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

2. 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

3. 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

4. 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

5. Regional yield forecasting  Time-series regression models: 5 Forecast year = a+ (b x year) + c + (d x indicator_value)

6. Regional yield forecasting  Scenario analysis (finding agro-meteorological years most similar to current year): 6 Forecast year = a+ (b x year) + weighted average residuals of similar years

7. Regional yield forecasting 7 courtesy: JRC, 6 th CGMS expert meeting

8. Regional yield forecasting 8 courtesy: AGRICAB, EU-FP7 Maize, Senegal Use of official yield statistics requires: Local expertise Rigorous & critical review Best performing single indicator models: Regional WOFOST (crop model) EO-based cumulated fAPAR Cumulated rainfall since 20 May Trend model

9. Regional yield forecasting courtesy: E-Agri, EU-FP7

10. 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

11. 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) 11 rain fed maize

12. Earth Observation (EO) data in models?  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) 12

13. Earth Observation (EO) data in models?  How? ● Driving weather variables e.g. RFE (TAMSAT, TRMM, CHIRPS..), radiation,.. 13 Default MCYFS MeteoSat 2nd Gen. Radiation 2011

14. Earth Observation (EO) data in models?  Data quality e.g. ● Validation of MSG radiation (Roerink et al., 2012) ● Validation RFE Mozambique (Toté et al., 2015 ) 14

15. Earth Observation (EO) data in models?  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

16. Example of optimizing the WOFOST model Optimum SPAN/TWDI for given LAI profile Courtesy: Belgian funded GLOBAM project

17. Earth Observation (EO) data in models?  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

18. Earth Observation (EO) data in models?  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

19. Example of satellite LAI for winter-wheat Belgium – Walloon region DUVEILLER et al.: USING THERMAL TIME AND PIXEL PURITY FOR ENHANCING VARIABLE TIME SERIES. DOI: 10.1109/TGRS.2012.2226731

20. Conclusions 20  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.)

21. Most promising regions – fields size Large field size Medium field size Small field size Source: IIASA

22. Most promising regions – cloud fraction