Irrigated or rainfed?

Use autocorelation between EVI and Rainfall to figure it out.

Step 1:  Import EVI and Chirps data layers

// Precipitation
var chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD');

// import MODIS EVI
var EVI = ee.ImageCollection("MODIS/MYD13A1").select("EVI");

Step 2:  Import the area of interest.

// countries of interest
var country_names = ['Myanmar (Burma)','Thailand','Laos','Vietnam','Cambodia'];
var countries = ee.FeatureCollection('ft:1tdSwUL7MVpOauSgRzqVTOwdfy17KDbw-1d9omPw');
var mekongCountries = countries.filter(ee.Filter.inList('Country', country_names));
var roi = mekongCountries.geometry();

Step 3: Define number of lag days

// define number of lagdays
var lagdays = 30;

Step 4 Define period of interest.

// set start and end date
var startdate = ee.Date.fromYMD(2000,1,1);
var enddate = ee.Date.fromYMD(2005,12,31);
var year = 2010;

// filter based on date
var EVI = EVI.filterDate(startdate,enddate);
var chirps = chirps.filterDate(startdate,enddate);

Step 5: Created a lagged collection

// Autocovariance and autocorrelation ---------------------------------------------
// Function to get a lagged collection. Images that are within
// lagDays of image are stored in a List in the 'images' property.
var lag = function(leftCollection, rightCollection, lagDays) {
 var filter = ee.Filter.and(
 difference: 1000 * 60 * 60 * 24 * lagDays,
 leftField: timeField,
 rightField: timeField
 leftField: timeField,
 rightField: timeField

 return ee.Join.saveAll({
 matchesKey: 'images',
 measureKey: 'delta_t',
 ordering: timeField,
 ascending: false, // Sort reverse chronologically
 primary: leftCollection,
 secondary: rightCollection,
 condition: filter

Step 6: apply the lagged function

// Advanced cross-correlation----------------------------------------------------
// Join the precipitation images from the previous month
var lag30PrecipEVI = lag(EVI, chirps, lagdays);

Step 7: add the sum of the lagged image collection as a band.


// calculate the sum of lagged image collection and ass them as a band
var sum30PrecipEVI = ee.ImageCollection( {
var laggedImages = ee.ImageCollection.fromImages(image.get('images'));
return ee.Image(image).addBands(laggedImages.sum().rename('sum'));

var yearlyPrecipEVI = sum30PrecipEVI.filter(ee.Filter.calendarRange(year, year, 'year'));

Step 8:  compute covariates.


// Function to compute covariance over time. This will return
// a 2x2 array image. Pixels contains variance-covariance matrices.
var covariance = function(mergedCollection, band, lagBand) {
return[band, lagBand]).map(function(image) {
return image.toArray();
}).reduce(ee.Reducer.covariance(), 8);

var cov30PrecipEVI = covariance(yearlyPrecipEVI, 'EVI', 'sum');

Step 9: calculate the correlation.


// Compute correlation from a 2x2 covariance image.
var correlation = function(vcArrayImage) {
var covariance = ee.Image(vcArrayImage).arrayGet([0, 1]);
var sd0 = ee.Image(vcArrayImage).arrayGet([0, 0]).sqrt();
var sd1 = ee.Image(vcArrayImage).arrayGet([1, 1]).sqrt();
return covariance.divide(sd0).divide(sd1).rename('correlation');

var corr30PrecipEVI = correlation(cov30PrecipEVI).set('system:time_start',ee.Date.fromYMD(year,1,1));

Step 10: Add the map.


Assignment 1:

Investigate the irrigated and non irigated areas. Which one has the highest autocorrelation?

Assignment 2:

Do the same analysis for 2011.








Follow this url for the full code.

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