Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics

Caitlin Kontgis caitlin@descarteslabs.com @caitlinkontgis

Descartes Labs

What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

Overview

What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

Overview

Who are we? o New Mexico-based startup spun out of Los Alamos

National Lab in December 2014

o Machine learning, computer vision, satellite imagery

o Team of 30+ physicists, philosophers, mathematicians, software engineers, and geographers

o Acquire, process, and store imagery (NASA, ESA, Planet)

o Building a living atlas of the world: persistent, real-time, multi-modal

o First application: global, real-time forecasts of commodity agriculture

Who we are

MODIS daily 250m/pixel

Planet RapidEye monthly 5m/pixel

Landsat weekly 30m/pixel

Sentinel-2 weekly 10m/pixel

Sentinel-1 weekly 20m/pixel

Who we are

MODIS daily 250m/pixel

Planet RapidEye monthly 5m/pixel

Landsat weekly 30m/pixel

Sentinel-2 weekly 10m/pixel

Who we are

Sentinel-1 weekly 20m/pixel

What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

Overview

Who am I?

Who am I? o PhD in Geography from University of Wisconsin at

Madison in 2016

o Used Landsat to study land cover and land use changes in southern Vietnam & CERES-Rice to investigate possible impacts of climate change to rice

o Spent every February/March in the Mekong River Delta during graduate school collecting ground-truth data and conducting farmer interviews

o Joined the engineering team at Descartes Labs in November of 2015

o Big fan of SAR data!

What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

Overview

What is synthetic aperture radar (SAR) data?

image: Sentinel 1 composite from maps.descarteslabs.com

SAR overview

SAR overview

Sentinel 1 satellite o Launched by European Space Agency in

2014

o C band (3.9 – 7.5 cm)

o 20-meter spatial resolution

o VV, VH, HH, HV capabilities that vary by region and temporal cycle

o Free!

Mosaic of Landsat 8 imagery over Borneo: December 2016

Mosaic of Sentinel 1A imagery over Borneo: December 2016

SAR overview

What is Descartes Labs? Who am I? A (brief) overview of SAR data Case study: rice in Mekong River Delta

Overview

Application

image: Landsat 8 composite at maps.descarteslabs.com

Case study: Vietnamese

Mekong River Delta

Vietnam is one of world’s largest exporters of rice…

Application

Vietnam is one of world’s largest exporters of rice…

Application

…and nearly all of it is grown in the densely populated Mekong River Delta.

Why should we care about rice? o Over 20% of the global calorie supply (Dawe et al.

2010)

o Staple grain for over 900 million people who live on less than $1.25 per day (Dawe et al. 2010)

o Declining yields are correlated with rising nighttime temperatures (Peng et al. 2004)

o Volatile! 80% of trade is controlled by 5 countries

Application

Ric

e ph

enol

ogy

Application

Proof of concept: image thresholding

threshold the lowest 20% of values since rice paddies are flooded prior to

planting

Minimum VV backscatter: 2015 growing season

Application

Proof of concept: image thresholding

threshold the lowest 20% of values since rice paddies are flooded prior to

planting

threshold the highest 45% of values since as rice grows the backscatter will

increase

Minimum VV backscatter: 2015 growing season

Mean VV backscatter: 2015 growing season

Application

Proof of concept: image thresholding

threshold the lowest 20% of values since rice paddies are flooded prior to

planting

threshold the highest 45% of values since as rice grows the backscatter will

increase

Minimum VV backscatter: 2015 growing season

Mean VV backscatter: 2015 growing season

Estimated extent of rice paddy for Can Tho Province

93.3% overall accuracy when compared to 150 random points

Application

Next steps: classification with machine-learning

Application

Generate temporal statistics for the 2015 growing season for VV and VH backscatter

Application

Generate temporal statistics for the 2015 growing season for VV and VH backscatter

0 128 255

Application

Create and label a random sample of points

o Label with high resolution Google Earth imagery o 129 non-rice points & 133 rice points

Application

Create and label a random sample of points

o Label with high resolution Google Earth imagery o 129 non-rice points & 133 rice points

Split into testing & training data

o 70% to training; 30% for testing

Application

Extract feature data from image statistics for each point to build and train a random forest classifier

o Tune the parameters o Apply to test data o Apply to full image set

Application

Extract feature data from image statistics for each point to build and train a random forest classifier

o Tune the parameters o Apply to test data o Apply to full image set

Classification

Rice Not rice Total Producer’s accuracy

Trut

h

Rice 132 1 133 99.2%

Not rice 2 127 129 98.4%

Total 134 128 262

User’s accuracy 98.5% 99.2% 98.9%

Application

Classifying the number of rice harvests per growing season

Application

Season Planting date winter - spring mid-October

summer - autumn mid-March autumn - winter mid-July

Single-cropped rice

Double-cropped rice

Triple-cropped rice

Ideal signature (over a single year)

Application

Single-cropped rice

Double-cropped rice

Triple-cropped rice

Ideal signature (over a single year)

Actual Landsat EVI signature

(over a three year period)

Application

Application

2015 winter-spring rice paddy extent 2015 summer-autumn rice paddy extent 2015 autumn-winter rice paddy extent

188,000+ hectares 414,000+ hectares 453,600+ hectares

Application

Future work 1. Validate annual number of harvests estimates

2. Incorporate Sentinel-1B data to move toward

real-time monitoring of rice management

3. Field-level analysis • Use the Descartes Labs edge detection

algorithm, which uses dense time stacks of SAR data to identify boundaries, to classify land cover/use at the field scale

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