Many useful climate and weather datasets come as gridded rasters. The techniques for working with them is slightly different than other remote sensing datasets. In this post, I will show how to work with gridded rainfall data in Google Earth Engine. This post also serves an an example of how to use the map/reduce programming style to efficiently work with such large datasets.

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Color correction is an important process working with satellite and aerial imagery. A common technique used to balance the colors across multiple images is Histogram Matching. While the algorithm has been around for a long time, there aren’t many free and open-source tools that can used at scale. Mapbox has released an open-source tool called rio-hist that works well for small and medium sized images. Whitebox Tools has a Histogram Matching algorithm that can be used in QGIS via Whitebox Tools Processing Plugin. But when working with large mosaics, such as the ones used in this post – it runs out of memory or takes a very long time. Google Earth Engine is a good alternative to perform fast histogram matching across large images.

In this post, I will first give an overview of the histogram matching algorithm and then show you how it can be implemented in Earth Engine. The example images are large high resolution orthomosaics (3cm/pixel resolution) collected by UAV around Oakland, CA area.

Source Images: © Dan Koopman, Downloaded from OpenAerialMap
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When working on Remote Sensing applications, many operations require calculating area. For example, one needs to calculate area covered by each class after supervised classification or find out how much area within a region is affected after a disaster. Calculating area for rasters and vectors is a straightforward operation in most software packages, but it is done in a slightly different way in Google Earth Engine – which can be confusing to beginners. In this post I will outline methods of calculating areas for both vectors as well as images. We will cover the following topics, starting from simple to complex.

  • Area Calculation for Features i.e. vector data
  • Area Calculation for Images (Single Class)
  • Area Calculation for Images by Class
  • Area Calculation for Images by Class by Region
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Time series analysis is one of the most common operations in Remote Sensing. It helps understanding and modeling of seasonal patterns as well as monitoring of land cover changes. Earth Engine is uniquely suited to allow extraction of dense time series over long periods of time.

In this post, I will go through different methods and approaches for time series extraction. While there are plenty of examples available that show how to extract a time series for a single location – there are unique challenges that come up when you need a time series for many locations spanning a large area. I will explain those challenges and present code samples to solve them.

The ultimate goal for this exercise is to extract NDVI time series from Sentinel-2 data over 1 year for 100 farm locations spanning an entire state in India.

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Google Earth Engine (GEE) is a powerful cloud-based system for analysing massive amounts of remote sensing data. One area where Google Earth Engine shines is the ability to calculate time series of values extracted from a deep stack of imagery. While GEE is great at crunching numbers, it has limited cartographic capabilities. That’s where QGIS comes in. Using the Google Earth Engine Plugin for QGIS and Python, you can combine the computing power of GEE with the cartographic capabilities of QGIS. In this post, I will show how to write PyQGIS code to programmatically fetch time-series data, and render a map template to create an animated maps like below.

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