2019 update

Images and script updated for 2019

Suggested citations:
Script by Rutger Hofste, available at https://github.com/rutgerhofste/drought_app_v01
Data available from the U.S. Geological Survey.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.

Description:
As input data I've used satellite imagery form USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance. Using the latest available date (July 19th) and a lookback period of 90 days, I masked out clouds and shadows and create three images: 2019, 2018 and pre. The pre image is using data from 2014-2017.

In the post-processing step I clipped the data to Gelderland province and visualized bands 4,3,2 [0 - 0.3].

Earthengine script:
Y2019M08D02_RH_Gelderlander_v01 https://code.earthengine.google.com/6129f8f5241154a1054592b14737d20e https://github.com/rutgerhofste/drought_app_v01/blob/master/gelderlander_2019_v01.js

Simple Drought App

Visualize the effects of the historic 2018 drought in The Netherlands.

Dashboard
Satellite App
Results App

Methodology to create map

The app uses data from landsat-8. Landsat-8 has a revisit time of 16 days and like many staelilites in the visual spectrum cannot see through clouds. Processing of the data is as follows:

Step 1

Landsat-8 data is filtered by month (July)

Step 2

For the left panel data is filtered by years 2014-2017.
For the right panel data is filtered by years 2018.

Step 3

Clouds and shadows are removed using cloud mask and shadow mask (see script)

Step 4

Take the median for all remaining valid pixels.

The number of valid pixels varies per location but ranges from 0 to 6 for 2018 and 0 to 10 for 2014 - 2017. Invalid pixels have been masked out.

Methodology to calculate greenness per municipality

start with the same methodology as for map.

step 5

per pixel, calculate greenness as defined by greenness = G / (R+G+B) where R, G, B are the Red, Green and Blue bands of the landsat 8 surface refectance product.

step 7

calculate the difference in greenness between July 2018 and the median of 2014-2017. greenness_difference = (greenness_2018-greenness_2014_2017)/greenness_2014_2017

step 8

calculate the average per municipality.

Questions/Suggestions/Additions?
Fork repo on Github please.