Image
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
library(rgeeExtra)
library(rgee)
ee_Initialize() # Initialize the Google Earth Engine API connection
extra_Initialize() # Load the extended functionalities of rgeeExtraFeatures for ee$Image
ee$Image objects encapsulate individual satellite images
and provide a suite of methods for image manipulation and analysis in
Earth Engine. The rgeeExtra package extends these
capabilities within R, offering tools for both simple and advanced image
processing tasks.
1. Earth Engine arithmetic, logic and compare generic functions
Arithmetic and logical operations are key for advanced raster data analysis, enabling intricate band math and conditional algorithms. These tools facilitate the merging of band information, implementation of corrections, and execution of classification and change detection algorithms.
Arithmetic Operations
Utilize arithmetic operators like +, -,
*, /, and ^ on
ee$Image objects for complex image-based calculations and
analyses in Earth Engine.
# Load and process MODIS land surface temperature images for two dates.
temp1 <- ee$ImageCollection("MODIS/006/MOD11A1")$
filterDate("2020-01-01", "2020-01-08")$
mean()$
select("LST_Day_1km") * 0.02 # Scale factor applied to convert to Kelvin.
temp2 <- ee$ImageCollection("MODIS/006/MOD11A1")$
filterDate("2020-07-01", "2020-07-08")$
mean()$
select("LST_Day_1km") * 0.02 # Scale factor applied to convert to Kelvin.
# Calculate temperature difference and ratio
temp_diff <- temp1 - temp2
temp_ratio <- temp1 / temp2
# Visualize temperature difference and ratio on map.
m1 <- Map$addLayer(
eeObject = temp_diff,
visParams = list(min = -65, max = 63, palette = c("#8EFFF6", "#0400FF")),
name = "Difference (K)"
)
m2 <- Map$addLayer(
eeObject = temp_ratio,
visParams = list(min = 0.8, max = 1.2, palette = c("#E0DB29", "#FE130C")),
name = "Ratio"
)
m1 | m2
Logical Operations
Apply logical operators such as !, &,
and | to ee$Image objects, enabling advanced
logical manipulation and condition-based filtering in Earth Engine.
# Load snow index images from MODIS for two different dates.
snow_index1 <- ee$ImageCollection("MODIS/006/MOD10A1")$
filterDate("2020-01-01", "2020-01-08")$
mean()$
select("NDSI_Snow_Cover")
snow_index2 <- ee$ImageCollection("MODIS/006/MOD10A1")$
filterDate("2020-07-01", "2020-07-08")$
mean()$
select("NDSI_Snow_Cover")
# Identify snow coverage and changes between dates using logic operators.
snow_present <- snow_index1 > 40 # NDSI > 40 indicates snow
snow_change <- !(snow_present) & (snow_index2 > 40) # Detect change in snow coverage
# Set map center and add layers to visualize snow presence and change.
Map$setCenter(lon = -67.05, lat = -18.81, zoom = 9)
m1 <- Map$addLayer(
eeObject = snow_present,
visParams = list(palette = c("#FFFFFF", "#00E4FF")),
name = "Snow"
)
m2 <- Map$addLayer(
eeObject = snow_change,
visParams = list(palette = c("#FFFFFF", "#FF0000")),
name = "Snow Change"
)
m1 | m2
Comparison Operations
Employ comparison operators including ==,
!=, >, <,
<=, >= for ee$Image objects
to facilitate detailed comparative analysis and decision-making in Earth
Engine.
# Load water index data from JRC's Monthly History for two periods.
water_index_pre <- ee$ImageCollection("JRC/GSW1_4/MonthlyHistory")$
filterDate("2020-01-01", "2020-01-08")$
mean()$
select("water")
water_index_post <- ee$ImageCollection("JRC/GSW1_4/MonthlyHistory")$
filterDate("2020-07-01", "2020-07-08")$
mean()$
select("water")
# Identify stable water areas and areas with water increase.
water_post <- water_index_post == 2 # Areas with water post-period
water_pre <- water_index_pre == 2 # Areas with water pre-period
stable_water <- water_post & water_pre # Areas with stable water presence
water_increase <- water_index_post < water_index_pre # Areas with water increase
# Center map and visualize stable water areas and water increase.
Map$setCenter(lon = -76.09, lat = -11.02, zoom = 10)
m1 <- Map$addLayer(
eeObject = stable_water,
visParams = list(palette = c("#FFFFFF", "#00A2FF")),
name = "Water stable"
)
m2 <- Map$addLayer(
eeObject = water_increase,
visParams = list(palette = c("#FFFFFF", "#000CFF")),
name = "Water increase"
)
m1 | m2
2. Mathematical functions
In Earth Engine, ee$Image objects offer a vast array of
mathematical functions, such as abs, sqrt,
log, and trigonometric operations like sin and
cos. These functions enable detailed pixel-level
manipulation, enhancing image analysis capabilities for a variety of
applications, from environmental monitoring to geographical research.
Such manipulations can reveal intricate details and patterns in
satellite imagery, crucial for advanced remote sensing analysis.
# Define the Area of Interest (AOI) - Central Valley, California.
aoi <- ee$Geometry$Rectangle(c(-121.0, 36.8, -119.0, 37.0))
# Load NDVI image from Landsat and clip it to the AOI.
ndvi_img <- ee$ImageCollection("LANDSAT/LC08/C01/T1_8DAY_NDVI")$
filterDate("2020-06-01", "2020-06-08")$
mean()$
select("NDVI")$
clip(aoi)
# Apply mathematical transformations to the clipped NDVI image.
abs_ndvi <- abs(ndvi_img) # Absolute value of NDVI.
sqrt_ndvi <- sqrt(ndvi_img) # Square root of NDVI.
ceiling_ndvi <- ceiling(ndvi_img) # Ceiling of NDVI.
log_ndvi <- log(ndvi_img) # Natural logarithm of NDVI.
exp_ndvi <- exp(ndvi_img) # Exponential function of NDVI.
log_transformed_ndvi <- log1p(ndvi_img) # Log(1 + NDVI).
# Visualize the transformations applied to NDVI.
visParams <- list(min = -1, max = 1, palette = c("brown", "white", "green"))
Map$centerObject(aoi, zoom = 11)
Map$addLayer(abs_ndvi, visParams, "Absolute NDVI") +
Map$addLayer(sqrt_ndvi, visParams, "Square Root NDVI") +
Map$addLayer(ceiling_ndvi, visParams, "Ceiling NDVI") +
Map$addLayer(log_ndvi, visParams, "Log NDVI") +
Map$addLayer(exp_ndvi, visParams, "Exp NDVI") +
Map$addLayer(log_transformed_ndvi, visParams, "Log Transformed NDVI")
3. Spectral Indices
In Earth Engine, the ee$Image$Extra_spectralIndex
function is essential for computing a wide range of spectral indices on
ee$Image objects. This versatile tool allows for the
calculation of indices like NDVI, EVI, and SAVI, each customizable with
specific parameters, facilitating detailed environmental analysis and
enhancing the understanding of vegetation health, water bodies, urban
areas, and other key earth surface features.
# Load first COPERNICUS/S2_SR image, compute NDVI and SAVI
s2_indices <- ee$ImageCollection("COPERNICUS/S2_SR")[[1]] %>%
ee$Image$Extra_preprocess() %>%
ee$Image$Extra_spectralIndex(c("NDVI", "SAVI"))
# Set visualization parameters
visParams <- list(
min = 0,
max = 0.2,
palette = c("brown", "yellow", "green")
)
# Display NDVI on map with custom visuals
Map$centerObject(s2_indices, zoom = 10)
Map$addLayer(
eeObject = s2_indices[["NDVI"]],
visParams = visParams,
name = "NDVI"
)
4. Tasseled Cap Transformation
The ee$Image$Extra_tasseledCap function in Earth Engine
provides a powerful tool for transforming ee$Image objects,
calculating tasseled cap components such as brightness, greenness, and
wetness. This transformation, applied using specific coefficients for
platforms like Sentinel-2 and Landsat, enriches the analysis of land
cover and environmental characteristics by enhancing spectral band
information.
# Apply Tasseled Cap transformation to a Sentinel-2 image
tc <- ee$Image("COPERNICUS/S2/20190310T105851_20190310T110327_T30TVK") %>%
ee$Image$Extra_tasseledCap()
# Display the names of the transformed bands
names(tc)
# Output: "B1", "B2", "B3", ..., "B12", "TCB", "TCG", "TCW"
# Map display with Tasseled Cap Brightness, Greenness, and Wetness layers
Map$centerObject(tc, zoom = 10)
Map$addLayer(tc$select("TCG"), list(min = -4700, max = 3900), "TC Greenness") +
Map$addLayer(tc$select("TCW"), list(min = -23700, max = 350), "TC Wetness") +
Map$addLayer(tc$select("TCB"), list(min = 950, max = 11000), "TC Brightness")
5. Panchromatic Sharpening
The ee$Image$Extra_panSharpen function in Earth Engine
is a sophisticated tool for enhancing ee$Image objects
through panchromatic sharpening. This technique improves image
resolution, offering algorithms like SFIM and HPFA, and includes the
option to assess quality, such as spectral distortion, enhancing the
clarity and detail of satellite imagery.
# Load Landsat 8 TOA reflectance image from GEE
img <- ee$Image("LANDSAT/LC09/C02/T1_TOA/LC09_047027_20230815")
# Apply HPFA panchromatic sharpening to enhance sharpness
img_sharp <- ee$Image$Extra_panSharpen(
img,
method="HPFA",
qa=c("MSE", "RMSE"),
maxPixels=1e13
)
# Define visualization parameters for displaying the images
visParams <- list(
bands = c("B4","B3","B2"),
min = 0,
max = 0.15,
gamma = 1.4
)
# Display original and HPFA-sharpened images on the map
Map$setCenter(lon = -123.11, lat = 47.307, zoom = 14)
Map$addLayer(img, visParams, "Original Image") |
Map$addLayer(img_sharp, visParams, "Sharpened Image")
6. Histogram Matching
The ee$Image$Extra_matchHistogram function in Earth
Engine enables precise histogram matching of an ee$Image to
a target image. This function adjusts the distribution of pixel values
in specified bands to mirror those in the target, enhancing
comparability between images for consistent analysis, especially useful
in time-series or multi-sensor studies.
# Load Landsat 8 and 7 images for histogram matching.
source <- ee$Image("LANDSAT/LC08/C02/T1_TOA/LC08_047027_20160819")
target <- ee$Image("LANDSAT/LE07/C02/T1_TOA/LE07_046027_20150701")
# Map Landsat 8 bands to corresponding Landsat 7 bands.
bands <- list("B4"="B3", "B3"="B2", "B2"="B1")
# Apply histogram matching from source to target using the band mapping.
matched <- ee$Image$Extra_matchHistogram(source, target, bands)
# Visualization settings for the images.
visParams <- list(
bands = c("B4","B3","B2"),
min = 0.1,
max = 0.5,
gamma = 1.5
)
# Display matched and target images on the map for comparison.
Map$setCenter(lon = -121.984, lat = 47.847, zoom = 14)
m1 <- Map$addLayer(matched, visParams = visParams, "matched")
m2 <- Map$addLayer(target, visParams = visParams, "target")
m1 | m2
7. Cloud Masking
Cloud masking with ee$Image$Extra_maskClouds in Earth
Engine is a critical preprocessing step, essential for reducing cloud
and shadow interference in remote sensing imagery. This function tailors
cloud masking through adjustable parameters like cloud probability,
buffer distance, and the CDI threshold, crucial for distinguishing
clouds from bright surfaces and even masking water bodies under specific
conditions.
# Cloud mask a Sentinel-2 image: 75% cloud prob, 300m buffer, -0.5 CDI
img <- ee$Image("COPERNICUS/S2_SR/20170328T083601_20170328T084228_T35SQA")
img_mask <- ee$Image$Extra_maskClouds(img, prob = 75, buffer = 300, cdi = -0.5)
# Get band names after masking
names(img_mask)
# Output: "B1", "B2", "B3", ..., "CLOUD_MASK", "CLOUD_MASK_CDI", "SHADOW_MASK", "CLOUD_SHADOW_MASK"
# Apply cloud and shadow masking with specified thresholds.
visParams <- list(
bands = c("B4","B3","B2"),
min = 200,
max = 4600,
gamma = 1.5
)
# Display both original and masked images on the map for comparison.
Map$setCenter(lon = 30.061, lat = 36.9, zoom = 11)
m1 <- Map$addLayer(img, visParams = visParams, name = "Image")
m2 <- Map$addLayer(img_mask, visParams = visParams, name = "Mask image")
m1 | m2
8. Automated Preprocessing
The ee$Image$Extra_preprocess function streamlines
preprocessing for ee$Image objects, encompassing essential
tasks like cloud masking and decompression. It adeptly removes cloud and
shadow pixels and converts integer pixel values to floating-point,
enhancing the clarity and usability of Earth Engine imagery for diverse
analyses.
# Load a Sentinel-2 image and apply automated preprocessing.
img <- ee$Image("COPERNICUS/S2_SR/20170328T083601_20170328T084228_T35SQA") %>%
ee$Image$Extra_preprocess()
# Visualization parameters: RGB bands, adjusted min, max, and gamma values.
visParams <- list(
bands = c("B4","B3","B2"),
min = 0.1,
max = 0.5,
gamma = 1.5
)
# Display the preprocessed image on the map.
Map$setCenter(lon = 30.061, lat = 36.9, zoom = 11)
Map$addLayer(img, visParams = visParams, "Preprocessed image")
9. Summary Methods
Summary methods extract key statistical data from images, which is critical for monitoring environmental changes and analyzing spatial patterns.
# Load a Sentinel-2 surface reflectance image from Earth Engine
img_demo <- ee$Image("COPERNICUS/S2_SR/20190310T105851_20190310T110327_T30TVK")
mean_img <- mean(img_demo) # Compute mean of image bands
min_img <- min(img_demo) # Compute minimum of image bands
max_img <- max(img_demo) # Compute maximum of image bands10. Minimum and maximum values
The ee$Image$Extra_maxValue and
ee$Image$Extra_minValue functions in Earth Engine provide a
method to determine the maximum and minimum values within an
ee$Image. These functions offer two modes: “Rectangle” for
using the image’s footprint and “Points” for sampling over it. They are
particularly useful for approximating extreme values in large
datasets.
# Load the first image's "B1" band from LANDSAT_LC08_C02_T1_TOA dataset
image <- ee$ImageCollection$Dataset$LANDSAT_LC08_C02_T1_TOA[[1]][["B1"]]
# Compute minimum value of "B1" band
min_value <- ee$Image$Extra_minValue(image)
min_value
# Result: [1] 0.1450491
# Compute maximum value of "B1" band
max_value <- ee$Image$Extra_maxValue(image)
max_value
# Result: [1] 1.0836111. Retrieve offset parameter
Earth Engine utilizes a lossless compression technique where
IMG_Float_Values = scale * IMG_Integer_Values + offset. The
ee$Image$Extra_getOffsetParams() function retrieves the
offset for each band of an ee$Image, critical for reverting
scaled integers to their original float values, ensuring data precision
and sensor-specific calibration.
# Retrieve offset parameters from the first image in NASA IMERG V06 collection
offset_params <- ee$ImageCollection("NASA/GPM_L3/IMERG_V06")[[1]] %>%
ee$Image$Extra_getOffsetParams()
offset_params
# Result:
# $HQobservationTime [1] 0
# $HQprecipSource [1] 0
# $HQprecipitation [1] 0
# $IRkalmanFilterWeight [1] 0
# $IRprecipitation [1] 0
# $precipitationCal [1] 0
# $precipitationUncal [1] 0
# $probabilityLiquidPrecipitation [1] 0
# $randomError [1] 012. Retrieve scale parameter
Earth Engine uses a lossless compression method:
IMG_Float_Values = scale * IMG_Integer_Values + offset. The
ee$Image$Extra_getScaleParams() function extracts the scale
for each band of an ee$Image, pivotal for translating
integer data to accurate float values, maintaining data integrity and
sensor-specific fidelity.
# Retrieve scale parameters from the first image in NASA IMERG V06 collection
scale_params <- ee$ImageCollection("NASA/GPM_L3/IMERG_V06")[[1]] %>%
ee$Image$Extra_getScaleParams()
scale_params
# Result:
# $HQobservationTime [1] 1
# $HQprecipSource [1] 1
# $HQprecipitation [1] 1
# $IRkalmanFilterWeight [1] 1
# $IRprecipitation [1] 1
# $precipitationCal [1] 1
# $precipitationUncal [1] 1
# $probabilityLiquidPrecipitation [1] 1
# $randomError [1] 113. Automatic decompression
Earth Engine applies a straightforward lossless compression method:
Floating-point image values (IMG_Float_Values) are calculated from
integer image values (IMG_Integer_Values) using the formula
IMG_Float_Values = scale * IMG_Integer_Values + offset. The
ee$Image$Extra_scaleAndOffset method is used to convert
integer pixel values back to floating-point numbers.
14. Retrieve STAC metadata
STAC (SpatioTemporal Asset Catalog) metadata plays a crucial role in
Earth Engine by standardizing the discovery and utilization of
spatial-temporal assets. It simplifies access to data, making it more
efficient and user-friendly. The function
ee$Image$Extra_getSTAC() is designed to retrieve this
metadata for an ee$Image, enhancing data management and
interoperability.
# Retrieve STAC metadata for the first image in NASA's GPM L3 IMERG V06 collection
stac_metadata <- ee$ImageCollection("NASA/GPM_L3/IMERG_V06")[[1]] %>%
ee$Image$Extra_getSTAC()
# Description of the dataset
stac_metadata$description
# Spatial extent of the dataset
stac_metadata$extent$spatial$bbox
# Temporal interval of the dataset
stac_metadata$extent$temporal$interval
# Data Provider IDs
stac_metadata$`gee:provider_ids`
# Terms of use of the dataset
stac_metadata$`gee:terms_of_use`
# Type of the collection
stac_metadata$`gee:type`
# Dataset ID
stac_metadata$id
# Keywords associated with the dataset
stac_metadata$keywords
# License information
stac_metadata$license
# Links to additional resources and information
stac_metadata$links
# Providers of the dataset
stac_metadata$providers
# Citation information
stac_metadata$`sci:citation`
# Digital Object Identifier (DOI)
stac_metadata$`sci:doi`
# STAC version and extensions
stac_metadata$stac_version
stac_metadata$stac_extensions
# Summaries of band information and visualizations
stac_metadata$summaries$`eo:bands`
stac_metadata$summaries$`gee:visualizations`
# Citation information
stac_metadata$`sci:citation`
# Digital Object Identifier (DOI)
stac_metadata$`sci:doi`
# STAC version and extensions
stac_metadata$stac_version
stac_metadata$stac_extensions
# Summaries of band information and visualizations
stac_metadata$summaries$`eo:bands`
stac_metadata$summaries$`gee:visualizations`15. Length of an Image
The length function counts the number of bands in an
ee$Image object, offering a straightforward way to assess
the complexity of Earth Engine Images.
16. Subsetting Bands
Utilize the [[ operator in R to selectively access or
modify specific bands within an ee$Image. This feature is
essential for focusing on particular spectral bands, simplifying the
process of analyzing and processing multispectral satellite imagery.
17. Names of Images layers (bands)
The names function retrieves band names from an
ee$Image object, providing a simple method to identify and
reference specific layers within Earth Engine Images.
18. Rename of Images layers (bands)
Renaming bands in an ee$Image object using the
names function in Earth Engine enhances clarity and
organization, aligning each band with elements of a character
vector.
# Load the first three bands of a specific Sentinel-2 image
image <- ee$Image("COPERNICUS/S2_SR/20190310T105851_20190310T110327_T30TVK")[[1:3]]
# Display the original names of these bands
names(image) # Original band names: "B1", "B2", "B3"
# Rename the bands for clarity
names(image) <- c("B01", "B02", "B03")
# Display the updated band names
names(image) # Updated band names: "B01", "B02", "B03"19. Citation in publications
Citations play a key role in recognizing data sources, particularly for images used in research and publications. They ensure the traceability and credibility of the analysis.
# Retrieve citation for the first image in NASA's IMERG V06 collection
citation <- ee$ImageCollection("NASA/GPM_L3/IMERG_V06")[[1]] %>%
ee$Image$Extra_getCitation()
# Display the citation
citation
# Citation: "Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019),
# GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06,
# Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC),
# Accessed: [Data Access Date],
# [doi:10.5067/GPM/IMERG/3B-HH/06](https://doi.org/10.5067/GPM/IMERG/3B-HH/06)"20. Get the Digital Object Identifier (DOI)
Retrieving the DOI of datasets in Earth Engine is vital for data sharing and reproducibility in scientific research, providing a permanent link to the datasets.