融雪傾向グラフ
1. 高精度融雪トレンドグラフの合成
このプロジェクトの Google Earth Engine (GEE) コードは、MODIS Aqua と Terra の積雪プロダクトを組み合わせて、融雪-積雪 (雪季節学) の日付を導き出しました。米国国立雪氷データセンター (NSIDC) によって処理された未加工の MODIS 製品 (それぞれ、Terra プラットフォームと Aqua プラットフォームの MOD10A1 と MYD10A1) は低い傾向がありました。
このコードは、このバイアスを考慮して max メソッドを使用して 2 つのデータ ストリームを融合します。さらに、このスクリプトは、春の雪解け日 (積雪量が 5% 未満) と秋の積雪量 (積雪量が 5% を超える) の日付を計算した派生積雪季節学製品も生成します。
例として、スクリプトは単純な回帰分析を実行して、融雪日の傾向にフラグを立て、時間の経過とともに融雪が起こるのが遅いか早いかという質問に答えます。
//----------------------------------------------------------------------------------
//
// Google Earth Engine code to create a combined MODIS snow product, fusing both
// Terra and Aqua MxD10A1 data streams. Both data streams are prone to
// underestimate snow cover (due to MODIS properties and sky conditions). A fused
// products aleviates these issues partially by taking the maximum value of the
// fractional snow cover bands of each data stream.
//
// This raw data is stored in the maxCollection imageCollection. This data is used
// calculate the date of first snow melt and the date of snow accumulation. These
// dates are determined registering the DOY where fractional snow cover drops below
// or the day before it last exceeds 5%, only earliest and latest instances are noted.
// A final product can be derived, namely the maximum snow free period, by taking the
// difference between the snow accumulation and snow melt dates.
//
// Note that this code is writen for the northern hemisphere, and will not work
// for the southern hemisphere without adjustments (sum days between Jan 1. and
// the northern hemisphere snow melt day and from the accumulation day until
// Dec. 31th to get to the snow free period, and obviously reverse the captions
// on the dates melt becomes accumulation and vice versa).
//
// Code written by Koen Hufkens, April 2016
// under a GNU AFFERO GENERAL PUBLIC LICENSE - see repository
//
//----------------------------------------------------------------------------------
// -- settings --
// years to process (from start year t0 to end year t1)
// [2003 is the first year with full coverage]
var t0 = "2003", t1 = "2016";
var t_diff = (t1 - t0) + 1;
// -- functions --
var maxVal = function(feature) {
// load feature and set start time
// when mapping
var img = ee.Image(feature);
var startTime = img.select('NDSI_Snow_Cover').get('system:time_start');
// assign 0 values (which can be confused with NA)
// a small negative value for both bands
var b1c = img.expression(
'b1 == 0 ? -10 : b1', {
'b1': img.select('NDSI_Snow_Cover')
});
var b2c = img.expression(
'b1 == 0 ? -10 : b1', {
'b1': img.select('NDSI_Snow_Cover_1')
});
// take the maximum value of the remapped data
// while unmasking (assigning 0 to NA) the data
var maxval = b1c.unmask(0).max(b2c.unmask(0));
// update the mask (set all 0 values to NA)
maxval = maxval.updateMask(maxval.neq(0));
// substitute in the original 0 values, which
// are currently registered as -10
maxval = img.expression(
'b1 == -10 ? 0 : b1', {
'b1': maxval
});
// associate the start time with the new image
// and return the data
maxval = maxval.set('system:time_start',startTime);
return maxval;
};
// concat two layers from a collection
// into an image with x bands
// (can be iterated)
var cat = function(a, b) {
return ee.Image(b).addBands(a);
};
// rename bands for export to ee.Image() format
var rename_bands = function (img) {
img = ee.Image(img);
var names = img.bandNames();
var prefix = ee.String(img.get("system:time_start")).cat("_");
var new_names = names.map(function(b) {
return prefix.cat(b); });
return img.select(names, new_names);
};
// take the difference between snow accumulation
// and snow melt days (doy values)
var difference = function(feature) {
var img = ee.Image(feature);
var b1 = img.select('snowmelt');
var b2 = img.select('snowacc');
var startTime = b1.get('system:time_start');
return b2.subtract(b1).set('system:time_start',startTime);
};
// set the snow melt date
var returnDoy = function(feature) {
var image = ee.Image(feature);
// Compute time of the image in fractional years relative to the start.
var doy = ee.Date(image.get('system:time_start')).difference(start, 'day').add(1);
// replace values smaller than 5% snow cover with
// their respective DOY
var replacement = image.expression(
'SNOW <= 5 ? DOY : 9999', {
'SNOW': image.select('NDSI_Snow_Cover'),
'DOY': doy
}).toInt();
// mask anything above 5% coverage as empty
var withObs = replacement.neq(9999);
return replacement.updateMask(withObs);
};
// stick two bands together which are linked with an
// inner join
var concatBands = function(feature) {
return ee.Image.cat(feature.get('primary'),feature.get('secondary'));
};
// This function adds a time band to the image,
// for subsequent regression analysis
var createTimeBand = function(feature) {
var image = ee.Image(feature);
return image.addBands(image.metadata('system:time_start'));
};
// Specify an equals filter for image timestamps.
var filterTimeEq = ee.Filter.equals({
leftField: 'system:time_start',
rightField: 'system:time_start'
});
// -- main --
// If a longer time series is selected, calculate statistics
// for each year independently. This can probably be done
// with some sort of reduce statement, but the documentation is a bit
// of a mess when it comes to custom functions.
for (var i = parseInt(t0); i <= parseInt(t1); ++i) {
// int to string
var evalYear = i.toString();
// Load MODIS Fractional Snow Cover product (aqua / terra)
var terraCollection = ee.ImageCollection('MODIS/006/MOD10A1')
.select('NDSI_Snow_Cover')
.filterDate(evalYear.concat("-01-01"),evalYear.concat("-12-31"))
.sort('system:time_start', true);
var aquaCollection = ee.ImageCollection('MODIS/006/MYD10A1')
.select('NDSI_Snow_Cover')
.filterDate(evalYear.concat("-01-01"),evalYear.concat("-12-31"))
.sort('system:time_start', true);
// create a collection of both MOD/MYD data matched
// joined per band using an inner join
var innerJoin = ee.Join.inner();
// Apply the join.
var innerJoinedCollection = innerJoin.apply(terraCollection, aquaCollection, filterTimeEq);
// combine matching images into one image with (2) bands
var joinedCollection = innerJoinedCollection.map(concatBands);
// calculate the maximum value across the two bands MOD/MYD
// see maxVal function above
var yearlyCollection = joinedCollection.map(maxVal);
if (evalYear == 2003){
// pick a location for visual code validation
var loc = ee.Geometry.Point([-118.84257812499999, 55.29665181611441]);
// Register a function to draw a chart when a user clicks on the map.
var mv = ui.Chart.image.series(yearlyCollection, loc, null, 50)
.setOptions({
title: 'joined MCD10A1'});
var mod = ui.Chart.image.series(terraCollection, loc, null, 50)
.setOptions({
title: 'MOD10A1'});
var myd = ui.Chart.image.series(aquaCollection, loc, null, 50)
.setOptions({
title: 'MYD10A1'});
print(mv, mod, myd);
}
// how many bands in the dataset?
// should be close to 365
//print(yearlyCollection.size());
// Get the starting time reference.
var start = ee.Date(yearlyCollection.first().get('system:time_start'));
// subsitute all values < 5% with their respective DOY
var doyCollection = yearlyCollection.map(returnDoy);
// calculate the snow melt and snow fall (accumulation)
var snowMeltAcc = ee.ImageCollection(doyCollection)
.reduce(ee.Reducer.minMax()).unmask(366);
// The latter part is rather ugly, not optimal (but works)
// if first in loop swap with the original layer
if (evalYear == t0){
// snow melt
var tmpImg = snowMeltAcc
.select(['constant_min'],['snowmelt'])
.set('system:time_start',i);
var snowMelt = ee.ImageCollection(tmpImg);
// snow accumulation
var tmpImg = snowMeltAcc
.select(['constant_max'],['snowacc'])
.set('system:time_start',i);
var snowAcc = ee.ImageCollection(tmpImg);
}else{
// add snow melt layers
var tmpImg = snowMeltAcc.select(['constant_min'],['snowmelt'])
.set('system:time_start',i);
var snowMelt = snowMelt.merge(ee.ImageCollection(tmpImg));
// add snow accumulation layers
var tmpImg = snowMeltAcc.select(['constant_max'],['snowacc'])
.set('system:time_start',i);
var snowAcc = snowAcc.merge(ee.ImageCollection(tmpImg));
}
}
// rename bands
var snowMeltlist = snowMelt.toList(t_diff);
snowMeltlist = snowMeltlist.map(rename_bands);
var snowMeltImg = snowMeltlist.slice(1).iterate(cat, snowMeltlist.get(0));
var snowAcclist = snowAcc.toList(t_diff);
snowAcclist = snowAcclist.map(rename_bands);
var snowAccImg = snowAcclist.slice(1).iterate(cat, snowAcclist.get(0));
// Apply the join
var innerJoinedCollection = innerJoin.apply(snowMelt, snowAcc, filterTimeEq);
// combine matching images into one image with (2) band
var joinedCollection = innerJoinedCollection.map(concatBands);
// take the difference of the two bands
var snowFree = joinedCollection.map(difference);
// calculate median snow free season length and
// and create a mask from the result
var snowFreeMedian = ee.ImageCollection(snowFree)
.median();
var snowFreeMask = ee.Image(snowFreeMedian)
.lt(306);
// -- quick regression analysis --
// add a time band
var snowMelt = snowMelt.map(createTimeBand);
// linear regression on the data
var linearFit = ee.ImageCollection(snowMelt).select(['system:time_start', 'snowmelt'])
.reduce(ee.Reducer.linearFit());
// -- plotting results --
// land-water mask for visualization, removes faulty cover
// values for the oceans and water bodies
var hansenImage = ee.Image('UMD/hansen/global_forest_change_2013');
var data = hansenImage.select('datamask');
var waterMask = data.eq(1);
// final mask, excluding water bodies and
// areas with a too short of a winter season
var mask = waterMask.multiply(snowFreeMask);
var filteredFit = ee.Image(linearFit).multiply(mask);
// Plot the snow free trend
Map.addLayer(linearFit
.mask(mask),
{
min: 0, max: [-2, 0, 2], bands: ['scale', 'offset', 'scale']}, 'fit');
以下の融雪傾向グラフは、融雪の初期が赤色でマークされ、融雪の後期が青色でマークされていることを示しています。
