Earth Engine is a powerful analytical tool, but you may have need to export the results
of your analysis in order to embed charts, images, maps, etc. into reports or
publications. In this section, you will learn how to create charts and images
that can be exported and viewed in other software. Recall that in the
[previous section](https://developers.google.com/earth-engine/tutorials/tutorial_api_06), you used code like the following to add an
NDVI band to every image in a collection, where the `l8` variable references the
[Landsat 8 TOA reflectance
collection](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC8_L1T_TOA):

### Code Editor (JavaScript)

```javascript
// Import the Landsat 8 TOA image collection.
var l8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA');

// Map a function over the Landsat 8 TOA collection to add an NDVI band.
var withNDVI = l8.map(function(image) {
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI');
  return image.addBands(ndvi);
});
```

## Charting

Suppose that what you want is actually a chart of NDVI over time at a given location. To
make such a chart, the first step is to choose a location of interest. Create a point
by getting the point drawing tool (![](https://developers.google.com/static/earth-engine/images/Playground_button_placemark.png))
and make a single point geometry in your area of interest. (If you already have imports,
hover on **Geometry Imports** and click **+ new layer** first).
Locate the point in an area of agriculture, deciduous forest, annual grassland, or some
other land cover with an annual cycle). Name the import `roi`. (See
[this page](https://developers.google.com/earth-engine/guides/geometries) for information about creating geometries
programmatically).

Now let's use the `roi` point to make a chart of NDVI over time in the pixel
under that point. The way to make charts in Earth Engine is with the `ui.Chart`
package. ([Learn more about making charts in Earth Engine](https://developers.google.com/earth-engine/guides/charts)).
Specifically, to make a chart over time, you can use the
`ui.Chart.image.series()` method:

### Code Editor (JavaScript)

```javascript
// Create a chart.
var chart = ui.Chart.image.series({
  imageCollection: withNDVI.select('NDVI'),
  region: roi,
  reducer: ee.Reducer.first(),
  scale: 30
}).setOptions({title: 'NDVI over time'});

// Display the chart in the console.
print(chart);
```

For the `roi` geometry, we chose a point in an agricultural area, resulting
in a chart that looks something like Figure 10. Note that the parameters to the chart
constructor include a reducer and scale like `reduceRegion()`. Since the
point we are providing as a region can only intersect one pixel, it suffices to use the
'first' reducer. If you have a larger region, you should use a 'mean' or other reducer
that specifies how to aggregate pixels. Also note that to visualize a chart, all you
have to do is print it.
![Tutorial_api_10_ndvi_chart.png](https://developers.google.com/static/earth-engine/images/Tutorial_api_10_ndvi_chart.png) Figure 10. Chart of Landsat NDVI over time at a point geometry.

> [!NOTE]
> **Note:** The upper right corner of the chart contains a little pop-out icon (). Click that icon to get a new tab with a version of the chart that can be downloaded in a variety of formats. See the `ui.Chart` section of the **Docs** tab for a complete list of the charting options in Earth Engine.

## Digression: Simple Cloud Masking for Landsat

Something you may have noticed about this chart is that the time series of NDVI values
at the point looks a little noisy. This is likely due to clouds. To ameliorate this
effect, Earth Engine includes a cloud-masking algorithm for Landsat sensors with a
thermal band: `ee.Algorithms.Landsat.simpleCloudScore()`. It takes a Landsat TOA
reflectance image as input and adds a band named `cloud` which is an index
of cloudiness in the pixel from 0 to 100, from least to most cloudy, respectively. By
modifying the function you mapped over the collection, you can use an arbitrary
threshold (20) on the cloud index to clean up the chart a bit:

### Code Editor (JavaScript)

```javascript
var cloudlessNDVI = l8.map(function(image) {
  // Get a cloud score in [0, 100].
  var cloud = ee.Algorithms.Landsat.simpleCloudScore(image).select('cloud');

  // Create a mask of cloudy pixels from an arbitrary threshold.
  var mask = cloud.lte(20);

  // Compute NDVI.
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI');

  // Return the masked image with an NDVI band.
  return image.addBands(ndvi).updateMask(mask);
});

print(ui.Chart.image.series({
  imageCollection: cloudlessNDVI.select('NDVI'),
  region: roi,
  reducer: ee.Reducer.first(),
  scale: 30
}).setOptions({title: 'Cloud-masked NDVI over time'}));
```

The cloud-masked result is illustrated in Figure 11. Note that the time series looks a
little smoother, but may still contain pixels affected by clouds. Adjust the threshold
on the cloud index and observe the charted time series to learn how this threshold can
affect your results.
![Tutorial_api_11_cloudless_chart.png](https://developers.google.com/static/earth-engine/images/Tutorial_api_11_cloudless_chart.png) Figure 11. Chart of cloud-masked NDVI over time at a point geometry.

## Exporting Images

You have seen a way to export a chart of data computed by Earth Engine, but what about a
whole image? Suppose, for example that you have built a greenest-pixel composite
[as discussed in the previous
section](https://developers.google.com/earth-engine/tutorials/tutorial_api_06#make-a-greenest-pixel-composite):

### Code Editor (JavaScript)

```javascript
var greenest = cloudlessNDVI.qualityMosaic('NDVI');
```

The only difference in this code from what you did previously is that now we're using
the cloud masked collection. You can export a subset (defined by a region) of this
using the `Export` package. ([Learn more about exporting
raster and vector data from Earth Engine](https://developers.google.com/earth-engine/guides/exporting).) For example, to export an image that can be
easily embedded in other docs, let's create a visualization image,
[as you've done previously](https://developers.google.com/earth-engine/tutorials/tutorial_api_05#mosaicking), an Export it to your
Google Drive folder:

### Code Editor (JavaScript)

```javascript
// Create a 3-band, 8-bit, color-IR composite to export.
var visualization = greenest.visualize({
  bands: ['B5', 'B4', 'B3'],
  max: 0.4
});

// Create a task that you can launch from the Tasks tab.
Export.image.toDrive({
  image: visualization,
  description: 'Greenest_pixel_composite',
  scale: 30
});
```

When you run this code, note that a new task is created in the **Tasks** tab.
To launch the export configuration dialog, click the **RUN** button in the
**Tasks** tab. Once you've configured the task, click the **Run**
button in the dialog to start the export. But before you do that, be warned:

> [!CAUTION]
> **Caution:** If you don't specify a region, the Map bounds at the time the script is run will be used!

The reason you should exercise caution when exporting without a `region`
argument is that if you set a relatively small value for `scale`, and the
Map is zoomed out to large area, then you will export a potentially *very* large
image to your Drive folder. See the `Export.image.toDrive()` docs in the
**Docs** tab for more details and a list of possible configuration parameters.

That concludes this introduction to the Earth Engine API! You have now seen much of
the most common Earth Engine functionality and should be able to confidently proceed to
more complex analyses. Be sure to [read the docs](https://developers.google.com/earth-engine/guides) and search the
[forum](https://groups.google.com/forum/#!forum/google-earth-engine-developers)
for answers to other questions. Happy coding!
[Previous page](https://developers.google.com/earth-engine/tutorials/tutorial_api_06)