine analyses. -- --- ## Data structure in GEE - Data in GEE are structured in Collections; a stack or time series of images or gridded datasets and features - For example, all of the images produced by a single sensor are grouped together and presented as a “collection”. - Collections can be filtered, sorted, joined, mapped, or reduced - An Image can be a Stack of Georeferenced bands - Each band has its own Mask, Projection, Resolution and a list of properties (Date, bbox..) <img src="GEE_AK_files/figure-html/unnamed-chunk-1-1.png" width="504" style="display: block; margin: auto;" /> -- --- # Interacting with GEE There are several ways to interact with the platform: .pull-left[ - [Explorer](https://explorer.earthengine.google.com/) - [Javascript Code Editor](https://code.earthengine.google.com/) - [Python wrapper library](https://github.com/google/earthengine-api/tree/master/python) - [Calling EE from within R](https://github.com/r-spatial/rgee) - [Using QGIS EE plugin](https://gee-community.github.io/qgis-earthengine-plugin/) <br/><br/> <img src="./static/img/Picture5.png" width="80%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="https://r-spatial.github.io/rgee/logo.png" width="60%" style="display: block; margin: auto;" /><img src="http://www.gisandbeers.com/wp-content/uploads/2020/12/Imagenes-Google-Earth-en-QGIS.jpg" width="60%" style="display: block; margin: auto;" /><img src="./static/img/Picture4.png" width="60%" style="display: block; margin: auto;" /> ] -- --- # `rgee` package ([Aybar et al. 2021](https://cloud.r-project.org/web/packages/rgee/index.html)) - A package for calling [Google Earth Engine API](https://developers.google.com/earth-engine/) from within R - [Several functions](https://r-spatial.github.io/rgee/reference/index.html) have been implemented to simplify the connection with the R spatial ecosystem - Details of rgee setup and examples of scripts can be found [here](https://csaybar.github.io/rgee-examples/) .pull-left[ Why rgee? - Various visualization options - Many functions to seamlessly interact with GEE API - Easier transition to a web application such as Shiny - Reproducibility ] .pull-right[ <img src="https://r-spatial.github.io/rgee/logo.png" width="80%" style="display: block; margin: auto 0 auto auto;" /> ] -- --- class: inverse, center, middle # Quick demo: GEE Data retrieval ```r # You will need the following libraries library(rgee,quietly = T) library(tidyverse) library(sf) library(lubridate) library(stars) library(raster) library(cptcity) ``` ```r # Initialize ee ee_Initialize(drive = T,quiet = T) ``` -- --- ## Select data sources We are going to obtain some hydrological datasets for the upper catchment of the Niger basin. We define an approximate location within the basin, retrieve the basin boundary, river network and other layers. ```r # get a rough location within a given catchment area poi <- ee$Geometry$Point(-9.3, 10.38) # change here to your catchment approx. location ``` Available data collections can be found at the [GEE data catalog](https://developers.google.com/earth-engine/datasets). Alternatively, you can run `ee_utils_dataset_display()`, a helper functions to open a web browser and search the catalog. .tiny[ ```r # Hydrosheds -Basins - level 4 (change the level for subcatchments) hybas4 <- "WWF/HydroSHEDS/v1/Basins/hybas_4" # River network ffriver <- "WWF/HydroSHEDS/v1/FreeFlowingRivers" # SMAP soil moisture 10km smap10km <- "NASA_USDA/HSL/SMAP10KM_soil_moisture" # SM2RAIN downscaled monthly precip 1km mth_prcp1km <- "OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01" # JRC Global Surface Water Mapping Layers 30m srfce_wtr30m <- "JRC/GSW1_2/GlobalSurfaceWater" # Copernicus landcover 100m cgls100m <- "COPERNICUS/Landcover/100m/Proba-V-C3/Global/2019" # ALOS global digital surface model 30m alos_dsm <- 'JAXA/ALOS/AW3D30/V3_2' # Sentinel 2- Surface reflectance S2SR <- "COPERNICUS/S2_SR" ``` ] -- --- ## Retrieve and visualize the data .tiny[ Get the data ```r # get the basin boundary basin <- ee$FeatureCollection(hybas4)$filterBounds(poi) # river network riverNet <- ee$FeatureCollection(ffriver)$filterBounds(basin) # get July rainfall over the basin mth_prcp_jul <- ee$Image(mth_prcp1km)$clip(basin)$select('jul') # get SMAP soil moisture and select ssm band (layer) over the basin smap_sm <- ee$ImageCollection(smap10km)$filterBounds(basin)$select("ssm")$first()$clip(basin) # get the surface Water- select frequency with which water was present jrc_srfce_wtr <- ee$Image(srfce_wtr30m)$select("occurrence")$clip(basin) # get land cover CGLC 15-19 and select the landcover classes clc2019 <- ee$Image(cgls100m)$select("discrete_classification")$clip(basin) # get ALOS global digital surface model 30m and select DSM alos_elev <- ee$ImageCollection(alos_dsm)$filterBounds(basin)$select("DSM")$mosaic()$clip(basin) ``` Visualization parameters ```r # define visualisation parameters swater_vis <- list(min = 0, max = 100, palette = c('ffffff', 'ffbbbb', '0000ff')) smap_vis <- list(min = 0.0, max = 28.0, palette =cpt("pj_4_earth" )) prcp_vis <- list(min = 0, max = 380, palette = cpt("ncl_precip3_16lev")) elev_vis <- list(min = 0, max = 1500, palette = cpt("gmt_GMT_elevation")) # initialize a map and add layers Map$centerObject(basin) basin_layer <- Map$addLayer(basin,{},"basin") riverNet_layer <- Map$addLayer(riverNet,list(width = 1.0, color = "blue"),"River network") jul_prcp_layer <- Map$addLayer(mth_prcp_jul,prcp_vis,"July Precipitation in mm",legend = T) smapSl_layer <- Map$addLayer(smap_sm,smap_vis,"SMAP soil moisture in mm") srfce_water_layer <- Map$addLayer(jrc_srfce_wtr,swater_vis,"Surface water occurrence %", legend = T) clc_layer <- Map$addLayer(clc2019,{},"Copernicus Land Cover 100m") elev_layer <- Map$addLayer(alos_elev,elev_vis,"ALOS DSM 30m", legend =T) ``` ] -- --- ## Add layers to map ```r # show layers basin_layer+jul_prcp_layer # smapSl_layer|srfce_water_layer ``` <div id="htmlwidget-39dbd53e46d7aa9881d7" style="width:100%;height:432px;" class="leaflet html-widget"></div> <script type="application/json" 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the gridded rainfall data at the basin level ```r # basin to ee Geometry ee_basin <- basin$geometry() # SM2RAIN downscaled monthly precip 1km prcp_raster <- ee_as_raster(mth_prcp_jul, region = ee_basin, quiet = T) # raster to df ras_todf <- as.data.frame(prcp_raster, xy = T) %>% drop_na() %>% filter(jul>0) # basin to sf basin_sf <- ee_as_sf(basin) ``` ] .tiny.pull-left[ ```r # Integrate GEE data with other ggplot layers ggplot(basin_sf) + geom_raster( data = ras_todf, aes(x = x, y = y, fill = jul) ) + scale_fill_gradientn(colours = cpt("ncl_precip3_16lev" )) + geom_sf(fill = NA, color = "red") + labs(fill = "July precip [mm]")+ theme_bw() ``` ] .pull-right[ <img src="GEE_AK_files/figure-html/plot-label-out-1.png" width="100%" /> ] -- --- layout: false class: inverse, center, middle # Quick demo2: identify surface water areas using machine learning within GEE -- --- ## Identify water and non-water - Here, we are going to train a simple machine learning algorithm to distinguish between water and non water areas for a given month at 10 m resolution within our basin using GEE data. - To train the model, we use Sentinel 2 surface reflectance images, then derive the water and vegetation indices (NDWI and NDVI) in addition to other Sentinel 2 bands. .tiny[ ```r # get S2 image collection getS2Image <- function(date, cldPerc) { date <- ee$Date(date) S2_collection <- ( ee$ImageCollection("COPERNICUS/S2_SR") # S2_SR collection $filterBounds(basin) # filter by basin boundary $filterDate(date, date$advance(1, "month")) # filter by month range $filter(ee$Filter$lt("CLOUDY_PIXEL_PERCENTAGE", cldPerc)) # filter by cloudy pixel % ) # print metadata print(ee_print(S2_collection), max = 1) # reduce using median S2_median <- S2_collection$ reduce(ee$Reducer$median()) # clip to the basin geometry S2_median <- S2_median$divide(10000)$clip(basin) return(S2_median) } ``` ] -- --- ## Get Sentinel 2SR composite image for a given month ```r # select a date dt <- '2021-01-01' s2_img <- getS2Image(date = dt, cldPerc=1) #only cloudless images ``` ``` ## ------------------------------------------------ Earth Engine ImageCollection -- ## ImageCollection Metadata: ## - Class : ee$ImageCollection ## - Number of Images : 189 ## - Number of Properties : 22 ## - Number of Pixels* : 6795017397 ## - Approximate size* : 2.79 TB ## Image Metadata (img_index = 0): ## - ID : COPERNICUS/S2_SR/20210102T104441_20210102T104435_T29PRR ## - system:time_start : 2021-01-02 10:57:19 ## - system:time_end : 2021-01-02 10:57:19 ## - Number of Bands : 23 ## - Bands names : B1 B2 B3 B4 B5 B6 B7 B8 B8A B9 B11 B12 AOT WVP SCL TCI_R TCI_G TCI_B MSK_CLDPRB MSK_SNWPRB QA10 QA20 QA60 ## - Number of Properties : 81 ## - Number of Pixels* : 35952473 ## - Approximate size* : 15.09 GB ## Band Metadata (img_band = 'B1'): ## - EPSG (SRID) : WGS 84 / UTM zone 29N (EPSG:32629) ## - proj4string : +proj=utm +zone=29 +datum=WGS84 +units=m +no_defs ## - Geotransform : 60 0 799980 0 -60 1600020 ## - Nominal scale (meters) : 60 ## - Dimensions : 1531 1021 ## - Number of Pixels : 1563151 ## - Data type : INT ## - Approximate size : 671.97 MB ## -------------------------------------------------------------------------------- ## NOTE: (*) Properties calculated considering a constant geotransform and data type.$name ## [1] "ImageCollection" ## ## [ reached getOption("max.print") -- omitted 26 entries ] ``` ] -- --- ## Identify water and non-water Calculate NDVI and NDWI and add that to the bands for training .tiny[ ```r # Normalized difference vegetation index getNDVI <- function (img) { img$normalizedDifference(c('B8_median', 'B4_median')) } # Normalized difference water index getNDWI <- function (img) { img$normalizedDifference(c('B3_median', 'B8_median')) } ``` ```r # get NDVI and NDWI S2_NDVI = getNDVI(s2_img)$rename("NDVI") S2_NDWI = getNDVI(s2_img)$rename("NDWI") # get info ee_print(S2_NDVI) ``` ``` ## ---------------------------------------------------------- Earth Engine Image -- ## Image Metadata: ## - Class : ee$Image ## - ID : no_id ## - Number of Bands : 1 ## - Bands names : NDVI ## - Number of Properties : 0 ## - Number of Pixels* : 64800 ## - Approximate size* : 202.50 KB ## Band Metadata (img_band = NDVI): ## - EPSG (SRID) : WGS 84 (EPSG:4326) ## - proj4string : +proj=longlat +datum=WGS84 +no_defs ## - Geotransform : 1 0 0 0 1 0 ## - Nominal scale (meters) : 111319.5 ## - Dimensions : 360 180 ## - Number of Pixels : 64800 ## - Data type : FLOAT ## - Approximate size : 202.50 KB ## -------------------------------------------------------------------------------- ## NOTE: (*) Properties calculated considering a constant geotransform and data type. ``` ] -- --- ## Identify water and non-water .tiny[ Stack all the bands together ```r band_stack = ( ee$Image(s2_img) $addBands(S2_NDVI) $addBands(S2_NDWI) $float() ) # select only bands with 10 resolution bands = c( 'NDWI', 'NDVI','B2_median', 'B3_median', 'B4_median', 'B8_median') band_stack <- band_stack$select(bands) # get info ee_print(band_stack) ``` ``` ## ---------------------------------------------------------- Earth Engine Image -- ## Image Metadata: ## - Class : ee$Image ## - ID : no_id ## - Number of Bands : 6 ## - Bands names : NDWI NDVI B2_median B3_median B4_median B8_median ## - Number of Properties : 0 ## - Number of Pixels* : 388800 ## - Approximate size* : 1.19 MB ## Band Metadata (img_band = NDWI): ## - EPSG (SRID) : WGS 84 (EPSG:4326) ## - proj4string : +proj=longlat +datum=WGS84 +no_defs ## - Geotransform : 1 0 0 0 1 0 ## - Nominal scale (meters) : 111319.5 ## - Dimensions : 360 180 ## - Number of Pixels : 64800 ## - Data type : FLOAT ## - Approximate size : 202.50 KB ## -------------------------------------------------------------------------------- ## NOTE: (*) Properties calculated considering a constant geotransform and data type. ``` ] -- --- ## Visualize the S2 image and the NDWI layers ```r # Display the classification result and the input image. s2Image_vis = list(bands = c("B4_median", "B3_median", "B2_median"), min = 0, max = 0.3) class_vis = list(min = 0, max = 1, palette = cpt( "neota_base_blue")) ``` <div id="htmlwidget-3fb5698a5f3ff2d22e48" style="width:100%;height:360px;" class="leaflet html-widget"></div> <script type="application/json" 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4 polygons to train the Random Forest classifier. Please note: there is no model evaluation in this simplified example. ```r # Get the values for all pixels in each polygon in the training. training <- band_stack$sampleRegions( # Get the sample from the polygons FeatureCollection collection = tr_polygons, # Keep this list of properties from the polygons. properties = list("class"), # Set the scale to get S2 image pixels in the polygons scale = 10 ) # Create a Random Forest classifier rf_classifier <- ee$Classifier$smileRandomForest(numberOfTrees = 50) # Train the classifier. trained <- rf_classifier$train(training, "class", bands) # Classify the image. classified_img <- band_stack$classify(trained) # Get class #1 of surface water Water_class <- classified_img$eq(0) water <- classified_img$updateMask(Water_class) ``` -- --- ## Visualize the classified surface water ```r # Display the classification result and the input image. class_vis = list(min = 0, max = 1, palette = c("blue")) Map$centerObject(basin) Map$addLayer(s2_img, s2Image_vis," S2_SR image" ) + Map$addLayer(water,class_vis,"water") ``` <div id="htmlwidget-5e3ec69e85af2cb4888e" style="width:100%;height:360px;" class="leaflet html-widget"></div> <script type="application/json" data-for="htmlwidget-5e3ec69e85af2cb4888e">{"x":{"options":{"minZoom":1,"maxZoom":52,"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}},"preferCanvas":false,"bounceAtZoomLimits":false,"maxBounds":[[[-90,-370]],[[90,370]]]},"calls":[{"method":"addProviderTiles","args":["CartoDB.Positron","CartoDB.Positron","CartoDB.Positron",{"errorTileUrl":"","noWrap":false,"detectRetina":false,"pane":"tilePane"}]},{"method":"addProviderTiles","args":["OpenStreetMap","OpenStreetMap","OpenStreetMap",{"errorTileUrl":"","noWrap":false,"detectRetina":false,"pane":"tilePane"}]},{"method":"addProviderTiles","args":["CartoDB.DarkMatter","CartoDB.DarkMatter","CartoDB.DarkMatter",{"errorTileUrl":"","noWrap":false,"detectRetina":false,"pane":"tilePane"}]},{"method":"addProviderTiles","args":["Esri.WorldImagery","Esri.WorldImagery","Esri.WorldImagery",{"errorTileUrl":"","noWrap":false,"detectRetina":false,"pane":"tilePane"}]},{"method":"addProviderTiles","args":["OpenTopoMap","OpenTopoMap","OpenTopoMap",{"errorTileUrl":"","noWrap":false,"detectRetina":false,"pane":"tilePane"}]},{"method":"addLayersControl","args":[["CartoDB.Positron","OpenStreetMap","CartoDB.DarkMatter","Esri.WorldImagery","OpenTopoMap"],[],{"collapsed":true,"autoZIndex":true,"position":"topleft"}]},{"method":"addScaleBar","args":[{"maxWidth":100,"metric":true,"imperial":true,"updateWhenIdle":true,"position":"bottomleft"}]},{"method":"addTiles","args":["https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/3fc8564108120ccc841ff8ab104eff1f-b96f4257c8fec6ab80faab0d715390a6/tiles/{z}/{x}/{y}"," S2_SR image"," S2_SR image",{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false}]},{"method":"addLayersControl","args":[["CartoDB.Positron","OpenStreetMap","CartoDB.DarkMatter","Esri.WorldImagery","OpenTopoMap"]," S2_SR image",{"collapsed":true,"autoZIndex":true,"position":"topleft"}]},{"method":"hideGroup","args":[null]},{"method":"addTiles","args":["https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/maps/d8eadbd47f17517ebe5b0b9e079049c7-cafb57054db92b46002f578961fc5da8/tiles/{z}/{x}/{y}","water","water",{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false}]},{"method":"addLayersControl","args":[["CartoDB.Positron","OpenStreetMap","CartoDB.DarkMatter","Esri.WorldImagery","OpenTopoMap"],[" S2_SR image","water"],{"collapsed":true,"autoZIndex":true,"position":"topleft"}]},{"method":"hideGroup","args":[null]}],"setView":[[11.0551876015876,-8.75483377540263],6,[]]},"evals":[],"jsHooks":{"render":[{"code":"function(el, x, data) {\n return (\n function(el, x, data) {\n // get the leaflet map\n var map = this; //HTMLWidgets.find('#' + el.id);\n // we need a new div element because we have to handle\n // the mouseover output separately\n // debugger;\n function addElement () {\n // generate new div Element\n var newDiv = $(document.createElement('div'));\n // append at end of leaflet htmlwidget container\n $(el).append(newDiv);\n //provide ID and style\n newDiv.addClass('lnlt');\n newDiv.css({\n 'position': 'relative',\n 'bottomleft': '0px',\n 'background-color': 'rgba(255, 255, 255, 0.7)',\n 'box-shadow': '0 0 2px #bbb',\n 'background-clip': 'padding-box',\n 'margin': '0',\n 'padding-left': '5px',\n 'color': '#333',\n 'font': '9px/1.5 \"Helvetica Neue\", Arial, Helvetica, sans-serif',\n 'z-index': '700',\n });\n return newDiv;\n }\n\n\n // check for already existing lnlt class to not duplicate\n var lnlt = $(el).find('.lnlt');\n\n if(!lnlt.length) {\n lnlt = addElement();\n\n // grab the special div we generated in the beginning\n // and put the mousmove output there\n\n map.on('mousemove', function (e) {\n if (e.originalEvent.ctrlKey) {\n if (document.querySelector('.lnlt') === null) lnlt = addElement();\n lnlt.text(\n ' lon: ' + (e.latlng.lng).toFixed(5) +\n ' | lat: ' + (e.latlng.lat).toFixed(5) +\n ' | zoom: ' + map.getZoom() +\n ' | x: ' + L.CRS.EPSG3857.project(e.latlng).x.toFixed(0) +\n ' | y: ' + L.CRS.EPSG3857.project(e.latlng).y.toFixed(0) +\n ' | epsg: 3857 ' +\n ' | proj4: +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +no_defs ');\n } else {\n if (document.querySelector('.lnlt') === null) lnlt = addElement();\n lnlt.text(\n ' lon: ' + (e.latlng.lng).toFixed(5) +\n ' | lat: ' + (e.latlng.lat).toFixed(5) +\n ' | zoom: ' + map.getZoom() + ' ');\n }\n });\n\n // remove the lnlt div when mouse leaves map\n map.on('mouseout', function (e) {\n var strip = document.querySelector('.lnlt');\n if( strip !==null) strip.remove();\n });\n\n };\n\n //$(el).keypress(67, function(e) {\n map.on('preclick', function(e) {\n if (e.originalEvent.ctrlKey) {\n if (document.querySelector('.lnlt') === null) lnlt = addElement();\n lnlt.text(\n ' lon: ' + (e.latlng.lng).toFixed(5) +\n ' | lat: ' + (e.latlng.lat).toFixed(5) +\n ' | zoom: ' + map.getZoom() + ' ');\n var txt = document.querySelector('.lnlt').textContent;\n console.log(txt);\n //txt.innerText.focus();\n //txt.select();\n setClipboardText('\"' + txt + '\"');\n }\n });\n\n }\n ).call(this.getMap(), el, x, data);\n}","data":null},{"code":"function(el, x, data) {\n return (function(el,x,data){\n var map = this;\n\n map.on('keypress', function(e) {\n console.log(e.originalEvent.code);\n var key = e.originalEvent.code;\n if (key === 'KeyE') {\n var bb = this.getBounds();\n var txt = JSON.stringify(bb);\n console.log(txt);\n\n setClipboardText('\\'' + txt + '\\'');\n }\n })\n }).call(this.getMap(), el, x, data);\n}","data":null}]}}</script> -- --- ### Conclusions This tutorial: * Introduces Google Earth Engine and its components * Introduces the rgee package ([Aybar et al. 2021](https://cloud.r-project.org/web/packages/rgee/index.html)) and how to interact with GEE from within R * Demonstrates how to retrieve hydrological data from GEE in R * Demonstrates how to visualize GEE data in ggplot * Demonstrates how to train a simple Machine Learning model in GEE Useful links: * [rgee vignette](https://cran.r-project.org/web/packages/rgee/vignettes/rgee01.html#1_What_is_Google_Earth_Engine_) * [rgee examples](https://csaybar.github.io/rgee-examples/) * [GEE User Guide](https://developers.google.com/earth-engine/guides/getstarted) * [GEE Data Catalogue](https://developers.google.com/earth-engine/datasets) * [Google Earth Engine Applications (Book)](https://www.mdpi.com/books/pdfview/book/1262) ****************************************** Contact email: a.khouakhi@cranfield.ac.uk