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

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

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

Who we are 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 Landsat weekly 30m/pixel Planet RapidEye monthly 5m/pixel Sentinel-2 weekly 10m/pixel Sentinel-1 weekly 20m/pixel

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

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!

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

SAR overview What is synthetic aperture radar (SAR) image: Sentinel 1 composite from maps.descarteslabs.com

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!

SAR overview Mosaic of Landsat 8 imagery over Borneo: December 2016 Mosaic of Sentinel 1A imagery over Borneo: December 2016

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

Case study: Vietnamese Mekong River Delta image: Landsat 8 composite at maps.descarteslabs.com

Vietnam is one of world s largest exporters of rice

Vietnam is one of world s largest exporters of rice and nearly all of it is grown in the densely populated Mekong River Delta.

Why should we care about rice? o o o o Over 20% of the global calorie supply (Dawe et al. 2010) Staple grain for over 900 million people who live on less than $1.25 per day (Dawe et al. 2010) Declining yields are correlated with rising nighttime temperatures (Peng et al. 2004) Volatile! 80% of trade is controlled by 5 countries

Rice phenology

Proof of concept: image thresholding Minimum VV backscatter: 2015 growing season threshold the lowest 20% of values since rice paddies are flooded prior to planting

Proof of concept: image thresholding Minimum VV backscatter: 2015 growing season Mean VV backscatter: 2015 growing season 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

Proof of concept: image thresholding Minimum VV backscatter: 2015 growing season Mean VV backscatter: 2015 growing season Estimated extent of rice paddy for Can Tho Province 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 93.3% overall accuracy when compared to 150 random points

Next steps: classification with machine-learning

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

Generate temporal statistics for the 2015 growing season for VV and VH backscatter 0 128 255

Create and label a random sample of points o o Label with high resolution Google Earth imagery 129 non-rice points & 133 rice points

Create and label a random sample of points o o Label with high resolution Google Earth imagery 129 non-rice points & 133 rice points Split into testing & training data o 70% to training; 30% for testing

Extract feature data from image statistics for each point to build and train a random forest classifier o o o Tune the parameters Apply to test data Apply to full image set

Extract feature data from image statistics for each point to build and train a random forest classifier o o o Tune the parameters Apply to test data Apply to full image set Classification Rice Not rice Total Producer s accuracy Rice 132 1 133 99.2% Not rice 2 127 129 98.4% Truth Total 134 128 262 User s accuracy 98.5% 99.2% 98.9%

Classifying the number of rice harvests per growing season

Season winter - spring summer - autumn autumn - winter Planting date mid-october mid-march mid-july

Ideal signature (over a single year) Single-cropped rice Double-cropped rice Triple-cropped rice

Ideal signature (over a single year) Single-cropped rice Actual Landsat EVI signature (over a three year period) Double-cropped rice Triple-cropped rice

2015 winter-spring rice paddy extent 2015 summer-autumn rice paddy extent 2015 autumn-winter rice paddy extent 414,000+ hectares 453,600+ hectares 188,000+ hectares

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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