DSD-INT 2017 Planetary-scale surface water detection from space - Donchyts

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Presentation by Gennadii Donchyts (Deltares) at the Symposium Earth Observation and Data Science, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.

Software

Planetary-scale surface water detection
from space
Gennadii Donchyts

Donchyts et. al. 2016, http://nature.com/nclimate/journal/v6/n9/full/nclimate3111.html
http://aqua-monitor.appspot.com

New Land
New Water
Donchyts et. al. 2016, http://nature.com/nclimate/journal/v6/n9/full/nclimate3111.html
http://aqua-monitor.appspot.com

cloudsshadows
TOAreflectance,%
Percentiles, %
clear pixels
10% 62%

http://aqua-monitor.appspot.com
How did the Earth’s surface water
change over the past 30 years?

http://aqua-monitor.appspot.com
Aqua Monitor

Earth Engine
Storage and Compute
Landsat Satellite Data
Aqua Monitor: NASA Landsat and Google Earth Engine

1. Annual surface reflectance percentile composites
2. Supervised classification (SVM)

Global Tide and Surge Model (GTSM) tidal range

Water occurrence (2013-2017, min/max suppression)

Reservoirs, ~150 000 mapped
http://bit.ly/global_reservoirs_map_2016
GRanD ~= 6000
N(t), A(t), V(t)

0
500000
1000000
1500000
2000000
2500000
3000000
3500000
11/14/1984 5/7/1990 10/28/1995 4/19/2001 10/10/2006 4/1/2012 9/22/2017
Surface water area estimated from satellite and from local water level station
* after outlier removal
R² = 0.9903
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
0 2000000 4000000
A* (A)
RMSE < 0.059 km2
A = 0.5 – 3 km2

t, x
λ
Sᵠ Sᶿ
Zs
Z
L
C
Cs
class
Y
ᵠ
z
clouds
land use classes
cloud shadows
azimuth
elevation
topography
hill shadows
Bayesian Network for Multispectral Optical Data

P(water | x, y, cloud=0)
2. Estimate probability density (prior)1. Detect water for cloud-free images
3. Estimate surface area using generative-discriminative algorithm
0. Input images from multiple sensors
~1200 images, <30m
𝑃 𝑊 𝑊 𝑡
=
𝑃 𝑊 𝑡
𝑊 ∙ 𝑃 𝑊
𝑃 𝑊 𝑡

Number of Landsat images measured annually
1 50

Trends and challenges
Resolutions
Temporal: months to seconds
Spatial: 1km to 1m
Spectral: 3-15 bands
Radiometric: 8  16bit
Single sensor  Multisensor
Coverage
Local  Global
Volumes
100 MB  10 000 000 000 MB
Availability
~20PB of free satellite images

How to estimate beach slopes in the absence of in situ measurements? Here are my slides from a recent presentation at the Coast2Coast webinar (organised by @Giovanni Coco, @Kristen Splinter, @Mitchell Harley) on a new technique to estimate beach slopes using satellite-derived shorelines and a global tide model. Beach slope data available at http://coastsat.wrl.unsw.edu.au/ and preprint at https://www.essoar.org/doi/10.1002/essoar.10502903.1.

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Describe and explain satellite remote sensing mission for monitoring.pdf

alshaikhkhanzariarts

Describe and explain satellite remote sensing mission for monitoring water, carbon and global climate change. Solution In recent years, the subjects of water, carbon, and global climate change have attracted worldwide attention by scientists and the media. Climate change, whether associated with human- induced or natural variations, has and will continue to be important to policy makers and the public. It is clear from reports such as that by the Intergovernmental Panelon Climate Change (IPCC) [1] that Earth observations play a critical role in providing information for assessment and modeling. Improving these observations, better quality and newvariables, is a goal of most national and intergovernmental space agencies. Major initiatives are under waythat will result in benefits to a broad range of our global society. In the United States, a decadal study [2] was recently completed by the Committee on Earth Science and Applications of the US National Research Council. The committee called for a commitment from the U.S. administration to Earth observations to secure benefits for mankind. The report gives both direction and a large boost to U.S. satellite programs as it recommended NOAA to restore key observational capabilities of satellite missions and also that NASA and NOAA launch 17 new satellite missions in the next 10 years. The study also adds an additional focus to these missions: societal benefits. Other countries have also been expanding their Earth observation programs with numerous advanced concept satellite missions. Of particular relevance to this issue are articles describing the Earth observing programs of the European Space Agency (ESA), the Japanese Aerospace Exploration Agency (JAXA), the China National Space Administration (CNSA), the Canadian Space Agency (CSA), and the National Space Program Office (NSPO) of Taiwan. The ESA has a long history of Earth observation from space that began with meteorological missions and has included a series of increasingly sophisticated radars that have provided valuable data about climate and the changing environment. The ESA’s current and future Earth observing is under its Living Planet Programme and includes the Earth Explorer, meteorological, and Sentinel missions. JAXA has supported a wide range of satellite-based instruments and platforms that have and will provide global Earth observations for water, carbon, and climate. Of particular note for the future is the commitment to the Global Change Observation Mission (GCOM) that will launch a series of two types of satellites (water and climate) to provide consistent and continuous observations of key variables over a 15-year period. In addition to the United States, ESA, and JAXA programs, there are strong satellite-based Earth observing programs in Canada, China, Argentina, Taiwan, and Brazil. In this Special Issue some of the most significant recent and future Earth observing satellites planned to monitor water, carbon and global clima.

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