Water Perspectives from Google Earth Engine

Tyler EricksonGoogle

Aspen-Nicholas Water ForumMay 29, 2015

source: http://www.lternet.edu/node/49495Photo: John Marr

Tyler EricksonDeveloper AdvocateEarth Engine ProjectGoogle

USGS Landsat Program● 30m resolution● ~10 spectral channels● ~500MB per scene● 45 seconds to acquire a scene● >40 years of observations

Satellite Remote Sensing Data Example

USGS Earth Resources Observation & Science (EROS) Center

https://ee-api.appspot.com/d1a642b652bd9c8fdc68f3d28f3c8cd7

http://earthenginepartners.appspot.com/science-2013-global-forest

European Commission Joint Research CentreGlobal Environmental Monitoring Unit

Global Surface Water Map

What is Earth Engine?

Google Earth Enginea cloud-basedgeospatial processing platform

Goals● Make substantive progress on global challenges

that involve large geospatial datasets.Approach● Build a geospatial analysis platform that allows

both highly-interactive algorithm development and global-scale analysis.

What is Earth Engine?

GeospatialDatasets

AlgorithmicPrimitives

add

focal_min

filter

reduce

join

distancemosaic

convolve

Results

Storage and Compute

Requests

a.k.a. Tyler's Provocative Statements

The Earth Engine View of the World

Too much data for a single machine.Big Data

Medium Data

Small Data An amount of data that humans can use to make a decision.

Fits on a single machine.

Google Works with Big Data

Value is in the Usage of Data

Disk CPU

Key lessons:● Bandwidth is (relatively) expensive,

so co-locate CPU and disk. Bring the algorithm to the data!

● Disk is cheap,so bring everything online.

● CPU is even cheaper,so don’t pre-process needlessly.

Bandwidth

Data Transfer is the Limiting Factor

> >

Exploration: only feasible if results are FAST

Federated Systems Look Goodin Presentations

(but that's about it)

USGS EROS

NASA/JPL PO.DAAC

NASA NEX

NASA/USGS LP DAAC

NASA LDAS

ORNL DAAC

NASA GES DISC

Earth Engine Public Data

Archive

NOAA NCEP

Evapotranspiration Modeling

Input datasets:▪ Satellite Imagery (Landsat)▪ Elevation data (National Elevation Dataset)▪ Land use (National Land Cover Dataset - NLCD)▪ Weather data (NLDAS / gridMET)

Christmas Valley, ORJuly 15th, 2014

source: Baburao Kamble, Ayse Kilic (UNL), Rick Allen (Univ. Idaho) & Justin Huntington (DRI)

Remote Sensing Data is Messy!

● clouds● haze● view angle● sun angle● properties change● sensor calibration ● lions, tigers, bears….

Our "Big Data" is just Sparse Sampling

● spatial resolution● spectral resolution● revisit frequency

You think this is the Big Data era?This is just the beginning...

Transparency Can Force Change