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

A. Olioso, Y. Inoue, S. Ortega-Faria, J. Demarty, J.-P. Wigneron, I. Braud, F. JacobP. 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)

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)

Needs for continuous estimationsinclude vegetation growth models• crop simulation models• ‘interactive’ vegetation-SVAT models

Use remote sensing data

Introduction

Soil-Vegetation-Atmosphere Transfer (SVAT) models

• energy balance : heat fluxes• mass fluxes : H2O and CO2• water budget

Mainly used for environmental studies (meteorology, hydrology…)

RS data surface temperaturesurface soil moistureLAI and fraction of vegetation cover albedo

Introduction

Crop simulation models

• plant development (phenology)• biomass production, yield• water and nitrogen budgets

Mainly used for agronomical studies:production: potentiality, monitoring, predictionpractices: e.g. optimisation of fertilisation, irrigation environemental : pollution (nitrate…)

RS data LAIwater biomass

Introduction

Remote sensing data provide spatialized information on plant processes

• information is indirect • requires to establish links between models variables andradiometric signals

Information has not always a regular time step and may originate from several sensors (or platforms)

• need for elaborated procedures to incorporate informationin models

assimilating remote sensing data in Crop and SVAT modelsVarious methods ……

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

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

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 balanceproduction quality ...

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.)

irrigated

lot of rain

slight irrigation

slight irrigation

Irrigation

-0.30.81999AvignonMaize-0.20.61993AvignonWheat-0.21.21990AvignonSoybean-0.71.11989AvignonSoybean

00.91998-99TalcaTomato

Bias(mm d-1)

RMSE(mm d-1)

YearPlaceCrop

Calibration of ISBA-Ags on Soybean 1990 experiment

→ vegetation structure (LAI) : biomass/LAI parameterleaf 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

Assimilation Principle : forcing methods Radiometric

measurements

Crop and Soil parameters

Canopy statusYield

Energy and water fluxes

Environmental variables

Climate forcing

SVAT model

Crop model

Inverse Radiative

Transf. model

Ex: LAI

ISBA-Ags

Forcing LAI profile

Flux estimation

Forcing LAI

Estimation of LAI temporal profile

RMSE = 1.1 mm/d

NDVI-LAImodel

Assimilation Principle: re-calibration methods

Signal simulations

Direct Radiative

Transf. model

Radiometric

measurements

Control

LAIWater content

Veg. heightSoil moistureTemperature

Crop and Soil parameters

Canopy statusYield

Energy and water fluxes

Environmental variables

Crop model

SVAT model

Climate forcing

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)

Simulating remote sensing data• reflectances• surface temperature• backscattering coefficients• microwave emission

(see Wigneron et al. 2002 JGR)

Recalibration of ISBA-Ags from remote sensing data

Case 1:

Data: Soybean 1989: Surface temperatureNDVIBackscattering 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

Assimilation of thermal infrared brightness temperature

Increasing initial soil moisture

Assimilation of NDVI

Increasing initial soil moisture

Assimilation of backscattering coefficients

Increasing initial soil moisture

Assimilation of RS data

→ known vegetation parameters → unknown initial soil moisture retrieved from RS data

RMSE on LE:

60 W/m254 W/m2

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

Recalibration of ISBA-Ags from remote sensing data

Case 2:

Data: Soybean 1989: Backscattering coefficients (Radar)

Model and parameters:→ unknown vegetation parameters→ known initial soil moisture

Method: varying vegetation growth parameters to fit remote sensing signal simulations to measurements

Case 2’:

Model and parameters:→ unknown vegetation parameters→ unknown initial soil moisture

Assimilation results

• LAI• Water reserve• Evapotranspiration

Radar -> RMSE = 1.0 mm d-1

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

Assimilation and results

• NDVI and Cumulated evapotranspiration

• real frequency of irrigation = ~1 week before DOE 170 ~4 days DOE 170-200~1 week DOE 200-230

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

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, …

Day of Experiment

Surfa

ce te

mpe

ratu

re d

iffer

ence

betw

een

two

field

s

Model with irrigationModel without irrigation

Identification of irrigated fields

by comparing time change of surface temperature to a non-irrigated field

irrigated

not irrigated

rainsoil depth

rainsoil depth

Assimilation

ISBA SVAT model

Surface parameters:Interpolated LAI and

height

Reflectance data

Ts

mm

Evapotranspiration map

Cumulated evapotranspiration (February-June)

Field 120(Irrigated ~100 mm)measured ET = 359 mm

Field 214Spring wheatmeasured ET = 245 mm

Field 101measured ET = 305 mm

Comparison to soil water balance in three instrumented fields

mm

Irrigation supply map

One irrigation event at the end of March usually

Fields for which irrigation was seen during the

ground survey

Field 120supply ~100 mm (?)

Comparison to ground information

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]

• 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]

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