Regional forecasting, models & satellites Some considerations in a general overview 10 March 2015, Hendrik Boogaard, Allard de Wit, Sander Janssen

Regional forecasting, models & satellites

Some considerations in a general overview

10 March 2015, Hendrik Boogaard, Allard de Wit, Sander Janssen

Key background experience

EC-JRC MARS system:● Development of CGMS: a regional implementation of WOFOST● Operational continental agro-meteorological services

AGMIP:● Inter-comparison of models (response to climate change)● Co-lead IT & develop an e-infrastructure: model connectors,

data management, visualization & access (journal)● Global Gridded Crop Model Inter-comparison (GGCMI)

EU SIGMA project:● Develop & test methods to (i) characterize cropland and assess

its changes at various scales; (ii) assess changes in agricultural production levels; (iii) environmental impacts of agriculture

2

1. Global / Regional System of Systems

main producer countries, main commodities

2. National Capacity Building

for agricultural monitoring

using earth observation

3. Monitoring countries at risk

food security assessment

4. EO data coordination

5. Method improvement through R&D coordination (e.g. JECAM)

6. Data products and information dissemination

Key background experience

Regional yield forecasting

In-situ within-season field measurements are expensive and possibly subjective

In addition use of complementary approaches:● Models varying from simple statistical, to canopy reflectance to

dynamic deterministic models to assess (a proxy of) yield

● Finding balance between complexity and sensitivity while taking account of data availability & quality

● Generally deliver simulated (in-season) yields, thus not actual (final) yield at regional level

4


Time-series regression models:

5

Winter-wheat yield -- Hebei province

2.5

3

3.5

4

4.5

5

5.5

6

1992 1994 1996 1998 2000 2002 2004

Year

Yie

ld [

ton

/ha

]

R 2̂ = 0.521

Residuals vs CGMS simulation results

-0.5

-0.25

0

0.25

0.5

3000 3500 4000 4500 5000 5500

CGMS simulated yield [kg/ha]

Yie

ld a

no

mal

y [t

on

/ha]

Forecastyear = a+ (b x year) + c + (d x indicator_value)


Scenario analysis (finding agro-meteorological years most similar to current year):

6

Forecastyear = a+ (b x year) + weighted average residuals of similar years


7

courtesy: JRC, 6th CGMS expert meeting


8courtesy: AGRICAB, EU-FP7

Maize, SenegalUse of official yield statistics requires:• Local expertise• Rigorous & critical review

Best performing single indicator models: Regional WOFOST (crop model)EO-based cumulated fAPARCumulated rainfall since 20 MayTrend model


0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Max R² of single indicator models for Morocco (DW)

Classic

Cum. Rainfall

Remote sensing

Improved

courtesy: E-Agri, EU-FP7

Why using dynamic, deterministic crop models?

Crop specific, direct response, extend with weather forecast (15 days, monthly)

Predict minor crops

Get insight in processes (extreme conditions)

Beyond crop monitoring & yield forecasting:● Impact analyses (crop management, climate change...)● Analyses of yield risk & variation in space and time e.g. up

scaling site-specific experimental yield data● Explore future yield levels assuming

optimal management e.g. GYGA(Global Yield Gap Atlas)

● Land use management, suitability

10

Earth Observation (EO) data in models?

EO assesses actual state or key driving variables delivering products such as: time series of rainfall, vegetation greenness, soil moisture etc.; and derived information like start of season (SOS), land cover / crop maps, ...with a high spatial variability

Limited use for explorative studies:● For instance EO-based correction of model parameters cannot

be used when exploring absolute potential yield levels (Yp, Yw) (useful to explore spatial var. of Yp/Yw)

● Products like crop masks might be used as realistic pre-conditions in these studies (e.g. SPAMM in GYGA)

11rain fed maize


Very useful for monitoring and crop yield forecasting:

● We need to assess actual production of on-going season

● Models do not simulate actual production as not all stresses are included to arrive at realistic yield level

● Regional implementation of models lack sufficient spatial variability for some input (e.g. SOS)

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How? ● Driving weather variables e.g. RFE (TAMSAT, TRMM, CHIRPS..),

radiation,..

13

Default MCYFS MeteoSat 2nd Gen.

Radiation 2011


Data quality e.g.● Validation of MSG radiation (Roerink et al., 2012)● Validation RFE Mozambique (Toté et al., 2015 )

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How?● Driving variables around weather e.g. RFE (TAMSAT, TRMM,

CHIRPS..), radiation,..● Crop maps ( ) to focus:

● Simulation effort (data collection, calibration)● More accurate aggregation to regional indicators

● SOS (EO spatial-tempo variability & local in-situ data; )● Improving model parameters and/or bio-physics

● To reduce model uncertainties (models, assumptions, input data,..) and omitted model processes / stresses

● Logical order in e.g. WOFOST:Step 1: Estimating sowing and harvest dates (avoid phenological shift)

Step 2: Improving simulated state variables: leaf area index, soil moisture,

(forcing, recalibration, updating)15

Example of optimizing the WOFOST model

Optimum SPAN/TWDI for given LAI profile

Courtesy: Belgian funded GLOBAM project


Requirements:● Large area coverage (regional/continental scale)● High within-season temporal coverage● Operational, economical and high degree of availability● Historic archive is crucial:

● Cover range of climatic variability over years● Continuity and consistency of system output over time● SIGMA: → inter-calibration of sensors to replace missing

values & to extend time series in time


Go from cropland to crop-specific ● Crop-specific modelling possibly hampered by

● Mismatch between landscape and satellite sensor; possible solutions:

● Un-mixing / selection of pure pixel by annual crop map (e.g. based on farmers declarations)

● Move to HR: costly, limited availability in time : data fusion of MR & HR

● No timely information on crop location● Need for rapid within-season crop mapping

● Provide exact location of specific crops● Also needed for spatial aggregation & estimating total

crop production For EO purposes complex models can be simplified

Example of satellite LAI for winter-wheatBelgium – Walloon region

DUVEILLER et al.: USING THERMAL TIME AND PIXEL PURITY FOR ENHANCING VARIABLE TIME SERIES. DOI: 10.1109/TGRS.2012.2226731

Conclusions

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For regional forecasting simple statistical models sometimes work

Focus on key products like cropland or crop specific maps, SOS, RFE as they serve many different apps

Dynamic, deterministic models have their added value for monitoring and crop yield forecasting (gain understanding, examine extremes, generate advanced indicators, extending simulations etc.)

EO-data assimilation in these models is data demanding (crop specific, early crop map..) and requires dedicated capacity/resources:

● Select most promising areas

● Protocol / guidance (objective, data aspects, landscape etc.)

Most promising regions – fields size

Large field size

Medium field size

Small field size Source: IIASA

Most promising regions – cloud fraction

Documents

Regional forecasting, models & satellites Some considerations in a general overview 10 March 2015, Hendrik Boogaard, Allard de Wit, Sander Janssen