Just swap the regions with your own features or points and run!
To access an example script for extracting NDVI from MODIS MOD13Q1 imagery in Earth Engine, run:
// Sample NDVI for IRG - MODIS MOD13Q1
// Example from irg package https://github.com/robitalec/irg
// Alec L. Robitaille
// Functions ===================================================================
function rescaleBands (im) {
var viBands = im.select(['NDVI', 'EVI']).multiply(0.0001);
var surfBands = im.select('sur_refl_b0.*').multiply(0.0001);
var viewBands = im.select(['ViewZenith', 'SolarZenith', 'RelativeAzimuth']).multiply(0.01);
return(im.addBands(viBands, null, true)
.addBands(surfBands, null, true)
.addBands(viewBands, null, true));
}
// Function to grab year from image and add it as a band
function addYear(img) {
return(img.addBands(ee.Image(img.date().get('year')).rename('yr')));
}
// Function to sample an image in each region of supplied geometry
function sampleRegions (im) {
return(im.reduceRegions(features, ee.Reducer.mean(), 250)
.copyProperties(im));
}
// Feature ====================================================================
var features = ee.FeatureCollection(
[ee.Feature(ee.Geometry.Point([-109.96, 53.853]),
{"id": "0"}),
ee.Feature(ee.Geometry.Point([-109.94, 53.852]),
{"id": "1"}),
ee.Feature(ee.Geometry.Point([-109.95, 53.851]),
{"id": "2"}),
ee.Feature(ee.Geometry.Point([-109.93, 53.854]),
{"id": "3"}),
ee.Feature(ee.Geometry.Point([-109.92, 53.853]),
{"id": "4"}),
ee.Feature(ee.Geometry.Point([-109.94, 53.852]),
{"id": "5"}),
ee.Feature(ee.Geometry.Point([-109.93, 53.851]),
{"id": "6"})]);
// Images ======================================================================
var modis = ee.ImageCollection('MODIS/006/MOD13Q1');
// Filter ======================================================================
modis = modis.filterDate('2015-01-01', '2020-01-01')
.filterBounds(features);
// Process images ==============================================================
// Add dates
modis = modis.map(addYear)
.map(rescaleBands);
// Sample images ===============================================================
var sample = modis.map(sampleRegions)
.flatten()
.filter(ee.Filter.neq('NDVI', null));
// Export ======================================================================
Export.table.toDrive({
collection: sample,
description: 'sampled-ndvi-MODIS-MOD13Q1',
selectors: ['id', 'NDVI', 'SummaryQA', 'DayOfYear', 'yr']
});To access an example script for extracting NDVI from Landsat 8 imagery in Earth Engine, run:
// Sample NDVI for IRG - Landsat
// Example from irg package https://github.com/robitalec/irg
// Alec L. Robitaille
// Functions ===================================================================
// Function to add mask identifying cloud and saturation
// Mask band = if QA_PIXEL indicates unwanted pixels +
// if QA_RADSAT indicates saturated pixels
// Either = 0 (good), 1 (one of two masking conditions), 2 (both conditions)
// Adapted from: Examples/Cloud Masking/Landsat8 Surface Reflectance
function addMask(image) {
// Bit 0 - Fill
// Bit 1 - Dilated Cloud
// Bit 2 - Cirrus
// Bit 3 - Cloud
// Bit 4 - Cloud Shadow
var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).neq(0);
var saturationMask = image.select('QA_RADSAT').neq(0);
var mask = qaMask.add(saturationMask);
// Apply the scaling factors to the appropriate bands.
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);
// Replace the original bands with the scaled ones and apply the masks.
return image.addBands(opticalBands, null, true)
.addBands(thermalBands, null, true)
.addBands(mask.rename('mask'));
}
// Function to calculate NDVI and add it as a band
function calcNDVI(im) {
var ndvi = im.normalizedDifference(['SR_B5','SR_B4']).rename('ndvi');
return im.addBands(ndvi);
}
// Function to grab date from image and add it as a band
function addDates(im) {
var date = im.date();
return im.addBands(ee.Image([date.getRelative('day', 'year'),
date.get('year')])
.rename(['doy', 'year'])).float();
}
// Function to sample an image in each region of supplied geometry
function sampleRegions (im) {
return(im.reduceRegions(features, ee.Reducer.mean(), 30)
.copyProperties(im));
}
// Feature ====================================================================
var features = ee.FeatureCollection(
[ee.Feature(ee.Geometry.Point([-109.96, 53.853]),
{"id": "0"}),
ee.Feature(ee.Geometry.Point([-109.94, 53.852]),
{"id": "1"}),
ee.Feature(ee.Geometry.Point([-109.95, 53.851]),
{"id": "2"}),
ee.Feature(ee.Geometry.Point([-109.93, 53.854]),
{"id": "3"}),
ee.Feature(ee.Geometry.Point([-109.92, 53.853]),
{"id": "4"}),
ee.Feature(ee.Geometry.Point([-109.94, 53.852]),
{"id": "5"}),
ee.Feature(ee.Geometry.Point([-109.93, 53.851]),
{"id": "6"})]);
// Images ======================================================================
var l8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2');
// Filter ======================================================================
l8 = l8.filterDate('2015-01-01', '2020-01-01')
.filterBounds(features);
// Process images ==============================================================
// Mask clouds and calculate NDVI
l8 = l8.map(addMask)
.map(addDates)
.map(calcNDVI);
// Sample images ===============================================================
var sample = l8.map(sampleRegions)
.flatten();
// Export ======================================================================
Export.table.toDrive({
collection: sample,
description: 'sampled-ndvi-Landsat-LC08-T1-L2',
selectors: ['id', 'ndvi', 'mask', 'doy', 'year']
});