I'm attempting to gap fill 9 datasets on Earth Engine. So far I've been able to reduce the daily dataset to monthly taking help of the site but I'm unable to apply focal mean to fill any possible nodata instances in the pixel values on the resultant collection. Need some help in this regard.
var bess = ee.ImageCollection("SNU/ESL/BESS/Rad/v1"),
aod = ee.ImageCollection("MODIS/006/MCD19A2_GRANULES"),
ndsi = ee.ImageCollection("MODIS/006/MYD10A1"),
ndwi = ee.ImageCollection("MODIS/MYD09GA_006_NDWI"),
evi = ee.ImageCollection("MODIS/061/MYD13A2"),
lst = ee.ImageCollection("MODIS/061/MYD11A1"),
erafi = ee.ImageCollection("ECMWF/ERA5_LAND/HOURLY"),
ele = ee.Image("CGIAR/SRTM90_V4"),
var vari = bess;
// function to generate monthly mean
var monmei = ee.List.sequence(0, 16*12).map(function(n) { // .sequence: number of years from starting year to present
var start = ee.Date('2021-01-01').advance(n, 'month'); // Starting date
var end = start.advance(1, 'month'); // Step by each iteration
return vari
.filterDate(start, end)
.select('RSDN_Daily')
.mean()
.set('system:time_start', start.millis());
});
print(monmei)
var fill = monmei.focal_mean(1, 'square', 'pixels', 20)
var full = fill.blend(monmei)
filvari = full
I am using GoogleEarthEngine to look at precipitation data for the Mekong River basin. The GLDAS data are set every three hours per day. My goal is to extract the GLDAS data from 2000 to 2020 by adding up the data by month.
I want to extract the total monthly precipitation in GLDAS using the Google Earth Engine, but I cannot extract the CSV due to the error
Image.date: Image '120' has a 'system:time_start' property which is not a number: 2010-01-01T00:00:00
I think I can extract the CSV by converting "system:time_start", how should I change it?
var studyArea = ee.Geometry.Rectangle(102, 8.5, 107, 15);
Map.centerObject(mekong, 9);
// 解析対象年
var years = ee.List.sequence(2000, 2019);
var months = ee.List.sequence(1, 12);
var early = ('2010-01-01');
var late = ('2011-01-01');
// MOD11A1の取り出し
var image = ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H')
.filterDate(early, late).filterBounds(mekong);
//print(image);
// mmに変換してmodLSTcに保存
var gldas_precipitation = image.select('Rainf_f_tavg');
var gldas_precipitation_mm = gldas_precipitation.map(function(img)
{return img.multiply(10080.0).copyProperties(img, ['system:time_start'])});
// 変数gldas_precipitation_mm_monthの中に月単位のメジアンデータを保存 ////////////////////////////////////////////
var gldas_precipitation_mm_month = ee.ImageCollection.fromImages(
years.map(function(y) {
return months.map(function(m) {
var monthly = gldas_precipitation_mm.filter(ee.Filter.calendarRange(y, y, 'year'))
.filter(ee.Filter.calendarRange(m, m, 'month'))
.sum()
.rename('precipitation_mm_month');
return monthly.set('year', y).set('system:time_start', ee.Date.fromYMD(y, 1, 1))
.set('month', y).set('system:time_start', ee.Date.fromYMD(y, m, 1));
});
}).flatten());
var gldas_precipitation_mm_month = gldas_precipitation_mm_month.filterBounds(mekong);
// TSLのポリゴン ///////////////////////////////////////////////////////////////////////////////////
var empty = ee.Image().byte();
// Paint all the polygon edges with the same number and width, display
var outline = empty.paint({
featureCollection: mekong,
color: 1,
width: 2
});
Map.addLayer(outline, {palette: 'FF0000'}, 'TSL');
//output_csv_precipitation
//Create variables and extract data
var scale = gldas_precipitation_mm_month.mean().projection().nominalScale().multiply(0.05); print(scale);
var gldas = gldas_precipitation_mm_month.filter(ee.Filter.listContains('system:band_names', gldas_precipitation_mm.mean().bandNames().get(0)));
var ft = ee.FeatureCollection(ee.List([]));
//Function to extract values from image collection based on point file and export as a table
var fill = function(img, ini) {
var inift = ee.FeatureCollection(ini);
var ft2 = img.reduceRegions(mekong, ee.Reducer.mean(), scale);
var date = img.date().format("YYYY/MM/dd");
var ft3 = ft2.map(function(f){return f.set('month', date)});
return inift.merge(ft3);
};
// Iterates over the ImageCollection
var profile = ee.FeatureCollection(gldas_precipitation_mm_month.iterate(fill, ft));
print(profile,'profile');
The value of the property system:time_start must be a number (even though it would make sense for it to be a Date, the system design didn't end up that way). You must change calls like
.set('system:time_start', ee.Date.fromYMD(y, m, 1))
to
.set('system:time_start', ee.Date.fromYMD(y, m, 1).millis())
While looking, I see other possible problems here:
return monthly.set('year', y).set('system:time_start', ee.Date.fromYMD(y, 1, 1))
.set('month', y).set('system:time_start', ee.Date.fromYMD(y, m, 1));
This is setting the month property value to the y variable (not m), and it's setting system:time_start twice (so only the second value will be used). Probably this is not what you meant. I have not looked at what you're intending to do with the collection, so you'll have to figure that part out yourself.
I'm working on a platform called Google Earth Engine that allows compute heavy analysis of satellite images on a cloud.
I have written a code in Javascript that takes two images, use one band inside them and then it suppose to create scatter plot.
I have problem with the part of the scatter plot which I struggle to understand.
Any time I run this part:
// Convert the band data to plot on the y-axis to arrays.
var x= ee.Array(imageNDVIcor.get('NDVI'));
var y = ee.Array(SARreproject.get('VH'));
// Make a band correlation chart.
var chart = ui.Chart.array.values(y, 0, x)
.setSeriesNames(['SAR vs NDVI'])
.setOptions({
title: 'NDVI vs SAR VH',
hAxis: {'title': 'SAR VH'},
vAxis: {'title': 'NDVI'},
pointSize: 3,
});
// Print the chart.
print(chart);
I get the following error:
Array: Parameter 'values' is required
I don't understand which values are missing or how and when they got lost. I put here the full code I have, any help to understand where the values dissapeard will be helpful.
//STEP 1:NDVI
/**
* Function to mask clouds using the Sentinel-2 QA band
* #param {ee.Image} image Sentinel-2 image
* #return {ee.Image} cloud masked Sentinel-2 image
*/
function maskS2clouds(image) {
var qa = image.select('QA60');
// Bits 10 and 11 are clouds and cirrus, respectively.
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
.and(qa.bitwiseAnd(cirrusBitMask).eq(0));
return image.updateMask(mask).divide(10000)
.copyProperties(image, ['system:time_start']);
}
// Map the function over one year of data and take the median.
// Load Sentinel-2 TOA reflectance data.
var dataset = ee.ImageCollection('COPERNICUS/S2')
.filterDate('2019-01-01', '2019-11-12')
// Pre-filter to get less cloudy granules.
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
.select('B2','B3','B4','B8','QA60')
.filterBounds(geometry)
.map(maskS2clouds);
var clippedCol=dataset.map(function(im){
return im.clip(geometry);
});
// Get the number of images.
var count = dataset.size();
print('Count: ',count);
// print(clippedCol);//here I get the error messege "collection query aborted after accumulation over 5000 elements
// print(dataset,'dataset');//the same error here
//function to calculate NDVI
var addNDVI = function(image) {
var ndvi = image.normalizedDifference(['B8', 'B4'])
.rename('NDVI')
.copyProperties(image,['system:time_start']);
return image.addBands(ndvi);
};
//NDVI to the clipped image collection
var withNDVI = clippedCol.map(addNDVI).select('NDVI');
var NDVIcolor = {
min: 0,
max:1,
palette: ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
'66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
'012E01', '011D01', '011301'],
};
//Filter according to number of pixels
var ndviWithCount = withNDVI.map(function(image){
var countpixels = ee.Number(image.reduceRegion({
reducer: ee.Reducer.count(),
geometry: geometry,
crs: 'EPSG:4326',
scale: 20,
}).get('NDVI'));
return image.set('count', countpixels);
});
print(ndviWithCount, 'ndviWithCount');
var max = ndviWithCount.reduceColumns(ee.Reducer.max(), ["count"]);
print('Number of pixels max:',max.get('max'));
//filter between a range
var filterNDVI = ndviWithCount.filter(ee.Filter.rangeContains(
'count', 98258, 98258));
print('Filtered NDVI:', filterNDVI);
var listOfImages =(filterNDVI.toList(filterNDVI.size()));
var listOfNumbers = [5]
for (var i in listOfNumbers) {
var image = ee.Image(listOfImages.get(listOfNumbers[i]));
var toexport=image.visualize(NDVIcolor).addBands(image);
// do what ever you need with image
Map.addLayer(image, NDVIcolor, i);
// Export.image.toDrive({
// image: toexport.toFloat(),
// description: i,
// scale:20,
// crs:'EPSG:4326',
// maxPixels:1310361348,
// region:geometry.geometry().bounds()
// });
}
Map.centerObject(geometry);
//STEP2: SAR
// Filter the collection for the VH product from the descending track
//var geometry=MITR;
var Sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.filter(ee.Filter.eq('instrumentMode', 'IW'))
.select('VH')
.filterDate('2019-01-01','2019-11-12')
.filterBounds(geometry);
var clippedVH= Sentinel1.map(function(im){
return im.clip(geometry);
});
var clippedVHsize=clippedVH.size();
print('SAR Size:',clippedVHsize);
print('SAR images data:',clippedVH)
var listOfImagesSAR =(clippedVH.toList(clippedVH.size()));
var listOfNumbersSAR = [3];
for (var i in listOfNumbersSAR) {
var image = ee.Image(listOfImagesSAR.get(listOfNumbersSAR[i]));
var toexport=image.visualize({min: -30, max: 1}).addBands(image);
// do what ever you need with image
Map.addLayer(image,{min: -30, max: 1}, i);
// Export.image.toDrive({
// image: toexport.toFloat(),
// description: i,
// scale:10,
// crs:'EPSG:4326',
// maxPixels:1310361348,
// region:geometry.geometry().bounds()
// });
}
//print(ui.Chart.image.series(filterNDVI, geometry, ee.Reducer.mean(), 20));
//(ui.Chart.image.series(clippedVH, geometry, ee.Reducer.mean(), 10));
//select the images for scatter plot
//select NDVI
var imageNDVIcor=ee.Image(listOfImages.get(5));
var imageSARcor=ee.Image(listOfImagesSAR.get(3));
// Get information about the projection.
var sar1Projection = imageSARcor.projection();
print('SAR projection:', sar1Projection);
var NDVIProjection = imageNDVIcor.projection();
print('NDVI projection:', NDVIProjection);
//resample SAR image to NDVI image
var SARreproject=imageSARcor.reduceResolution({reducer: ee.Reducer.mean()}).reproject({crs: NDVIProjection});
Map.addLayer(SARreproject,{min: -30, max: 1},'Reproject SAR');
// print(imageNDVIcor)
// print(imageSARcor)
//Map.addLayer(imageNDVIcor,NDVIcolor,'NDVI select');
//Map.addLayer(imageSARcor,{min: -30, max: 1},'SAR select');
// Convert the band data to plot on the y-axis to arrays.
var x= ee.Array(imageNDVIcor.get('NDVI'));
var y = ee.Array(SARreproject.get('VH'));
// Make a band correlation chart.
var chart = ui.Chart.array.values(y, 0, x)
.setSeriesNames(['SAR vs NDVI'])
.setOptions({
title: 'NDVI vs SAR VH',
hAxis: {'title': 'SAR VH'},
vAxis: {'title': 'NDVI'},
pointSize: 3,
});
// Print the chart.
print(chart);
Not sure what you are trying to accomplish. But if you are trying to compare the values between NDVI and VH, here's a way to do that. Click on any point inside the geometry, and the values for the point in the time-series are plotted in the console tab.
var geometry =
ee.Geometry.Polygon(
[[[-122.56145019531249, 37.93899992220671],
[-122.56145019531249, 37.37365054197817],
[-121.80888671874999, 37.37365054197817],
[-121.80888671874999, 37.93899992220671]]], null, false);
Map.centerObject(geometry, 10);
function maskS2clouds(image) {
var qa = image.select('QA60');
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(qa.bitwiseAnd(cirrusBitMask).eq(0));
return image.updateMask(mask).divide(10000).copyProperties(image, ['system:time_start']);
}
// Sentinel 2
var sentinel2 = ee.ImageCollection('COPERNICUS/S2')
.filterDate('2019-01-01', '2019-12-31')
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
.select('B2','B3','B4','B8','QA60').filterBounds(geometry).map(maskS2clouds);
// NDVI
var addNDVI = function(image) {
var ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI').copyProperties(image,['system:time_start']);
return image.addBands(ndvi);
};
var sentinel2NDVI = sentinel2.map(addNDVI).select('NDVI');
// SAR
var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.filter(ee.Filter.eq('instrumentMode', 'IW'))
.select('VH').filterDate('2019-01-01','2019-12-31').filterBounds(geometry);
var list1 = sentinel2NDVI.toList(sentinel2NDVI.size());
var list2 = sentinel1.toList(sentinel1.size());
var list = list1.cat(list2);
var combinedData = ee.ImageCollection(list);
// chart
var generateChart = function (coords) {
print('-----------------------');
var lat = coords.lat;
var lon = coords.long;
var point = ee.Geometry.Point(coords.lon, coords.lat);
var dot = ui.Map.Layer(point, {color: '000000'}, 'clicked location');
var chart = ui.Chart.image.series(combinedData, point, ee.Reducer.mean(), 30);
chart.setOptions({
title: 'VH vs NDVI',
hAxis: {'title': 'Time'},
vAxis: {'title': 'SAR VH'},
pointSize: 3,
});
print(chart);
};
Map.onClick(generateChart);
Map.style().set('cursor', 'crosshair');
Also, get the GEE link for the same from here.
I am relatively new to JavaScript and Django and I am struggling with passing big data to my google chart.
I have a chart representing velocities for a given date and distance. In my django views I create list of distances, dates and according velocities. I also generate there a list with sorted values occuring in velocity list and a list with colors according to velocity's value.
I want to have a chart with velocity map with applied colortable like this :
http://i.imgur.com/9Tyv8Rn.jpg
So I used scatter chart with velocity series. The chart is dynamic, it's diffrent for every item selected by a user.
JS to generate rows and columns :
// Define data table rows:
var rows = [];
var rows_list = [];
var vl_max = vel_list.length;
for (i=0; i < vl_max; i+=1) {
var date_tmp = new Date(date_list[i].split(',')[0],date_list[i].split(',')[1]-1,date_list[i].split(',')[2]);
var date = [date_tmp];
var vel_tmp = vel_list[i];
var vtemp_max = vel_tmp.length;
var tooltip_dsname = dsname_list[i];
var tooltip_track = track_list[i];
for (j=0; j < vtemp_max; j+=1) {
var cell = [{v : date_tmp}];
for (k=0; k < vr_max; k+=1) {
var vel_full = vel_tmp[j];
var vel = vel_full.toFixed(1);
if (vel == vel_range[k]) {
// tooltip:
var dist = dist_list[j]/1000;
var yyyy = date_tmp.getFullYear().toString();
var mm = (date_tmp.getMonth()+1).toString(); // getMonth() is zero-based
var dd = date_tmp.getDate().toString();
var tooltip_date = yyyy + "-" + (mm[1]?mm:"0"+mm[0]) + "-" + (dd[1]?dd:"0"+dd[0]);
var tooltip = "<b>dataset: </b>"+tooltip_dsname+"<br><b>date: </b>"+tooltip_date+"<br><b>track: </b>"+tooltip_track+"<br><b>distance: </b>"+dist+" k"+mapunit+"<br><b> velocity: </b>"+vel_full.toFixed(2)+" m/d";
var color = color_list[k]
var style = "point { shape-type: square; fill-color: "+color+";}"
} else {
var dist = NaN;
var tooltip = "empty" ;
var style = "empty" ;
}
cell.push({v: dist},{v: tooltip},{v:style});
}
rows_list.push({c: cell});
}
};
Here is JSfiddle for chart generation with smaller data :
http://jsfiddle.net/joannao89/t26ooyrt/2/
The problem that I have is while the chart is working for smaller data, once I want to load it for a long distance and a wide date range, the browser keeps on popping us this line : "A website is slowing down your browser, what would you like to do ? {stop} {wait}"
I know that this is probably the problem of too large amount of rows, my website generates also 3 other charts like this, with the same data but in another X-Y axis combination (for example time on X-axis, velocity on Y-axis and distance as series) and it works perfectly fine. That's why I would like to pass the data to the chart in some faster way, but I have no clue how.
I already tried to use setTimeout, but it doesn't change a lot. I also tried doing a little less coding on JS side and more in django views, but it also didn't help.
So any suggestions about how to solve this will be very appreciated!
I am doing one project while stuck at the first step. The idea is to set up a 3D model. The (x,y) is the geometry area and z is the value of interests. I need simulate all area of (x,y) coordinates.Firstly, I try to initiate the z value for all (x,y) coordinate. I met problems at this step by following codes.
<script type="text/javascript">
// use the rectangle as the "unit" of the 2D model.
var x = [];
var y = [];
var z = [];
var R1 = 5;
var dataset = [];
// use function to initiate a row
var rowGen = function(k){
for (j=0;j<10;j++){
x.push(j*5);
y.push(k*2);
z.push(R1);
dataset.push({x:x[j],y:y[j],z:z[j]});
}
}
// initiate 10 rows
for (i=0;i<10;i++){
rowGen(i);
}
This always just initiate the z value for parts of the (x,y) area. Just the first row (y=0). I checked dataset in console, it has the correct numbers(100) of objects （x,y,z）, while all of them have y=0, which shows in the figure only one line, instead of 2D area.
I tried a different loops to do that but just can not find out the solution. Could you please help bring me some light?
Just found the logic bug. I had pushed the wrong value into y.Changed the code as below works.
// use the rectangle as the "unit" of the 2D model.
var x = [];
var y = [];
var z = [];
var R1 = 5;
var dataset = [];
// use function to initiate a row
var rowGen = function(k){
for (j=0;j<10;j++){
x.push(j*5);
y.push(k*2);
z.push(R1);
dataset.push({x:x[j],y:k*2,z:z[j]});
}
}
// initiate 10 rows
for (i=0;i<10;i++){
rowGen(i);
}