EO data for Rice monitoring in AsiaEO data for Rice monitoring in Asia

Thuy Le Toan

CESBIO, Toulouse, France

&

The Asia-RICE team

G20 GEOGLAM Goal:

To strengthen the international community’s capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales through reinforced use of EO

Information/ Products

For Asia-RICE Information and Product Types

Area estimate

Information/ Products EO Data Products

For Asia-RICE Information and Product Types

Area estimate• Cropland mask • Rice grown area

Information/ Products

Production estimate - Crop outlook / Early

warning/ Damage - Yield forecast

• Agricultural practices • Crop condition indicators• Biophysical variables • Environmental variables

(soil moisture)• Weather

EO Data Products

For Asia-RICE Information and Product Types

Area estimate• Cropland mask • Rice grown area

Earth Observation data for rice monitoring 2013-2014

Rice grown area estimates and mapping

Spatial Resolution Fine (1m-100m)

Moderate (250-1000m)

Coarse (4km-40km)

Spectral Domain VIS/IR MW (SAR)

VIS/IR VIS/IR MW

Rice grown area

Detailed base-map( e.g.every 3-5 year)

Landsat 8,

SPOT,ASTERTHEOS, IRS, FORMOSAT, VNREDSat1

-

-

-

Rice Cultivated Area (every rice season and yearly)

Landsat 8,

SPOT,ASTERTHEOS, IRS, FORMOSAT, VNREDSat1

Radarsat2,

RISAT-1, TerraSAR-X, Cosmo-Skymed ALOS-2* Sentinel-1*

MODIS

SPOT-VGT / PROBA-V

-

SPOT Vegetation Wheat Rice

Rice Rice

Monitoring at global scale: MODIS & SPOT VGT

Xiao et al, 2006

Flooding

Need to use LSWI (Land Surface Water Index or Normalised Difference Water Index) to discriminate rice fromother vegetation before using NDVI to monitor rice activity

LSWI=SWIR-NIR/ SWIR+NIR

NDVI=NIR-R/NIR+R

Monitoring at global scale: MODIS & SPOT VGT

Rice grown areas at national scale using MODIS. Comparison with NationalStatistics (Xiao et al., 2006)

Can we use MODIS for rice grown area estimate ?

Can we use MODIS for rice grown area estimate ?

Various results obtained. Better at global and multi-year average than at local-provincial scales. Sources of error are among others:

- resolution of MODIS vs small field size and non uniform rice crop calendar- confusion with other crop (specially id direct sowing) - cloud contamination..

Major advantages: data widey available and methods accessible by users

Can we use SAR data for rice grown area estimate?

Relevance of SAR data to monitor land surfaces in frequently cloud covered regions Studies show the relevance of C, L, X band data to map rice grown area Major shortcomings:

lack of systematic, widely available (and free of charge) data for operational use lack of simple and available methods accessible by users

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SAR data for rice monitoring 2013-2014

Mission Instrument Data adapted to rice monitoring

RADARSAT-2

(CSA/MDA)

C-band SAR

(5.405 GHz)

24 days repeat cycle

Wide Fine mode( 150 km, 20m)

F0W2, Incidence 31-39°

VV and VH

Sentinel-1 (2014)

(ESA)

C-BandSAR (5.405 GHz)

12 days

Stripmap mode, 80 km swath, 10 m resolution.

Incidence range 30-40°

VV and VH

RISAT-1

(ISRO)

C-Band SAR

(5.350 GHz)

MRS (115 km swath, 22-43 m)

Incidence 23-49°

HH and VV

TerraSAR-X

(DLR)

X-Band SAR

(9.65 GHz)

a) StripMap (SM), 15x50 km

1.2x 6.6 m resolution, HH&VV 20-40°

b) Tandem X: StripMap,

interferometric mode, alternating bistatic, HH,

COSMO SkyMed

(ASI)

X-Band SAR

(9.6 GHz)

StripMap pingpong (20m, 30 km swath)

HH and VV

Incidence >40°

ALOS-2 (2014)

(J AXA)

L-band PALSAR-2

(1,270 GHz)

StripMap (10m, 70 km) 14 days

HH and VV

Can we use SAR data for rice grown area estimate?

Relevance of SAR data to monitor land surfaces in frequently cloud covered regions Studies show the relevance of C, L, X band data to map rice grown area Major shortcomings:

lack of systematic, widely available (and free of charge) data for operational use lack of simple and available methods accessible by users

Phase 1A of Asia-RiCE will consist of four technical demonstration sites which will focus on developing provincial-level rice crop area estimations.

Phase 1B, and/or Phase 2, other technical demonstrators will be added, and/or the scope may be increased to produce whole country estimates.

Objectives of Technical Demonstration sites

VAST: Lam Dao Nguyen, Hoang Phi Phung

CESBIO: Thuy Le Toan, Alexandre Bouvet

Objective phase 1: – To develop area estimation using all available data in

2013-2014 (SAR and optical)– To compare the results and to define the data type

than can be used for the country estimates (for SAR: resolution, mode, frequency, polarisation, acquisition timing.., but also long term availability and cost)

South Vietnam demonstration site: An Giang province

VIETNAMMekong DeltaIn 1000 tons

Choice of the An Giang province:Increase in the third season rice (Autumn-Winter) made possible by construction of dykes to protect the fields from seasonal floods

17

Aug Sep Oct Nov Dec

CSK acquisitions

TDX acquisitions

RS acquisitionsIntensive measurements

Extensive measurements

Dates of satellite data acquisitions in Autumn-Winter 2013 crop over An Giang:

Cosmo-Skymed: 10 dates 19 August, 4 September, 20 September, 6 October, 14 October, 22 October, 30 October, 7 November, 15 November, 23 November Radarsat-2: 4 dates 30 August, 23 September, 17 October, 10 November TerraSAR-X: 3 dates 25 September, 17 October, 28 October 

2013

Scattering on leaves, ears

Attenuated ground scattering

Stem-ground interaction

For the diversity of SAR data, method development needs to be based on knowledge of scattering physics

18

The relative contributions of volume, surface andvolume-surface(interaction) scattering depend on rice growth stage, radar frequency, incidence angle and polarisation

cb

Rice backscatter model

Example at X-band

Le Toan et al, 1989

Examples of measurementsat X-band

Inoue et al., 2004

25° of incidence

55° of incidence

20/09/2013 R: HH G:HH/VV B: VV

CosmoSkymed data

Cosmo-Skymed HH 19 August 2013

Cosmo-Skymed HH 4 September 2013

Cosmo-Skymed HH 20September 2013

Use of backscatter temporal variation to distinguish rice

Polarization HH

COSMO-SKYMED data 19 August 2013

Use of polarization (HH and VV) to distinguish rice from other land use types

Polarization VV

Polarisation ratio 19 August 2013

Developed rice plants

HH/VV

Polarisation ratio 4 Sept ember 2013

Polarisation ratio 20 Sept ember 2013

VV 19 August, 4 Sept, 20 Sept

Details of rice fields structure

0 10 20 30 40 50 60-30

-25

-20

-15

-10

-5

0

Days after sowing

HH

Rice varieties- 50404 (circle) - OM4218 (square) - Jasmine (+) - 7347 (losange) unknown (x)

19 08 2013

04 09 201320 09 2013

06 10 2013

14 10 2013

Temporal variation of the backscatter

0 10 20 30 40 50 60-30

-25

-20

-15

-10

-5

0

Days after sowing

VV

19 08 2013

04 09 2013

20 09 2013

06 10 2013

14 10 2013

Temporal variation of the backscatter

0 10 20 30 40 50 60-10

-5

0

5

10

15

Days after sowing

ratio

19 08 2013

04 09 2013

20 09 2013

06 10 2013

14 10 2013

HH

/VV

Rat

io

Temporal variation of the backscatter

-25 -20 -15 -10-28

-26

-24

-22

-20

-18

-16

-14

-12

-10

-8

-6

HH (dB)

VV

(dB

)

19 08 2013

04 09 2013

20 09 2013

14 10 2013

06 10 2013

Temporal variation of the backscatter

-30 -25 -20 -15 -10 -5-30

-25

-20

-15

-10

-5

HH backscatter (dB)

VV

bac

ksca

tter

(dB

)

19/08/2013

04/09/2013

20/09/201306/10/2013

14/10/2013

30 cm10 cm

Angle : 50°-55°

70cm

tillering

2-3 leaves

30 cm

70 cm

stemextension

An Giang, Aug-Oct 2013CosmoSkymed, X band SAR

Camargue, 1988X band airborne SAR

Different experiments, same scattering physics

Autumn-Winter rice map as of October 14 2013

Châu Thành

Thoại Sơn

TP Long Xuyên

Chợ MớiChâu Phú

Use of robust indicators for rice mapping

36AW 2007 AW 2007 crop crop from ASAR APPfrom ASAR APP

AW 2010 AW 2010 crop crop from ASAR APPfrom ASAR APP

AW 2013 AW 2013 crop from crop from CSK PP (14/10/2013)CSK PP (14/10/2013)

Spatial Resolution Fine (1m-100m)

Moderate (250-1000m)

Coarse (4km-40km)

Satellite repeat cycle Weekly - Monthly Daily - Monthly

Hourly - Daily

Spectral Domain VIS/IR MW (SAR)

VIS/IR VIS/IR MW

Product description

Damage Assess-ment

Detection of flooding, drought and pests impacted area

Landsat 8,

SPOT, ASTER, THEOS, IRS, FORMOSAT, VNREDSAT-1

MODIS,

SPOT VGT / PROBA-V

-

Crop CalendarGrowth anomaly

Timing of rice phenological stages

Growth anomaly

-

TerraSAR-X, Cosmo-Skymed

Sentinel-1*

MODIS,

SPOT-VGT/

PROBA-V

-

Early Warning

Yield Estima-tion

Agro-meteorology anomaly(e.g.drought, extreme temperature)

-

-

MODIS

MTSAT,TRMM,

SMOS ;Megha Tropiques

Crop biophysical parameters (LAI, biomass..)

-

Radarsat-2, RISAT-1 TerraSAR-X, CosmoSkymedALOS-2*, Sentinel-1*

MODIS,

SPOT VGT

/ PROBA-V

-

1. Development rates: require weather and phenological observations: sowing date, emergence time, tillering, heading, flowering, maturity

2. Output of the model to be adjusted with measurements:-LAI, Biomass of stems, leaves, panicles

At least at 6 sampling times (provided if possible by EO)- Transplanting- Maximum tillering- Panicle initiation- Flowering- Grain filling- Maturity

Requirements for rice gowth model

24-31 Jul

1-7 Aug

8-14 Aug

15-21 Aug

22-28 Aug

29 Aug - 4 Sep

5-14 Sep

Estimated sowing date

Estimated sowing date from CSK SAR data

A SA

S

Sowing date

Est

imat

ed s

owin

g da

te

RMSE=4,1 days

Sowing date +/- 3 days

Assessment of sowing date estimate

Date from August to Sept 2013

SUMMARY For Rice monitoring in Asia, various EO data sources exist

Works are to be done to combine different data sourcesfor rice grown area esimates (low resolution optical, narrow/large swath SAR data, sampling strategies..)

For Rice yield estimates, research effort is still needed

There is a need to assess the methods not only at a singlesite, but across Asia

There is an action to be undertaken by Asia-RICE /GEOGLAM for future data acquisition for Rice (e.g.towards Sentinel-1 and ALOS-2)