SPEI vs NDVI MODIS.js

////////////////////////////////////////////////////////////////////////////
// This script uses MODIS 1km NDVI product and it is spatially            //
// correlate with SPEI calculated from NOAH Global Land Assimulation      //
// System data. It will display and export the correlation map of SPEI vs //
// three months sum of NDVI anomalies. The generated corrmap will be      //
// converted to a shapefile based on the threshold (R>0.3 | R>0.5)        //
// (SPEIxMonth in Jan-Dec vs NDVI three month anomalies)                  //
//------------------------------------------------------------------------//
// For fast global study                                                  //
// Contact: Shunan Feng (冯树楠): fsn.1995@gmail.com                      //
////////////////////////////////////////////////////////////////////////////


//------------------------------------------------------------------------//
//                             Preparation                                //
//------------------------------------------------------------------------//
var worldmap = ee.FeatureCollection('ft:1tdSwUL7MVpOauSgRzqVTOwdfy17KDbw-1d9omPw');//world vector
var roi = worldmap.geometry();// country 
// study time range
var year_start = 2001; //  MODIS NDVI 2000-02-18T00:00:00 - Present
var year_end = 2018;
var month_start = 1;
var month_end = 12;

var date_start = ee.Date.fromYMD(year_start, 1, 1);
var date_end = ee.Date.fromYMD(year_end, 12, 31);
var years = ee.List.sequence(year_start, year_end);// time range of years
var months = ee.List.sequence(month_start, month_end);// time range of months
// change the month lag here, e.g. no lag is 0,-1 is one month lag,-2 is 2 month lag
var lagflag = 0; 

// The defalt setting will correlate 2 month time scale of SPEI(SPEI2m)
// with one month lag of three month sum of NDVI anomalies.

//------------------------------------------------------------------------//
//                               Datainput                                //
//------------------------------------------------------------------------//

// load MODIS NDVI 2000-02-18T00:00:00 - Present
var ndvi = ee.ImageCollection('MODIS/006/MOD13A2')
    .filterDate(date_start, date_end)
    .select('NDVI');

var spei1m = ee.ImageCollection("users/fsn1995/spei1m_noah");
var spei2m = ee.ImageCollection("users/fsn1995/spei2m_noah");
var spei3m = ee.ImageCollection("users/fsn1995/spei3m_noah");
var spei4m = ee.ImageCollection("users/fsn1995/spei4m_noah");
var spei5m = ee.ImageCollection("users/fsn1995/spei5m_noah");
var spei6m = ee.ImageCollection("users/fsn1995/spei6m_noah");
var spei7m = ee.ImageCollection("users/fsn1995/spei7m_noah");
var spei8m = ee.ImageCollection("users/fsn1995/spei8m_noah");
var spei9m = ee.ImageCollection("users/fsn1995/spei9m_noah");
var spei10m = ee.ImageCollection("users/fsn1995/spei10m_noah");
var spei11m = ee.ImageCollection("users/fsn1995/spei11m_noah");
var spei12m = ee.ImageCollection("users/fsn1995/spei12m_noah");

// select the time scale of spei here
var spei = spei11m.filterDate(date_start, date_end)
                  .map(function(image) {
                    var speiMask = image.lte(0);
                    return image.updateMask(speiMask);
                }); // mask out spei


// load land cover data
var lucc = ee.Image('USGS/NLCD/NLCD2011').select('landcover');
// monthly average NDVI
// sytstem time is set as 1st of each month
var NDVI_monthlyave = ee.ImageCollection.fromImages(
    years.map(function (y) {
        return months.map(function(m) {
            var vi = ndvi.select('NDVI')
                         .filter(ee.Filter.calendarRange(y, y, 'year'))
                         .filter(ee.Filter.calendarRange(m, m, 'month'))
                         .mean()
                         .rename('NDVIm');
            return vi.set('year', y)
                     .set('month', m)
                     .set('system:time_start', ee.Date.fromYMD(y, m, 1));
        });
    }).flatten()
);

// 20yr monthly average NDVI
var NDVI_30yrave = ee.ImageCollection.fromImages(
    months.map(function (m) {
        var vi = ndvi.select('NDVI')
                     .filter(ee.Filter.calendarRange(m, m, 'month'))
                     .mean()
                     .rename('NDVIy');
        return vi.set('month', m);
    }).flatten()
);
// print(NDVI_30yrave);

// NDVI anomaly = monthly average NDVI - 30yr monthly average NDVI 
// NDVI monthly anomaly
var monthfilter = ee.Filter.equals({
    leftField: 'month',
    rightField: 'month',
});
var monthlink = ee.Join.saveFirst({
    matchKey: 'match',
});

var NDVI_monthlink = ee.ImageCollection(monthlink.apply(NDVI_monthlyave,NDVI_30yrave,monthfilter))
    .map(function(image) {
        return image.addBands(image.get('match'));
    });
                             
var addNDVI_anomaly = function(image) {
    var anomaly = image.expression(
        'b1-b2',
        {
            b1: image.select('NDVIm'),
            b2: image.select('NDVIy'),
        }
    ).rename('NDVI_anomaly');
    return image.addBands(anomaly);
};
                            
var NDVI_anomaly = NDVI_monthlink.map(addNDVI_anomaly);

//------------------------------------------------------------------------//
//                                  Lag                                   //
//------------------------------------------------------------------------//
// lag is achieved by shifting the date of the data

var addLagm = function(image) {
    var lagm = ee.Date(image.get('system:time_start')).advance(lagflag,'month');
    return image.set({'lagm': lagm});
};

// below is to compute ndvi three month anomaly
var addLag0m = function(image) {
    var lagm = ee.Date(image.get('system:time_start')).advance(0,'month');
    return image.set({'lagm': lagm});
};

var addLag1m = function(image) {
    var lagm = ee.Date(image.get('system:time_start')).advance(-1,'month');
    return image.set({'lagm': lagm});
};

var addLag2m = function(image) {
    var lagm = ee.Date(image.get('system:time_start')).advance(-2,'month');
    return image.set({'lagm': lagm});
};

// compute three month sum ndvi anomaly

var NDVI0 = NDVI_anomaly.select('NDVI_anomaly').map(addLag0m);
var NDVI1 = NDVI_anomaly.select('NDVI_anomaly').map(addLag1m);
var NDVI2 = NDVI_anomaly.select('NDVI_anomaly').map(addLag2m);

var lagFilter = ee.Filter.equals({
    leftField: 'lagm',
    rightField: 'lagm',
});
var lagLink = ee.Join.saveFirst({
    matchKey: 'match',
});

var NDVI_threeMonthAnomaly = ee.ImageCollection(lagLink.apply(NDVI0.select('NDVI_anomaly'),
        NDVI1.select('NDVI_anomaly'),lagFilter))
        .map(function(image) {
            return image.addBands(image.get('match'));
        });


var NDVI_threeMonthAnomaly = ee.ImageCollection(lagLink.apply(NDVI_threeMonthAnomaly,
        NDVI2.select('NDVI_anomaly'),lagFilter))
        .map(function(image) {
            return image.addBands(image.get('match'));
        });

var NDVI_anomaly_sum = NDVI_threeMonthAnomaly.map(function(image) {
    return image.addBands(
        image.expression('a1 + b1 + c1', {
            a1: image.select('NDVI_anomaly'),
            b1: image.select('NDVI_anomaly_1'),
            c1: image.select('NDVI_anomaly_2'),
        }).rename('NDVI_anomalySum'));
});

var NDVI_anomSumMLag = NDVI_anomaly_sum.select('NDVI_anomalySum').map(addLagm);


//------------------------------------------------------------------//
// This part compares NDVI anomalies with spei computed from NOAH   //
// Global land assimulation system                                  //
//------------------------------------------------------------------//

var speiSet = spei.map(function(image) {
    return image.set('date', image.date());
  });
var timescaleFilter = ee.Filter.equals({
    leftField: 'lagm',
    rightField: 'date',
});  
// print(speiSet,'speiSet');
var NDVI3mLag_spei = ee.ImageCollection(lagLink.apply(NDVI_anomSumMLag.select('NDVI_anomalySum'),
        speiSet.select('b1'),timescaleFilter))
        .map(function(image) {
            return image.addBands(image.get('match'));
        });

var corrmap = NDVI3mLag_spei.reduce(ee.Reducer.pearsonsCorrelation()); 
//                        //.addBands(lucc.select('landcover').rename('lucc'));
// // var corrmap = NDVI_spei.reduce(ee.Reducer.spearmansCorrelation()).clip(roi);
//                     // .addBands(lucc.select('landcover').rename('lucc'));

var corrParams = {min: -1, max: 1, palette: ['red','white', 'green']};
Map.addLayer(corrmap.select('correlation'), corrParams, 'Correlation Map');

Export.image.toDrive({
  image: corrmap,
  folder: 'speiCorr',
  description: 'Correlation map of spei with ndvi anomalies',
  scale: 10000,
//   region: roi // If not specified, the region defaults to the viewport at the time of invocation
});


// Define arbitrary thresholds R>0.3 R>0.5
var zones = corrmap.select('correlation').gt(0.3).add(corrmap.select('correlation').gt(0.5));
zones = zones.updateMask(zones.neq(0));

var vectors = zones.addBands(corrmap.select('correlation')).reduceToVectors({
    geometry: roi,
    crs: corrmap.projection(),
    scale: 25000,
    // geometryType: 'polygon',
    eightConnected: false,
    labelProperty: 'zone',
    reducer: ee.Reducer.mean()
  });
  
// print(zones);
// print(vectors);
// R> 0.3 will be displayed in blue, R>0.5 will be displayed in red
Map.addLayer(zones, {min: 1, max: 2, palette: ['0000FF', 'FF0000']}, 'raster');

// var display = ee.Image(0).updateMask(0).paint(vectors, '000000', 3);
// Map.addLayer(display, {palette: '000000'}, 'vectors');
  
Export.table.toAsset({
    collection: vectors,
    description:'corrmap2vector',
    assetId: 'corrmap0305',
  });
  
// var options = {
//     // lineWidth: 1,
//     // pointSize: 2,
//     hAxis: {title: 'R and P value'},
//     vAxis: {title: 'Correlation Coefficient'},
//     title: 'Correlation map average'
// };
// var chart = ui.Chart.image.byClass(
//     corrmap, 'lucc', roi, ee.Reducer.mean(), 100000, lucc.get('landcover_class_names')
// ).setOptions(options);  
// print(chart);