Assimilation of remote sensing data into crop simulation models and SVAT models for evapotranspiration and irrigation monitoring

Transcription

1 Assimilation of remote sensing data into crop simulation models and SVAT models for evapotranspiration and irrigation monitoring A. Olioso, Y. Inoue, S. Ortega-Faria, J. Demarty, J.-P. Wigneron, I. Braud, F. Jacob P. Lecharpentier, C. Ottlé, J.-C. Calvet, N. Brisson INRA, Avignon et Villenave d Ornon, France (Agronomie, Remote Sensing) NIAES, Tsukuba, Japan (Carbon cycle, Remote Sensing) CITRA, Talca, Chile (Agronomie, Irrigation) LTHE, Grenoble, France and CEMAGREF, Lyon, France (Hydrology) CETP, Vélizy, France (Hydrology, Remote Sensing) CNRM, Météo-France, Toulouse, France (Meteorology, Climate, Remote Sensing) ESAP, Toulouse, France (Remote Sensing)

2 Introduction Goals : using models for crop production assessment crop water requirement monitoring environmental issues (hydrology, meteorology, pollution Work at the landscape scale We need to correct model drifts We need to control unknown parameters (spatial variability) Use remote sensing data Needs for continuous estimations include vegetation growth models crop simulation models interactive vegetation-svat models

3 Introduction Soil-Vegetation-Atmosphere Transfer (SVAT) models energy balance : heat fluxes mass fluxes : H 2 O and CO 2 water budget Mainly used for environmental studies (meteorology, hydrology ) RS data surface temperature surface soil moisture LAI and fraction of vegetation cover albedo

4 Introduction Crop simulation models plant development (phenology) biomass production, yield water and nitrogen budgets Mainly used for agronomical studies: production: potentiality, monitoring, prediction practices: e.g. optimisation of fertilisation, irrigation environemental : pollution (nitrate ) RS data LAI water biomass

5 Introduction Remote sensing data provide spatialized information on plant processes information is indirect requires to establish links between models variables and radiometric signals Information has not always a regular time step and may originate from several sensors (or platforms) need for elaborated procedures to incorporate information in models assimilating remote sensing data in Crop and SVAT models Various methods

6 Content Introduction SVAT models Crop models Some simple examples of assimilation procedures forcing recalibration Application: mapping wheat evapotranspiration and irrigation over the Alpilles test site

7 SVAT models 1-Classical Soil-Vegetation-Atmosphere Transfer (SVAT) model : ISBA (Noilhan and Planton 1989) physical model, energy balance, water balance land surface scheme of the Météo-France weather forecast model and atmospheric general circulation model 2-SVAT model with interactive vegetation : ISBA-Ags (Calvet et al. 1998) vegetation carbon balance (interaction photosynthesis-stomatal conductance) simple LAI / biomass parametrisation

8 Crop model Crop models : STICS (Brisson et al. 1998): generic crop model (wheat, corn, sorghum, soybean, sunflower, forage, tomato, banana, sugar cane, grape...) modular description of phenology biomass production water balance nitrogen balance production quality...

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11 Some results with the ISBA model uncalibrated inputs = meteorological data, crop LAI and height basic knowledge on soil (texture, wilting point, field capacity) standard vegetation parameters (albedo, minimum stomatal res.) Crop Place Year RMSE Bias (mm d -1 ) (mm d -1 ) Tomato Irrigation Talca Soybean slight irrigation Avignon Soybean slight irrigation Avignon Wheat lot of rain Avignon Maize irrigated Avignon

12 Calibration of ISBA-Ags on Soybean 1990 experiment vegetation structure (LAI) : biomass/lai parameter leaf death parameter dry matter production and soil moisture evolution : leaf photosynthesis and stomatal conductance parameters Application to Soybean 1989 improved results : RMSE on ET = 0.78 mm/d

13 Assimilation Principle : forcing methods Radiometric measurements Inverse Radiative Transf. model Ex: LAI Crop and Soil parameters Crop model SVAT model Canopy status Yield Energy and water fluxes Environmental variables Climate forcing

14 Forcing LAI Forcing LAI profile ISBA- Ags Estimation of LAI temporal profile Flux estimation NDVI-LAI model RMSE = 1.1 mm/d

15 Assimilation Principle: re-calibration methods Radiometric measurements Control Crop and Soil parameters Signal simulations Direct Radiative Transf. model LAI Water content Veg. height Soil moisture Temperature Crop model SVAT model Canopy status Yield Energy and water fluxes Environmental variables Climate forcing

16 Radiative transfer models Solar and thermal domains : original models or Beer law type formulations calibrated against radiative transfer models : Solar domain : SAIL-2M (Weiss et al. 2001) and Myneni (Myneni et al. 1992), Thermal infrared domain : SAIL-T (Olioso 1992, Chelle et al. 2001) Microwave domain : water cloud type models (+ IEM soil model) active and passive microwave (L, X and C bands) calibrated against more physical radiative transfer models derived by Wigneron et al. (1993, 1997)

17 Simulating remote sensing data reflectances surface temperature backscattering coefficients microwave emission (see Wigneron et al JGR)

18 Recalibration of ISBA-Ags from remote sensing data Case 1: Data: Soybean 1989: Surface temperature NDVI Backscattering coefficients (Radar) Model and parameters: known vegetation parameters (model calibrated over 1990) unknown initial soil moisture Method: varying initial soil moisture to fit remote sensing signal simulations to measurements

19 Assimilation of thermal infrared brightness temperature Increasing initial soil moisture

20 Assimilation of NDVI Increasing initial soil moisture

21 Assimilation of backscattering coefficients Increasing initial soil moisture

22 Assimilation of RS data known vegetation parameters unknown initial soil moisture retrieved from RS data RMSE on LE: 60 W/m2 54 W/m2

23 Assimilation results LAI Water reserve Evapotranspiration NDVI -> RMSE = 1.2 mm d -1 Ts -> RMSE = 0.8 mm d -1 Radar -> RMSE = 0.9 mm d -1

24 Recalibration of ISBA-Ags from remote sensing data Case 2: Data: Soybean 1989: Backscattering coefficients (Radar) Case 2 : Model and parameters: unknown vegetation parameters known initial soil moisture Method: varying vegetation growth parameters to fit remote sensing signal simulations to measurements Model and parameters: unknown vegetation parameters unknown initial soil moisture

25 Assimilation results LAI Water reserve Evapotranspiration Radar -> RMSE = 1.0 mm d -1

26 Recalibration of ISBA-Ags from remote sensing data Case 3: Data: Maize : NDVI Model and parameters: known vegetation parameters unknown irrigation supplies Method: varying frequency of irrigation supplies to fit remote sensing signal simulations to measurements

27 Assimilation and results NDVI and Cumulated evapotranspiration real frequency of irrigation = ~1 week before DOE 170 ~4 days DOE ~1 week DOE

28 Application: mapping wheat evapotranspiration and irrigation over the Alpilles test site (1997) 4 km X 5 km agricultural zone near Avignon (SE France) Main crops: wheat (30 %, 100 fields), Sunflower, Orchard Climate: very wet winter, very dry spring (no rain for 3 months) Data: Land use map from SPOT data and aerial photography Reflectances (airborne PoLDER): 16 images from February to October maps of LAI (20 m resolution) maps of wheat vegetation height Thermal infrared data (airborne INFRAMETRICS TIR camera): 18 images Standard meteorological measurements

29 Application: mapping wheat evapotranspiration and irrigation over the Alpilles test site (1997) Model and parameters: ISBA standard (calibrated in 1993 for wheat) known initial soil moisture (very wet winter) variability of soil characteristics not known with accuracy (wilting point, field capacity, soil depth. irrigation supplies unknown Method: forcing interpolated LAI and height in ISBA comparing simulated surface temperature to TIR images identification of irrigated fields retrieval of soil depth mapping evapotranspiration validation to be done / flux and soil moisture measurements in 3 fields / flux maps from SEBAL, MAM, S-SEBI,

30 Identification of irrigated fields by comparing time change of surface temperature to a non-irrigated field Surface temperature difference between two fields not irrigated irrigated Model with irrigation Model without irrigation Day of Experiment

31 Reflectance data rain soil depth Surface parameters: Interpolated LAI and height Ts ISBA SVAT model Assimilation

32 Evapotranspiration map Cumulated evapotranspiration (February-June) Comparison to soil water balance in three instrumented fields Field 120 (Irrigated ~100 mm) measured ET = 359 mm mm Field 214 Spring wheat measured ET = 245 mm Field 101 measured ET = 305 mm

33 Irrigation supply map One irrigation event at the end of March usually Comparison to ground information mm Fields for which irrigation was seen during the ground survey Field 120 supply ~100 mm (?)

34 Concluding remarks SVAT models may be used in combination with remote sensing data for monitoring evapotranspiration of irrigated crops. Similar studies are undergoing using crop models (but the problem is more complex: very large number of input parameters). Various methods for assimilating remote sensing data may be used (depending on the type of data and model, the information to retrieve ) They are many advantages of using SVAT or crop models, e.g.: continuous estimation of evapotranspiration over the crop cycle (instead of snapshot images with classical mapping methods) [ self interpolation between Earth observation satellite images (TM, SPOT) which have a good spatial resolution but a bad temporal resolution] [they may be used when no remote sensing data is available and in a predictive mode]

35 close results may be obtained using any type of classical remote sensing data (reflectances, Ts, radar) [evapotranspiration for stressed crop may be obtained without thermal infrared because of the interaction between water stress and crop growth] crop and SVAT models simulate not only evapotranspiration, but also plant transpiration, soil moisture, crop production [crop models are already used for production assessment, irrigation and nitrogen management]

36 SVAT or crop models also have drawbacks, e.g.: large number of parameters to retrieve [ISBA has between 10 and 15 important parameters; crop models have a lot more] continuous monitoring of meteorological data is required assimilation may be very sensitive to the accuracy of remote sensing data when applied over large area, it may be difficult to have information on the soil, the plant type a large computing power is required for running models over large area; this may be increased several times for assimilation