Land-use classification using the Random Forest algorithm of the GEE - google-earth-engine

Question

I am working on a land use classification program using the RS algorithm of the GEE platform. The codes are as the following link.
https://code.earthengine.google.com/7e99f1de58c1251bd9bff0ff7af9368b
Specific codes:
var table = ee.FeatureCollection("users/zongxuli/Jing_Jin_Ji");
//Set up bands and corresponding band names
var inBands = ee.List([1,2,3,4,5,7,6,'pixel_qa'])
var outBands = ee.List(['blue','green','red','nir','swir1','temp', 'swir2','pixel_qa'])
// Get Landsat data
var l8s = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterDate(2019-01-01,2019-12-31)
.filterBounds(table)
.select(inBands,outBands)
.filter(ee.Filter.lt("CLOUD_COVER",10))
function getIndexes(image){
// Normalized Difference Vegitation Index(NDWI)
var ndvi = image.normalizedDifference(['nir','red']).rename("ndvi");
image = image.addBands(ndvi);
// Normalized Difference Snow Index(NDWI)
var ndsi = image.normalizedDifference(['green','swir1']).rename("ndsi");
image = image.addBands(ndsi);
// Normalized Difference Water Index(NDWI)
var ndwi = image.normalizedDifference(['nir','swir1']).rename("ndwi");
image = image.addBands(ndwi);
// add Enhanced Vegetation Indexes
var evi = image.expression('2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', {
'NIR' : image.select('nir'),
'RED' : image.select('red'),
'BLUE': image.select('blue') }).float();
image = image.addBands(evi.rename('evi'));
// Add Index-Based Built-Up Index (IBI)
var ibiA = image.expression('2 * SWIR1 / (SWIR1 + NIR)', {
'SWIR1': image.select('swir1'),
'NIR' : image.select('nir')}).rename(['IBI_A']);
var ibiB = image.expression('(NIR / (NIR + RED)) + (GREEN / (GREEN + SWIR1))', {
'NIR' : image.select('nir'),
'RED' : image.select('red'),
'GREEN': image.select('green'),
'SWIR1': image.select('swir1')}).rename(['IBI_B']);
var ibiAB = ibiA.addBands(ibiB);
var ibi = ibiAB.normalizedDifference(['IBI_A', 'IBI_B']);
image = image.addBands(ibi.rename('ibi'));
return(image);
}
function getTopography(image,elevation) {
// Calculate slope, aspect and hillshade
var topo = ee.Algorithms.Terrain(elevation);
// From aspect (a), calculate eastness (sin a), northness (cos a)
var deg2rad = ee.Number(Math.PI).divide(180);
var aspect = topo.select(['aspect']);
var aspect_rad = aspect.multiply(deg2rad);
var eastness = aspect_rad.sin().rename(['eastness']).float();
var northness = aspect_rad.cos().rename(['northness']).float();
// Add topography bands to image
topo = topo.select(['elevation','slope','aspect']).addBands(eastness).addBands(northness);
image = image.addBands(topo);
return(image);
}
// Get an image to train and apply classification to.
var image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
.filterBounds(table)
.first();
// get bands
var bands=image.bandNames();
print(bands);
// integers starting from zero in the training data.
var label = 'lcode';
// Overlay the points on the imagery to get training.
var trainings = image.select(bands).sampleRegions({
collection: l8s, //.filterDate(2019-01-01,2019-12-31),
properties: [label],
scale: 30
});
// The randomColumn() method will add a column of uniform random
// numbers in a column named 'random' by default.
var sample = trainings.randomColumn();
var split = 0.7; // Roughly 70% training, 30% testing.
var training = sample.filter(ee.Filter.lt('random', split));
print(training.size());
// Random forest
var classifier = (ee.Classifier.smileRandomForest(15)
.train({
features: training,
classProperty: label,
inputProperties: bands
}));
var classified = image.classify(classifier);
print(classified);
So far, I always received the wrong message "Number (Error) Empty date ranges not supported for the current operation." when running the program. What am I doing wrong?

Answer 1

There is a quotation problem in image collection date filtering:
// Get Landsat data
var l8s = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterDate(2019-01-01,2019-12-31)
.filterBounds(table)
.select(inBands,outBands)
.filter(ee.Filter.lt("CLOUD_COVER",10))
it must be:
// Get Landsat data
var l8s = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterDate('2019-01-01','2019-12-31')
.filterBounds(table)
.select(inBands,outBands)
.filter(ee.Filter.lt("CLOUD_COVER",10))
And in training for classifier section:
// Overlay the points on the imagery to get training.
var trainings = image.select(bands).sampleRegions({
collection: l8s, //.filterDate(2019-01-01,2019-12-31),
properties: [label],
scale: 30 });
it must be :
// Overlay the points on the imagery to get training.
var trainings =
image.select(bands).sampleRegions({
collection: table,
properties: [label],
scale: 30 });