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The Role Crop Modeling and Remote Sensing in Rice Productivity ImprovementInternational Rice Research Institute

Images Source: Setiyono 2015, Soc Trang DARD Seed Center, VietnamTri Setiyono

BackgroundClimate information, crop modeling, and remote sensing applicationsRemote sensing role drought detectionCrop modeling RoleRemote sensing role linked to crop modelingOutputs from the RIICE project (Remote Sensing-based Information and Insurance for Crops in Emerging Economies)Implementation potentialsOutline

Rice the staple food of most of the world's poor are vulnerable to climate shock events such as drought and flood Timely and accurate information on rice productivity On demand to support decision making for food security and economic developmentCan facilitate development of a cost-effective crop insurance system and effective linkage to social safety net and other risk transfer measure Putting farmers in a better position to adopt new technologiesWe can make use of current advances in climate information, crop simulation modeling, and remote sensing applications for developing crop monitoring system with the above interestsBackground

BackgroundChronology of climate information, crop modeling, and remote sensing applicationsRemote sensing role drought detectionCrop modeling roleRemote sensing role linked to crop modelingOutputs from the RIICE project (Remote Sensing-based Information and Insurance for Crops in Emerging Economies)Implementation potentialsOutline

AspectPre-SeasonIn-SeasonPost-SeasonClimate Information (CI)Long-term forecastSeasonal Weather ForecastShort-term forecast Real-time weather monitoringWeather monitoring and evaluationRemote Sensing (RS)Historical (baseline)Real-time crop monitoringReal-time crop monitoringCrop Simulation Model (CSM)Seasonal yield forecastIn-season yield forecastPost-season yield estimatesExample Applications Climate early warning (CI) Impact outlook (CI,CSM)Production monitoring (CI, CSM , RS)Impact confirmation (CI, CSM, RS)Pest and Disease Early Warning (CI,RS)Reducing yield gaps (CI, CSM, RS), Disaster impact assessment & intervention (CI, CSM, RS)

Chronology of climate information, crop simulation & remote sensing applications

BackgroundChronology of climate information, crop modeling, and remote sensing applicationsRemote sensing role drought detectionCrop modeling roleRemote sensing role linked to crop modelingOutputs from the RIICE project (Remote Sensing-based Information and Insurance for Crops in Emerging Economies)Implementation potentialsOutline

20032004

Map: MODIS LAI (USGS); Time series data: MODIS NDVI (USGS)Myanmar (2003, 2004*)

2013 SEP 06LandsatLook "Natural Color" Image (USGS)Cambodia

2014 SEP 18LandsatLook "Natural Color" Image (USGS)Cambodia*

BackgroundChronology of climate information, crop modeling, and remote sensing applicationsRemote sensing role drought detectionCrop modeling roleRemote sensing role linked to crop modelingOutputs from the RIICE project (Remote Sensing-based Information and Insurance for Crops in Emerging Economies)Implementation potentialsOutline

more variables to consider than the human mind can reasonably organized (Whisler et al 1999)CropYield

WeatherSoilPest & DiseaseManagementGenotype

Fertilizer NWaterMaturity DurationSolar, TemperatureRainfall, Wind SpeedHydraulic propertiesTexture, Soil N

Crop Simulation Model (CSM). e.g. ORYZA2000Crop Modeling Role

TPDFNRExplanatoryOryza2000TPD Transplanting datePPD Population densityFNR Fertilizer N rateIRA Irrigation amountCrop Modeling Role

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BackgroundChronology of climate information, crop modeling, and remote sensing applicationsRemote sensing role drought detectionCrop modeling roleRemote sensing role linked to crop modelingOutputs from the RIICE project (Remote Sensing-based Information and Insurance for Crops in Emerging Economies)Implementation potentialsOutline

Day of Year

180200220240260280LAI (m2 m-2)

12345

Yield (Mg ha-1)-

12345678

Days after establishment

-20-100102030405060708090

LAI: CSM

LAI :CSM + RS

LAI ; RS

Yield :CSM

Yield: CSM + RS

Yield :Obs.

Remote Sensing (RS) RoleCSM Crop Sim. Model; RS Remote Sensing; Obs. - Observed

15Scale: FieldRice Yield EstimationScale: SpatialRS

ORYZA2000 a computer model (software) for simulating rice growth and yield given crop characteristic, soil, and weather parameters.The domain of the model is potential yield, water-relevant, nitrogen-limited and interaction between water and nitrogen conditions.The model requires daily weather data consist of solar radiation, min and max temperature, reletive humidity, windspeed and precipitation.The native scale of the model is at field level. Integration of SAR technology with ORYZA2000 allows upscaling the yield results into a large spatial coverage (e.g. at sub national level).15

ORYZA2000

ORYZA2000+ RS

20-56%56%Clay Content

ORYZA2000+ RS

World Inventory of Soil Emission Potentials (WISE)Tropical Rainfall Measuring Mission (TRMM)HWSD (Harmonized World Soil Database)

Integrating GIS, RS, & CSM for Yield Monitoring System

Soil : WISE database, HWSD, local soil database

Station weather dataGridded weather data (NASA power, TRMM) Agronomic management

ORYZA2000

Remote sensing-basedInformation andInsurance for Crops in Emerging economies

BackgroundChronology of climate information, crop modeling, and remote sensing applicationsRemote sensing role drought detectionCrop modeling roleRemote sensing role linked to crop modelingOutputs from the RIICE project (Remote Sensing-based Information and Insurance for Crops in Emerging Economies)Implementation potentialsOutline

Remote sensingWeather, soil, etc.Field measurements

Remote SensingCrop modelingWebGISDatabasesExpert knowledgeTrainingRice areaPlanting datesYield & productionYield forecastsFlood & droughtYield gapsMinistry of agricultureStatistical bureausPolicy makersResearchersDisaster responseFinance/insurance

The RIICE project demonstrates that remote sensing and other technologies can provide accurate and timely information on rice.Remote Sensing based Information and Insurance for Crops in Emerging Economies

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RIICE demonstration sites in AsiaRemote Sensing based Information and Insurance for Crops in Emerging Economies

Nov-Apr (Wet Season)TransplantingIrrigatedCiherang , Inpari, Mekonga, Sintanur (115)Ketan, IR42 (135)Subang, Indonesia 2013/14 WS rice areaRIICE rice area (ha) Rice area accuracy 6453397%

Zooming in provides detail picture of the rice area23

At the highest resolution, start and peak of season can also be displayed. The different patches indicates when a particular area started the rice season24

Rice phenology or growth stages are also monitored through out the season. For a particular date we classify the rice stage.25

We also provide pop up summary information of a district. Ths includes the area of rice, the simulated yield, start of season and chart of yield distribution26

SitePeriodSeasonEstablishmentMaturity (days)Water sourceRice area (ha)AccuracyCambodia, TakeoOct to AprDry Direct seeding (DS)95Irrigated (IR)150,02685%Philippines, Leyte EastMay to SepDry Transplanting (TP)114IR17,81787%Philippines, Leyte WestMay to SepDry TP110-112IR15,22989%Philippines, A. del NorteMay to OctDry TP & DS107-123IR & some rainfed (RF)13,16389%Vietnam, Soc TrangJun to SepSummer- autumn TP & DS95-120IR55,21687%Vietnam, Nam DinhJul to NovSummer TP125-134IR108,73389%Indonesia, SubangNov to AprWet TP115-135IR64,53397%India, CuddaloreJul to JanSamba TP130-160IR26,01592%India, ThanjavurAug to DecSamba TP & DS135-160IR83,87191%India, SivagangaSep to JanSamba TP & DS100-110Semi-dry rice 41,825 87%Thailand, Muang YangMay to NovWetDS150-178RF91,90886%Thailand, Suphan BuriJun to OctWetDS92-120IR555,31787%Philippines, Nueva EcijaJul to NovWetTP114IR424,80186%DS 2WS - 11DS 7TP - 1092-178IR 12RF 31.65M ha

In 2013 all sites reached our 85% accuracy threshold, suggesting that we can map and monitor rice across different management, environments and varieties.

Monitoring System for Rice YieldORYZA2000 + RSwww.riice.org

*Attema & Ulaby (1978); #Shen et al., 2009Cloud* based semi-empirical model for converting radar backscatter into rice LAILAI (output) is Leaf area index (m2 m-2)s (input) is radar backscattering (dB), C-band or X-banda (parameter) is backscattering coefficient at full canopy closure (m2 m-2)b (parameter) is coefficient of attenuation per unit canopy water (m2 kg-1)sBG (parameter) is backscattering from canopy background (m2 m-2)q (parameter) is incident angle of radar beam ()A, B, and C (parameters) are non-linear reg. coefficients for LAI vs W.h#, where W is amount of canopy water (kg m-3) and h is canopy height (m) and A = 10.22468, B=0.3379559, and C=1.7230986

Baseline Values:LAI = 1.73; a = 0.34; b = 0.20; sBG = 0.045; q = 22a is backscattering coefficient at full canopy closure, m2 m-2b is coefficient of attenuation per unit canopy water, m2 kg-1sBG is backscattering from canopy background,m2 m-2q is incident angle of radar beam,

*Attema & Ulaby (1978); #Shen et al., 2009A, B, and C are non-linear reg. coefficients for LAI vs W.h, where W is amount of canopy water (kg m-3) and h is canopy height (m) A = 10.22468B=0.3379559C=1.7230986#Cloud* based semi-empirical model for LAI as a function of radar backscattering from lowland rice : Sensitivity analysis

Symbols are observed data from Chen & Lin (1994) and Inoue et al. (1995)*Attema & Ulaby (1978); #Shen et al., 2009

#Cloud* based semi-empirical model for LAI as a function of radar backscattering from lowland rice : Sensitivity analysis

Symbols are observed data from Chen & Lin (1994) and Inoue et al. (1995)*Attema & Ulaby (1978); #Shen et al., 2009

#Cloud* based semi-empirical model for LAI as a function of radar backscattering from lowland rice : Sensitivity analysis

Symbols are observed data from Chen & Lin (1994) and Inoue et al. (1995)*Attema & Ulaby (1978); #Shen et al., 2009

#Cloud* based semi-empirical model for LAI as a function of radar backscattering from lowland rice : Sensitivity analysis

VietnamMekong River Delta2013 Summer AutumnJun-SepEnd of Season Sim.