Google Earth Engine – Troubleshooting reduceRegion Not Working

google-earth-engine

I want to get the NDVI value for each point of my featureCollection vector. I had used reduceRegion for that in codeeditor javascript of GEE. I when i using the same function in python api of GEE, it is showing 'Unrecognized argument type to convert to a FeatureCollection'. May be some thing need to change in python api for the reducer function. Please help me in this regards.

import ee

import time
import datetime
import json
from ee.batch import Export

ee.Initialize()

startyear=2018
startmonth=01
startday=01

endyear=2018
endmonth=02
endday=01
def maskS2clouds(image):
    qa = image.select('QA60');
    cloudBitMask = ee.Number(2).pow(10).int();
    cirrusBitMask = ee.Number(2).pow(11).int();
    mask = qa.bitwiseAnd(cloudBitMask).eq(0) and(
             qa.bitwiseAnd(cirrusBitMask).eq(0));
    return image.updateMask(mask).divide(10000);

print("test")

boundary = ee.FeatureCollection('ft:1T-XrxMki2k49fhQdhMxesWKBoyyUJiKN70pErh9z')
#print(boundary)
gtpoints = ee.FeatureCollection('ft:1ZvFeFO5jHGo3VExlKNc-kdL-3hmJSeyfLzqLvHhy')
print (gtpoints)
image = (ee.ImageCollection('COPERNICUS/S2').
         filterBounds(boundary).
         filterDate(datetime.datetime(startyear,startmonth,startday),
                     datetime.datetime(endyear,endmonth,endday)).
         sort("CLOUD_COVERAGE_ASSESMENT").
         map(maskS2clouds). 
         limit(20))

mosaic = image.mosaic();
    #median = mosaic.median();
palette = ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
               '74A901', '66A000', '529400', '3E8601', '207401', '056201',
               '004C00', '023B01', '012E01', '011D01', '011301'];
clip = mosaic.clip(boundary)
#median = clip.median()
#print(clip)
ndvi = clip.normalizedDifference(['B8', 'B4'])
NDVI=ndvi.multiply(100).uint8()
#print(clip)
palette = ','.join(palette)
vis = {'min': 0, 'max': 300, 'bands':'nd', 'palette': palette}
toexport = NDVI.visualize(**vis).addBands(NDVI);
#print(toexport,'toexport')
points_ndvi = ndvi.reduceRegions({
  'collection':gtpoints,
  'reducer':ee.Reducer.first(),
  'scale':10
})
properties = ee.Feature(points_ndvi.first()).propertyNames()
new_names = properties.replace('first', 'ndvi')
points_ndvi = points_ndvi.select(properties, new_names)
print(points_ndvi,"test")

Best Answer

You can't use dictionaries of arguments in python; python has it's own way of doing keyword arguments for that:

points_ndvi = ndvi.reduceRegions(
  collection=gtpoints,
  reducer=ee.Reducer.first(),
  scale=10
)

(also, you don't need to use datetime):

.filterDate(ee.Date.fromYMD(startyear,startmonth,startday),
            ee.Date.fromYMD(endyear,endmonth,endday))

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[GIS] Getting List elements from .reduceRegion() results in Google Earth Engine

Yes, certainly it's a weir behavior. When you print(patch_years) it clearly says it's a List, but when trying to get a value it says that it is a Float<dimensions=2>, which matches with the column named 'histogram' in the FeatureCollection.

A workaround would be to bring it to the client side, and get it there:

var forest_years = forest.select('lossyear')

var test = forest_years.reduceRegions({
  collection:GB_patches,
  reducer: ee.Reducer.fixedHistogram(0,18,18),
  scale:30,
})


var first = ee.Feature(test.first())
var patch_years = ee.List(first.get('histogram'))
var patch_years_list = patch_years.getInfo()
print (first)
print (patch_years_list)
print (patch_years_list[1])

[GIS] reduceRegion() not working in Earth Engine

Here is an alternative approach that avoids nested map() calls, ee.Image.clip(), ee.Algorithms.If(), and accumulating results with a dictionary.

Modify the cloud masking function to add a band cloud_flag that indicates whether or not there is a cloud.

// Copyright 2019 Google LLC.
// SPDX-License-Identifier: Apache-2.0

var cloudMaskL457 = function(image) {
  // If the cloud bit (5) is set and the cloud confidence (7) is high
  // or the cloud shadow bit is set (3), then it's a cloudy pixel.
  var qa = image.select('pixel_qa');
  var cloud = qa.bitwiseAnd(1 << 5)
          .and(qa.bitwiseAnd(1 << 7))
          .or(qa.bitwiseAnd(1 << 3)).rename('cloud_flag');

  // Add a band that indicates if valid data exists in all bands.
  var validData = image.mask().reduce(ee.Reducer.min());

   // Mask out cloudy pixels and invalid pixels.
  new_image = image.updateMask(cloud.not()).updateMask(validData);

  // Add a new band that indicates whether there is a cloud. Note that this
  // new bands mask will be the same as the 'pixel_qa' band.
  var new_image = new_image.addBands(cloud);

  return new_image;
};

Map the function across a filtered collection of Landsat images:

var landsat5_filtered = LANDSAT5.filter(ee.Filter.calendarRange(1985, 1985, 'year'))
                                .filter(ee.Filter.calendarRange(3, 3, 'month'))
                                .filterBounds(table)
                                .map(cloudMaskL457);

Next define a function that processes each image, extracting values that correspond with the sample points. This function is then mapped over all of the images to create a feature collection of sampled values.

var reduceRegionsForImage = function(img) {
  // Add a property representing the time of the image.
  img = img.addBands(ee.Image.constant(img.get('system:time_start')).rename('time'));

  var meanFeatures = img.reduceRegions({
    collection: table,
    reducer: ee.Reducer.mean(),
    scale: 30,
  });
  return meanFeatures;
};
var extractValues = landsat5_filtered.map(reduceRegionsForImage).flatten();

The feature collection extractValues will contain many entries with null properties, because many of the images do not have values defined at the sample point locations. We can filter out those features by filtering for pixels that have non-null cloud_flag values (which includes the areas where clouds occur).

extractValues = extractValues.filter(ee.Filter.notNull(['cloud_flag']));

Next, select the attributes that you want to export, and rename them as needed.

extractValues = extractValues.select(
    ['ID', 'Lat', 'Lon', 'cloud_flag', 'time',
     'B1', 'B2', 'B3', 'B4', 'B5', 'B7'], 
    ['ID', 'Lat', 'Lon', 'cloud_flag', 'time',
     'Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2']
);

Finally, export the feature collection to create a CSV file.

Export.table.toDrive({
  collection: extractValues,
  description: 'export_sampled_values',
  folder: 'EE Exports',
  fileFormat: 'CSV'
});

Here is a link to the complete script: https://code.earthengine.google.com/55b196d9d01154dddcda362eeb90e041

Best Answer

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