Develop Reference

Gap filling a monthly-reduced imageCollectiom

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

Chart.js : sparser clickable data points

I've got a chart.js line chart, with 128 datapoints.
Currently, it uses the solution here for drag and drop verticals.
But it's a hassle. I'd like to limit the clickable / editable points to 30 or so.
(Then ideally it would do some smart interpolation of the surrounding values.)
Any ideas how to achieve this?
function move_handler(event)
{
// locate grabbed point in chart data
if (activePoint != null) {
var data = activePoint._chart.data;
var datasetIndex = activePoint._datasetIndex;
// read mouse position
const helpers = Chart.helpers;
var position = helpers.getRelativePosition(event, myChart);
// convert mouse position to chart y axis value
var chartArea = window.myChart.chartArea;
var yAxis = window.myChart.scales["y-axis-0"];
var yValue = map(position.y, chartArea.bottom, chartArea.top, yAxis.min, yAxis.max);
// update y value of active data point
data.datasets[datasetIndex].data[activePoint._index] = yValue;
// try {
// for (let i = -5;i < 5;i++){
// data.datasets[datasetIndex].data[activePoint._index + i] = yValue;
// }
// }
// catch (err) {}
window.myChart.update();
};
};

I found Smooth.js which did the trick

How to concatenate arrays obtained from imagecollections along the 1-axis in Google Earth Engine

I get an error when trying to concatenate 3 arrays along the 1-axis.
I obtained an array from a NDVI image collection using .toArrayPerBand(0)
Then I created two other arrays using .arraySlice(), which give me the values of 1 and 2 days before.
I want to concatenate these three arrays using .arrayCat() along the 1 axis, but I get this error:
Image (Error)
Image.arrayCat: Incompatible type dimensions, found 'Type<Float<-1.0, 1.0, dimensions=2>>' and 'Type<Float<-1.0, 1.0, dimensions=1>>'.
I need the three arrays to be concatenated so that I can compute for each date the maximum in the last 3 dates using .arrayReduce().
// 3 PREVIOUS VALUES MAXIMUM NDVI
//define area of interest and the image collection
var aoi =
ee.Geometry.Polygon(
[[[11.111455811313702, 46.3205838600638],
[11.111455811313702, 46.31527834569152],
[11.11800040131004, 46.31527834569152],
[11.11800040131004, 46.3205838600638]]], null, false);
var ndvi_IC=ee.ImageCollection("LANDSAT/LC08/C01/T1_8DAY_NDVI")
.filterDate('2020-04-01','2020-10-01')
.filter(ee.Filter.bounds(aoi))
//transform the image collection in an array
var array=ndvi_IC.toArrayPerBand(0)
//define the function to obtain for each date the maximum values of the 3 previous dates
var computemax3previous = function(image) {
//I want to create 3 arrays:
//ar_original with values in order :0,1,2,3,4,5,6,7,8,9,10
//ar_1torightfilled with values in order:0,0,1,2,3,4,5,6,7,8,9
//ar_2torightfilled with values in order:0,0,0,1,2,3,4,5,6,7,8
var arpos0= image.arraySlice({axis: 0, start: 0, end: 1})//the first value of the array(position0)
var ar_original=image.arraySlice({axis: 0, start: 0})
var ar_1toright=image.arraySlice({axis: 0, start: 0, end: -1})
var ar_1torightfilled=arpos0.arrayCat(ar_1toright,0)
var ar_2toright=image.arraySlice({axis: 0, start: 0, end: -2})
var ar_2torightfilled=arpos0.arrayCat(arpos0,0).arrayCat(ar_2toright,0)
//Concatenate the 3 arrays on the 1-axis
var A3dates= ar_original.arrayCat(ar_1torightfilled,1).arrayCat(ar_2torightfilled,1)
print(A3dates)
// reduce along the 1-axis to obtain the 3previous dates maximum
var arraymax = array.arrayReduce(ee.Reducer.max(), [1])
.arrayProject([0])//retain just the 0-axis
return arraymax}
var max3previous=(computemax3previous(array))
https://code.earthengine.google.com/4a8a10a6e285d19d7ce62ffc1406c828

Error origins from here: var A3dates = ar_original.arrayCat(ar_1torightfilled, 1).arrayCat(ar_2torightfilled, 1).
In your version, ar_original and ar_1torightfilled are 1D array. But after arrayCat,
ar_original.arrayCat(ar_1torightfilled, 1) will be 2D.
//define area of interest and the image collection
var aoi =
ee.Geometry.Polygon(
[[[11.111455811313702, 46.3205838600638],
[11.111455811313702, 46.31527834569152],
[11.11800040131004, 46.31527834569152],
[11.11800040131004, 46.3205838600638]]], null, false);
var imgcol = ee.ImageCollection("LANDSAT/LC08/C01/T1_8DAY_NDVI")
.filterDate('2020-04-01', '2020-10-01')
.filter(ee.Filter.bounds(aoi));
// transform the image collection in an array
// The result will be masked if any input is masked.
imgcol = imgcol.map(function(img) { return img.unmask(-1.0); });
var array = imgcol.toArray();
print(array);
Map.centerObject(aoi, 12);
// Map.addLayer(imgcol);
// Map.addLayer(array);
//define the function to obtain for each date the maximum values of the 3 previous dates
var computemax3previous = function (image) {
//I want to create 3 arrays:
//ar_original with values in order :0,1,2,3,4,5,6,7,8,9,10
//ar_1torightfilled with values in order:0,0,1,2,3,4,5,6,7,8,9
//ar_2torightfilled with values in order:0,0,0,1,2,3,4,5,6,7,8
var arpos0 = image.arraySlice({ axis: 0, start: 0, end: 1 });//the first value of the array(position0)
var ar_original = image.arraySlice({ axis: 0, start: 0 });
var ar_1toright = image.arraySlice({ axis: 0, start: 0, end: -1 });
var ar_1torightfilled = arpos0.arrayCat(ar_1toright, 0);
var ar_2toright = image.arraySlice({ axis: 0, start: 0, end: -2 });
var ar_2torightfilled = arpos0.arrayCat(arpos0, 0).arrayCat(ar_2toright, 0);
//Concatenate the 3 arrays on the 1-axis
var A3dates = ar_original.arrayCat(ar_1torightfilled, 1).arrayCat(ar_2torightfilled, 1);
// reduce along the 1-axis to obtain the 3previous dates maximum
var arraymax = array.arrayReduce(ee.Reducer.max(), [1])
.arrayProject([0]);//retain just the 0-axis
return arraymax;
};
var max3previous = computemax3previous(array);
print(max3previous);
Map.addLayer(max3previous, {}, 'max3previous')

I want to solve the "system:time_start" error when trying to output GLDAS monthly precipitation to CSV using Google Earth Engine

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.

Google Earth Engine - image gap filling in imagecollection

i need to combine MODIS Terra and Aqua collection in a new collection in order to get the max NDSI value by day.
The problem is that, for some years, the dataset has some gaps and the join function keeps just the common images on the two dataset.
For example in 2003 the Terra dataset has 358 images while the Aqua ones has 365, the combined dataset results with just 358 images.
I would like the combine function keeps the Aqua images values when the Terra ones are not available in order to have 365 images at the end.
Any suggestion? Thanks!
here is the GEE link to the code
https://code.earthengine.google.com/b71873bff48fe5f3de883763f25c4938
here is the code
var MOD = ee.ImageCollection("MODIS/006/MOD10A1")
var MYD = ee.ImageCollection("MODIS/006/MYD10A1")
var ROI = geometry
// MONITORING DATE
var currdate = ee.Date.fromYMD(2004,1,1)
var firstdate = ee.Date.fromYMD(2003,1,1)
Map.centerObject(ROI,5);
var clip = function(img) {return img.clip(ROI);}
var MOD = MOD.filterDate(firstdate, currdate)
.map(clip)
var MYD = MYD.filterDate(firstdate, currdate)
.map(clip)
print(MOD,'MOD')
print(MYD,'MYD')
// Combine collections (MOD & MYD)
var Filter = ee.Filter.equals({
leftField: 'system:time_start',
rightField: 'system:time_start'
});
var simpleJoin = ee.Join.inner();
var innerJoin = ee.ImageCollection(simpleJoin.apply(MOD, MYD, Filter))
var comb = innerJoin.map(function(feature) {
return ee.Image.cat(feature.get('primary'), feature.get('secondary'));
})
print(comb,'comb');
// calculate maximum value from the two MODIS images
var max = function(img) {
var br = img.select('NDSI_Snow_Cover').max(img.select('NDSI_Snow_Cover_1'))
return br.rename('NDSI')
.copyProperties(img, ['system:time_start', 'system:time_end']);
}
var NDSImax = comb.map(max)
print(NDSImax,'NDSImax')
// Map.addLayer(NDSImax.first().clip(ROI).select('NDSI'),{min: -9,max: 100,palette: ['red', 'white', 'blue']}, 'NDSImax')