An "index" for satellite data is a combination of 2 or more bands from a dataset that The Calculate Index block allows you to select from pre-built band combinations that form the most commonly used indices.  

Block Guide: Calculating an Index

TABLE OF CONTENTS

• Block Guide: Calculating an Index
  • Add Calculate Index block to your workflow
  • Indices for Optical Data
  • Indices for Radar Data
  • Placement of Indices in your workflow

Add Calculate Index block to your workflow

• This block provides a list of pre-defined indices for use on your data.
• This is a shortcut which means you don't have to calculate the indices explicitly in the Calculator block.  (If you need an index not listed, use the Calculator block instead).
• When the dataset you are using in your workflow has standard indices, then the block will give you the choice of a category, and then an index.
• The options dynamically change depending on which dataset you are using. For example, if you are using Sentinel 1 radar data in your workflow it will provide radar indices and not optical indices such as NDVI or EVI.
• When it is placed within a workflow where an index calculation is not appropriate, a red triangle will appear on the block to let you know. 

All our indices are listed in the "Awesome Spectral Indices for Google
Earth Engine" resource.

Indices for Optical Data

• For optical data, the categories, and relevant indices are:
  • Water:
    • NDWI.  The Normalised Difference Water Index makes use of reflected near-infrared radiation and visible green light to enhance the presence of water features while reducing the impact of soil and vegetation.
    • MNDWI. Modified Normalised Difference Water Index is more suitable for areas that are dominated by human impact as it is more effective than NDWI at separating water from built-up areas.
    • LSWI. Land Surface Water Index is the normalized difference between the near IR and the short wave IR and is linked to water content in vegetation and soils.
    • WRI. The Water Ratio Index is used for identifying open water bodies or vegetation containing moisture. 
  • Urban:
    • NDBI. The Normalised Difference Built-up Index. It takes advantage of the spectral response of built-up areas and other land cover. 
    • IBI. Index-Based Built-Up Index is a combination of three thematic indices: the soil-adjusted vegetation index (SAVI), the modified normalized difference water index (MNDWI) and the normalized difference built‐up index (NDBI).
      
  • Snow:
    • NDSI.  The Normalised Difference Snow Index. It picks out high visible reflectance (from the green band) with low short-wave IR (SWIR) reflectance, which is characteristic of snow (rather than clouds, which are high in both).
    • NDSII. Normalised Difference Snow Ice Index. This uses a visible (green) and NIR band to create a normalised difference index that picks out both snow and ice. 
    • S3 Snow Index.  Some studies have shown this to have better accuracy than NDSI. 
  • Vegetation:
    • NDVI. Normalised Difference Vegetation Index. This is the classic index that is the standard default for any vegetation mapping.  
    • EVI. Enhanced Vegetation Index.   This includes the blue channel to improve upon the NDVI.  (The values used for the constants are the most commonly used: g=2.5, L=1.0, C1=6.0, C2=7.5.) 
    • SAVI. Soil Adjusted Vegetation Index. This includes a factor to account for background soil contributions (and so is helpful for agricultural studies).  (L=1.0).
    • SR. This is a Simple Ratio of NIR/R.
  • Burn:
    • NBR. The Normalised Burn Ratio is a normalised difference between NIR and short-wave IR (SWIR).
    • NBR2. The Normalised Burn Ratio 2 modifies the NBR to highlight water sensitivity in vegetation and may be useful in post-fire recovery studies.
    • CSI. Char Soil Index. A simple index specifically aimed at detecting post-fire effects. 
    • BAI. This is a Burned Area Index that uses NIR and short-wave IR (SWIR).

Indices for Radar Data

• For radar data, there is only one category, radar, which offers the following indices:
  • DpRVIVV. The Dual-Polarised Radar Vegetation VV Index is a normalised index that can be used with Sentinel 1 to indicate the presence of vegetation.
  • DpRVIHH. The Dual-Polarised Radar Vegetation HH Index is a normalised index that can be used with PALSAR and Radarsat to indicate the presence of vegetation.
  • VH-VV Ratio. The simple VV/VH ratio (in linear units) is effective at highlighting the location of vegetation in Sentinel 1 data.  
  • HV-HH Ratio. The simple HV-HH Ratio (in linear units) is effective at highlighting the location of vegetation in PALSAR and Radarsat data.  

Placement of Indices in your workflow

You should think carefully about how the best way to use your indices and whether it comes before or after the Aggregate Images in Time block.

Since creating a composite aggregates data using some kind of an average it is sometimes better to generate the index for each image, and then average across each pixel.  This can be achieved by adding the index block before the aggregation.

We would recommend computing your index before aggregating where possible.

Other times you might want to average the pixels in the composite first and then use the index. This can be achieved by adding the index block after the aggregation block. Doing the index after aggregating will also reduce the processing if you are looking at very large areas or long time periods.

Typically, if you expect your target object or land surface to remain constant, then doing the index afterwards is OK.

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