integrating landsat, palsar‐2 and sentinel‐1 data with ... · rice management & indc...

INTERNATIONAL MEETING ON LAND USE AND EMISSIONS IN SOUTH/SOUTHEAST ASIA ‐ HO CHI MINH CITY, VIETNAM, OCTOBER 17‐19TH, 2016

Integrating Landsat, PALSAR‐2 and Sentinel‐1 Data with DNDC for Regional Rice GHG Inventories

Will iam A. Salas*, Nathan Torbick, Pete Ingraham and Diya ChowdhuryAppl ied GeoSolut ions, LLC

October 17 , 2016

Internat iona l Meet ing on Land Use and Emiss ions in South/Southeast As ia ‐

Ho Ch i Minh C i ty, V ietnam, October 17‐19th , 2016

*[email protected]


Rice Management & INDC TargetsRice produces significant methane (Global annual emissions 30‐100 Tg (10‐20% anthropogenic)).

Increased focus Short‐lived climate pollutants (methane , black carbon and F‐gases)

20‐yr GWP = 86 (100‐yr =34) [AR5]

COP 21: 48 countries mention rice methane mitigation as part of INDC targets

Alternating Wetting and Drying (AWD)Reduces MethaneReduces water requirements: adaptation strategy as well


RiceDSS VisionBuild a rice monitoring system to track and monitor rice production (extent and yields), drought risk and GHGs.

Multi‐EO Platforms: Integrate moderate resolution optical and radar data for mapping rice extent, crop intensity, inundation dynamics and crop development stages.

EO data assimilations to drive rice growth and soil biogeochemical model (DNDC) for quantification of GHG emissions and rice yields.

Deliver open source system for national and jurisdictional rice GHG assessment to support INDCs, carbon markets (offsets and “insetting”)


Presentation OutlineMapping rice at regional to national scales with multi‐sensor EO data

DNDC Modeling rice yields and GHG emissions

Example results for Red River DeltaNext Steps…


Mapping rice at regional to national scales with multi‐sensor EO data

Multi‐temporal remote sensing key for rice monitoringMapping Rice with Timeseries Optical and SAR

Rice Mapping and Modeling System

A.) Map of LULC in West Java generated from multitemporal FBS/D PALSAR (n=48) B.) Rice extent across Java (scaled to percent of 10km cell for visualization) derived from Mosaics and ScanSARC.) Rice extent across Indochina (% 10km cell for visualization) derived from Mosaics and ScanSAR

PALSAR Mapping Rice LCLUC in Monsoon AsiaNASA: Oklahoma University, AGS


C

PALSAR ScanSAR Results:Mixed results vs census.

PALSAR temporal resolution (46 day repeat) insufficient for mapping rice details

Built mapping framework…… on to Sentinel‐1 !!!

Presenter

Presentation Notes

A. Map of Indochina displays the percentage of the landscape occupied by PALSAR mapped rice at the subnational scale. The major growing areas are well captured by the multitemporal FBD 50m Mosaic classification as apparent across the Mekong and Red River Deltas, Chao Phraya and I-San Plateau, and coastal regions showing dense rice cultivation B. Scatterplot for Vietnam (left) showed relatively strong agreement with Total Rice (R2=0.89) vs multitemporal FBD 50m Mosaics. C. A scatter plot between subnational Thailand rice statistics for Major rice vs. PALSAR rice had a significant Pearson correlation coefficient of 0.89 (t=19.411, df=64, p-value=<0.0001, 95% CI=0.78-0.91, linear line for Major rice=blue, linear line for Total rice=red). Ubon Ratchathani (red triangle) and Kanchanaburi (green triangle) Provinces are the two noteworthy outliers. Should have some discussion of this regional map as it shows results for more than just Thailand. Data illustrated in the scatter plot. D. A scatter plot between subnational rice statistics for planted rice area in hectares (or harvested area if planted not available) for circa 2009 vs. Mosaic Agricultural Classes which had a significant Pearson correlation coefficient of 0.85 (t=20.21, df=167, p-value=<0.0001, 95% CI=0.79-0.88). The map shows the quantile distribution for the percentage of the landscape occupied by rice agriculture at the subnational scale. The major growing areas are captured by the agricultural classes which had crop fields that were flooded during the Mosaic overpass “wet” cycle.

Preliminary Results with Sentinel‐1

A.) Wall‐to‐wall Sentinel‐1 (~650 IW mode scenes) with example B.) sowing and C. peak for Irrawaddy Delta, Myanmar 2015.

Encouraging results, needs more QA/QC and validation


Modeling rice yields and GHG emissions


The DNDC ModelDNDC stands for DeNitrification‐DeComposition

DNDC is a soil biogeochemical model that has been used for quantifying GHG emissions from agricultural

DNDC is a process (as know as mechanistic) model that simulates the biogeochemical processes to drive C and N cycling in agricultural soils. DNDC can simultaneously simulate anaerobic (flooded) and aerobic (non‐flooded) conditions in soils.

DNDC can model both Methane and Nitrous Oxide emissions: critical for rice agro‐ecosystems.

DNDC has been extensively validated for rice globally.

CH4

CH4

CH4

CH4CH4 CH4

Water

Soil

CO2+4H2 → CH4 +2H2OCH3COOH+H2 → CH4 + CO2

Methanogenesis

CH4+2O2 → CO2+2H2OMethane oxidation

CH4

CH4

Rice: CH4 production and emission

Labile C →source

(REDOX < -100 to -200 mv)

CO2CH4 Ebulation

13Slide from Will Horwath

DNDC Models 3 Pathways Plant mediated transport Ebullition, diffusion Soil out gassing

DNDC Model Validation


Example results for regional GHG Red River Delta

Integrating Remote Sensing Observations with DNDC Modeling Framework for Regional GHG Emissions.


Red River Delta Multiscale Imagery

Presenter

Presentation Notes

Sentinel-1 VH/VV/VV for 20150304/20150304/20150608 Landsat 8 2014361 543 PALSAR-2

Collecting field training data for cal val, Ground Truth, surveys

Left) RRD land use land cover map (rice=yellow) generated from fused Sentinel‐1, PALSAR‐2, and Landsat‐8; Right) Scatterplot of rice extent and national census statistics show strong agreement.


Driving DNDC with Earth Observations for GHG Assessment

Crop calendar (1st crop planting DOY)

LULC / Rice Extent0 - 26

27 - 42

43 - 46

47 - 54

55 - 57

58 - 61

62 - 65

66 - 73

Used Sentinel‐1 to map rice extent, crop calendar, hydroperiod (duration of flooding) for Red River Delta

Sentinel‐1 Crop Calendar Products

Driving DNDC with Earth Observations for GHG Assessment

IntensityHydroperiod

Sentinel‐1 Crop Intensity and Hydroperiod

Crops

Soils

Weather

Tillage

IrrigationFlooding

ManureFertilizer

DNDC

DNDC Model Inputs

Presenter

Presentation Notes

Used surveys to collect fertilizer, residue and manure use information


Red River Delta 2015 Rice CH4 Emissions

CH4 CH4 GWP(GgCH4-C/y) (GgCO2/y)

DNDC 345 11,515

IPCCTier 1

mean 324 10,788 low 243 8,087 high 435 14,508

Presenter

Presentation Notes

IPCC tier 1 calculations: Flood duration based on remotely sensed plant dates, as-adjusted for DNDC simulations Assumed 105 days per crop – 21 day end-of-season dry phase = 85 days If season was < 105 days, set crop duration to plant date 2 – plant date 1 – 7 days Area based on 75% double rice; 18% season 1 only; 7% season 2 only Based straw application on 50% residue with typical yields Based manure application on 8 fresh tonnes of farmyard manure per hectare (Mai Van Trinh data)

DNDC vs IPCCDNDC MODELED METHANE EMISSION IPCC (BASED ON RS CROPPING/WATER MGMT)

Red areas: communes with higher DNDC modeled methane emissions.

Blue areas: communes with higher IPCC modeled methane emissions.

Process models captures differences in drivers.

Inform mitigation strategies and targeting.

DNDC – IPCC(HWD polys)


Next Steps: MRV Demonstration Thai Binh Province (with MARD IAE)GHG measurements for 2 rice growing seasons, 11 sites per season (from 8 districts), each of sites will have 3 treatments: 66 sets of treatment‐seasons of GHG (CH4 and N2O) measurements.

Collect rice crop development: 99 sites with rice crop development and water table depth (AWD) measured at least every 10 days for 2 cropping seasons.

Dense time series of SAR images: Sentinel‐1A,B (6 day repeat) and TerraSAR‐X (3 images per 11 day repeat) to map water management, crop growth and yields.

DNDC validation and uncertainty

Refined SAR processing algorithms for field level management mapping at site to provincial scale.


Continuous vs. AWD Flood Management

0

1

2

3

4

5

6

7

8

9

10

0 20 40 60 80 100Days After Initial Flood

Flo

od H

eigh

t (cm

)

2-wk flood holdingperiod

DryingCycle

1

DryingCycle

2

DryingCycle

3

Continuous Flood

Less-than-FullFlood

Cycle Time~ 5 to 8 d

Slide from Joe Massey (MSU)

Presenter

Presentation Notes

Uses significantly less water – less reliance on surface or ground water, adaptation strategy Reductions in methane are large – mitigation measure

Summary PALSAR‐2, Sentinel‐1, Landsat 8 fusion high LULC accuracyMultitemporal required for mapping rice attributes Suite of parameters: extent, hydroperiod, intensity, calendar

RRD GHG footprint characterized through integration of EO data and DNDC with uncertainty characterized

Integrated RiceDSS and DNDC model provides template scalable platform for rice GHG MRV for tracking INDC progress.

Next steps: Thai Binh study 2017 with MARD IAEMapping AWD with high temporal frequency SAR Validate rice MRV system for Red River Delta Expand collaborations, tech transfer RiceDSS platform/system

Thank [email protected]

Questions?

Special thanks to the NASA LCLUC, MuSLI and NASA SBIR programs for Funding the

development of the RiceDSS

mailto:[email protected]

integrating landsat, palsar‐2 and sentinel‐1 data with ... · rice management & indc...

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