Mapping Paddy Rice in Asia

> ISRSE 37 > Kersten Clauss > 2017-05-081

Mapping Paddy Rice in Asiaa multi-sensor, time-series approach

Kersten Clauss1, Marco Ottinger1, Wolfgang Wagner2, Claudia Kuenzer3

1 Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg2 Department of Geodesy and Geoinformation, Vienna University of Technology3 German Remote Sensing Data Center (DFD), Earth Observation Center (EOC),

German Aerospace Center (DLR)

[email protected]

Motivation

• Food Security

• >40% of calorie intake in South Asian

countries

• single most important food crop in

Asia

• stable demand as food crop even with

dietary change

• Trade

• production volume, market price, food

security interact

• rice is a globally traded commodity

• Livelihoods

• ~75% of the worlds farms are in Asia,

of which 80% are smaller than 2 ha

• high environmental risk (drought,

flood, salinization)


Global Rice Science

Partnership 2013

Rice Production 2014


0 >208 mio. tonnes

rice productionFAOSTAT

Motivation

• Food Security


countries


Asia


dietary change

• Trade


security interact


• Livelihoods






AMIS

FAOSTAT

Motivation

• Food Security


countries


Asia


dietary change

• Trade


security interact


• Livelihoods






DeltAdapt project

Methodology

• combine results from time-series

of different sensors to reduce

data size

• ability to transfer to different rice

regions

• MODIS:

• high temporal resolution

• global coverage

• moderate data size

• free, open access

• Sentinel-1:

• high spatial resolution

• unaffected by cloud cover

• sensitive to surface water

• free, open access


Rice Area Detection from Time-Series

• rice fields are commonly

flooded prior to

transplanting/seeding

• water level is maintained

throughout the growing cycle

• emergence, horizontal and

vertical growth influences

spectral and microwave

response

• flooding and phenological

growing stages of rice create

a distinct temporal footprint

time-series data




flooded prior to







response




time-series data


Clauss, Yan, Kuenzer (2016)

doi:10.3390/rs8050434

Rice Production in China


Feature Calculation


LSWI 75th percentile

EVI - LSWI inversions

EVI 90th percentile

Feature Calculation


LSWI 75th percentile

EVI - LSWI inversions

EVI 90th percentile

Feature Bands

10th percentile EVI, LSWI, Blue, Red, NIR, MIR





amplitude NDVI, EVI, LSWI, Blue, Red, NIR, MIR

75th - 25th percentile EVI, LSWI, Blue, Red, NIR, MIR

90th - 10th percentile EVI, LSWI, Blue, Red, NIR, MIR

local maxima > 0.8 EVI, LSWI



local minima < 0.3 EVI, LSWI



EVI LSWI inversions EVI, LSWI

Rice Area in China derived from MODIS with OCSVM


2002

2005

2010

2014


doi:10.3390/rs8050434

2014

2010

2005

Rice Area Change - Beimin Lake


doi:10.3390/rs8050434

2002


Rice Area Change - Heilongjiang

2014

2010

2005

2002



doi:10.3390/rs8050434

Accuracy Assessment


classified area compared to statistical yearbook data

Overall

Accuracy

User‘s

Accuracy

Producer‘s

Accuracy

rice no rice rice no rice

0.90 0.90 0.89 0.89 0.79


doi:10.3390/rs8050434

Sentinel-1 Coverage 2015

Clauss, Ottinger, Kuenzer (2017)

in review

Sentinel-1A IW DV coverage 2015


Sentinel-1 Study Sites


in review


A

B

CFED


Sentinel-1 Study Sites


accepted with revisions


A

B

CFED

Giao Thuy, Vietnam Soc Trang, Vietnam Poyang Lake, China

Sacramento, USA Ebro Delta, Spain Isla Mayor, Spain



in review

Methodology



in review

Pre-Processing



in review

Segmentation



accepted with revisions

Time Series per Object


from time series per pixel to

time series per object

Classification


Giao Thuy, Vietnam Soc Trang, Vietnam

Classified Rice Areas


in review


Poyang Lake, China Sacramento, USA



in review


Ebro Delta, Spain Isla Mayor, Spain



in review


validation points, Soc Trang study site

Study Site Overall

Accuracy

Producer’s

Accuracy

User’s

Accuracy

rice no rice rice no rice

Giao Thuy 0.87 0.88 0.85 0.85 0.88

Soc Trang 0.85 0.82 0.88 0.89 0.81

Poyang Lake 0.81 0.78 0.85 0.87 0.75

California 0.78 0.73 0.86 0.89 0.67

Ebro Delta 0.87 0.90 0.83 0.82 0.91

Isla Mayor 0.82 0.78 0.88 0.90 0.74


in review

Accuracy Assessment


Multi-Sensor Methodology


Moderate Resolution Rice Area (MODIS)

Mekong Delta,

Vietnam

rice 2015


High Resolution Rice Area and Seasonality (Sentinel-1)

Mekong Delta,

Vietnam


• large area rice mapping is possible with remote sensing time-series

• frequent cloud cover in rice growing regions requires high revisit time of multi-

spectral sensors

• SAR time-series enable high resolution rice mapping in cloud prone regions

• require frequent coverage over large areas

• seasonality extraction depends on temporal density of time-series

• combined approach reduces data and can aid towards current, high resolution

rice area mapping at large scale

• large scale mapping is limited by calibration/validation, not EO data

Conclusions and Outlook


Mapping Paddy Rice in Asia

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