Need help with "Error: GeometryConstructors.MultiGeometry: Geometry coordinate projection requires non-zero maxError. (Error code: 3)"

[omega2025]

Hello all!

I am a student and very new to coding on Google Earth Engine. I am attempting to export the rasters I created so that I can work with them in RStudio. The error message I get is "Error: GeometryConstructors.MultiGeometry: Geometry coordinate projection requires non-zero maxError. (Error code: 3)". I researched the error and looked at similar discussions but haven't been able to solve the issue. Any help is greatly appreciated! Here is the code:

// Define function to mask clouds, scale, and add variables
// (NDVI, time and a constant) to Landsat 8 imagery.
function maskScaleAndAddVariable(image) {
// Bit 0 - Fill
// Bit 1 - Dilated Cloud
// Bit 2 - Cirrus
// Bit 3 - Cloud
// Bit 4 - Cloud Shadow
var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111',
2)).eq(0);
var saturationMask = image.select('QA_RADSAT').eq(0);

// Apply the scaling factors to the appropriate bands.
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-
    0.2);
var thermalBands = image.select('ST_B.*').multiply(0.00341802)
    .add(149.0);

// Replace the original bands with the scaled ones and apply the masks.
var img = image.addBands(opticalBands, null, true)
    .addBands(thermalBands, null, true)
    .updateMask(qaMask)
    .updateMask(saturationMask);
var imgScaled = image.addBands(img, null, true);

// Now we start to add variables of interest.
// Compute time in fractional years since the epoch.
var date = ee.Date(image.get('system:time_start'));
var years = date.difference(ee.Date('1970-01-01'), 'year');
// Return the image with the added bands.
return imgScaled
    // Add an NDVI band.
    .addBands(imgScaled.normalizedDifference(['SR_B5', 'SR_B4'])
        .rename('NDVI'))
    // Add a time band.
    .addBands(ee.Image(years).rename('t'))
    .float()
    // Add a constant band.
    .addBands(ee.Image.constant(1));

}

// //Merced point:
var centerPoint = ee.Geometry.Point([ -120.37559812236756, 37.27069238838713]);
var crop1 = ee.Geometry.Point([ -120.33645932842225, 37.28298585843524]);
var crop2 = ee.Geometry.Point([ -120.41336362529725, 37.260582920157454]);

// // Import the USGS Landsat 8 Level 2, Collection 2, Tier 1 image collection),
// // filter, mask clouds, scale, and add variables.
var landsat8sr = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
.filterBounds(roi)
.filterDate('2022-01-01', '2024-01-01')
.map(maskScaleAndAddVariable);

var landsat9sr = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')
.filterBounds(roi)
.filterDate('2022-01-01', '2024-01-01')
.map(maskScaleAndAddVariable);

var landsatCombined = landsat8sr.merge(landsat9sr);
// Set map center over the ROI.
Map.centerObject(roi, 10);

// // Plot a time series of NDVI at a single location.
var landsatChart = ui.Chart.image.series(landsatCombined.select('NDVI'), centerPoint)
.setChartType('ScatterChart')
.setOptions({
title: 'Landsat 8 and 9 NDVI time series at centerPoint',
lineWidth: 1,
pointSize: 3,
});
print(landsatChart);

// // Plot a time series of NDVI with a linear trend line at a single location.
var landsatChartTL = ui.Chart.image.series(landsatCombined.select('NDVI'), centerPoint)
.setChartType('ScatterChart')
.setOptions({
title: 'Landsat 8 and 9 NDVI time series at centerPoint',
trendlines: {
0: {
color: 'CC0000'
}
},
lineWidth: 1,
pointSize: 3,
});
print(landsatChartTL);

// // List of the independent variable names
var independents = ee.List(['constant', 't']);

// // Name of the dependent variable.
var dependent = ee.String('NDVI');

// // Compute a linear trend. This will have two bands: 'residuals' and
// // a 2x1 (Array Image) band called 'coefficients'.
// // (Columns are for dependent variables)

var trend = landsatCombined.select(independents.add(dependent))
.reduce(ee.Reducer.linearRegression(independents.length(), 1))
.clip(roi);
Map.addLayer(trend, {}, 'trend array image');

// // Flatten the coefficients into a 2-band image.
var coefficients = trend.select('coefficients')
// Get rid of extra dimensions and convert back to a regular image
.arrayProject([0])
.arrayFlatten([independents])
.clip(roi);
//Map.addLayer(coefficients, {}, 'coefficients image');
//print(coefficients, 'coefficients');

// Compute a detrended series.
var detrended = landsatCombined.map(function(image) {
return image.select(dependent).subtract(
image.select(independents).multiply(coefficients)
.reduce('sum'))
.rename(dependent)
.copyProperties(image, ['system:time_start']);
});

// Plot the detrended results.
var detrendedChart = ui.Chart.image.series(detrended, centerPoint, null, 30)
.setOptions({
title: 'Detrended Landsat time series at ROI',
lineWidth: 1,
pointSize: 3,
trendlines: {
0: {
color: 'CC0000'
}
},
});
print(detrendedChart);

// Use these independent variables in the harmonic regression.
var harmonicIndependents = ee.List(['constant', 't', 'cos', 'sin']);
//print(harmonicIndependents, 'harmonicIndependents');

// Add harmonic terms as new image bands.
var harmonicLandsat = landsatCombined.map(function(image) {
var timeRadians = image.select('t').multiply(2 * Math.PI);
return image
.addBands(timeRadians.cos().rename('cos'))
.addBands(timeRadians.sin().rename('sin'))
.clip(roi);
});
print(harmonicLandsat, 'harmonicLandsat');

// // Fit the model.
var harmonicTrend = harmonicLandsat
.select(harmonicIndependents.add(dependent))
// The output of this reducer is a 4x1 array image.
.reduce(ee.Reducer.linearRegression(harmonicIndependents.length(),
1));
print(harmonicTrend, 'harmonicTrend');

// // Turn the array image into a multi-band image of coefficients.
var harmonicTrendCoefficients = harmonicTrend.select('coefficients')
.arrayProject([0])
.arrayFlatten([harmonicIndependents]);

// Compute fitted values.
var fittedHarmonic = harmonicLandsat.map(function(image) {
return image.addBands(
image.select(harmonicIndependents)
.multiply(harmonicTrendCoefficients)
.reduce('sum')
.rename('fitted'));
});
print(fittedHarmonic, 'fittedHarmonic');

// Plot the fitted model and the original data at the centerPoint.
print(ui.Chart.image.series(
fittedHarmonic.select(['fitted', 'NDVI']), centerPoint, ee.Reducer
.mean(), 30)
.setSeriesNames(['NDVI', 'fitted'])
.setOptions({
title: 'Harmonic model: original and fitted values: centerPoint',
lineWidth: 1,
pointSize: 3,
}));

// Plot the fitted model and the original data at the pecans.

print(ui.Chart.image.series(
fittedHarmonic.select(['fitted', 'NDVI']), crop1, ee.Reducer
.mean(), 30)
.setSeriesNames(['NDVI', 'fitted'])
.setOptions({
title: 'Harmonic model: original and fitted values: Crop1',
lineWidth: 1,
pointSize: 3,
}));

// Plot the fitted model and the original data at the crop.

print(ui.Chart.image.series(
fittedHarmonic.select(['fitted', 'NDVI']), crop2, ee.Reducer
.mean(), 30)
.setSeriesNames(['NDVI', 'fitted'])
.setOptions({
title: 'Harmonic model: original and fitted values: Crop2',
lineWidth: 1,
pointSize: 3,
}));

// Compute phase and amplitude.
var phase = harmonicTrendCoefficients.select('sin')
.atan2(harmonicTrendCoefficients.select('cos'))
// Scale to [0, 1] from radians.
.unitScale(-Math.PI, Math.PI);

print(phase, 'phase');

var amplitude = harmonicTrendCoefficients.select('sin')
.hypot(harmonicTrendCoefficients.select('cos'))
// Add a scale factor for visualization.
.multiply(5);
print(amplitude, 'amplitude');

// Compute the mean NDVI.
var meanNdvi = landsatCombined.select('NDVI').mean();
print(meanNdvi, 'meanNdvi');

// Use the HSV to RGB transformation to display phase and amplitude.
var rgb = ee.Image.cat([
phase, // hue
amplitude, // saturation (difference from white)
meanNdvi // value (difference from black)
]).hsvToRgb();

Map.addLayer(rgb, {}, 'phase (hue), amplitude (sat), ndvi (val)');

var cdl = ee.Image('USDA/NASS/CDL/2023').select(['cropland']);
Map.addLayer(cdl.clip(roi), {}, 'CDL 2023');

// save phase, amplitude, meanNDVI, and cdl as rasters to be used in R for classification
// exports the rasters to GeoTIFFs, which will be saved in the specified folder in your Google Drive

Export.image.toDrive({
image: phase,
description: 'phase',
folder: 'RS_Lab12', // Folder you create in your Google Drive where file will write
fileNamePrefix: 'phase',
scale: 30, // Adjust the scale as needed. Resolution in meters
region: roi, // Define the region of export, here we use the image's geometry
crs: 'EPSG:4326', // Define the coordinate reference system
maxPixels: 1e13 // Adjust max pixels if needed. In this lab, your area should be small, so this won't be an issue most likely
});

Export.image.toDrive({
image: amplitude,
description: 'amplitude',
folder: 'RS_Lab12', // Folder you create in your Google Drive where file will write
fileNamePrefix: 'amplitude',
scale: 30, // Adjust the scale as needed. Resolution in meters
region: roi, // Define the region of export, here we use the image's geometry
crs: 'EPSG:4326', // Define the coordinate reference system
maxPixels: 1e13 // Adjust max pixels if needed. In this lab, your area should be small, so this won't be an issue most likely
});

Export.image.toDrive({
image: meanNdvi,
description: 'meanNdvi',
folder: 'RS_Lab12', // Folder you create in your Google Drive where file will write
fileNamePrefix: 'meanNdvi',
scale: 30, // Adjust the scale as needed. Resolution in meters
region: roi, // Define the region of export, here we use the image's geometry
crs: 'EPSG:4326', // Define the coordinate reference system
maxPixels: 1e13 // Adjust max pixels if needed. In this lab, your area should be small, so this won't be an issue most likely
});

Export.image.toDrive({
image: cdl,
description: 'cdl',
folder: 'RS_Lab12', // Folder you create in your Google Drive where file will write
fileNamePrefix: 'cdl',
scale: 30, // Adjust the scale as needed. Resolution in meters
region: roi, // Define the region of export, here we use the image's geometry
crs: 'EPSG:4326', // Define the coordinate reference system
maxPixels: 1e13 // Adjust max pixels if needed. In this lab, your area should be small, so this won't be an issue most likely
});

// After running the export.image functions above, 4 Tasks will appear in the upper right corner
// You then need to click Run on each (and then Run again on the popup) to make them upload to your Google Drive