1

Earth Observation for Agriculture – State of the Art –

F. BaretINRA-EMMAH

Avignon, France

2/20

Outlook

• The several needs for agriculture• Observational Requirements

– Variables targeted / accessible– Spatial– Temporal

• Retrieval of key variables from S2 observations– Generic algorithm– Specific algorithm– Assimilation

• Conclusion/recommandations

3/20

The several needs for agriculture

Regional/International

Local

Statistics

Control

Precision agriculture

Farmers

Tools Seeds Fertilizer Pesticide

Dealers

Insurance

Governments

Food Industry

Cooperatives

Consultants

Traders

Governments

Food Industry

4/20

From observations to applications

Structure

Biochemical content

Soil

AtmosphereCanopy

FunctioningModels

Assimilation of radiances

Biophysical variables estimates (Products) Assimilation of Products

• Need for biophysical products (LAI, fAPAR, fCover, Albedo) and their dynamics– Used as indicators for decision making – Input to crop process models– Smooth expected temporal course (allows smoothing / real time estimates)– Allows validation– Provide uncertainties

• Need for crop classification

5/20

Observational requirements: Variables targetted (and accessible!)

Biophysical variables of interest:• LAI (actually GAI)• Green fraction (FAPAR, FCOVER)• Chlorophyll content• Water content• Soil related characteristics • Crop residue estimates

6/20

Spectral requirements• Correction for the atmosphere• Sampling the absorption of main leaf constituants

7/20

Observational requirements: Spatial resolution

• Precision agriculture: intra-field variability• Other applications:

– Fields– Species (regional assessment of production)

Number of patches/pixel Purity of pixel Variability within pixel

Large differences between 10-20-60 m with 100-250-1000m

8/20

Observational requirements: Revisit frequency

• Providing information on crop state at specific stages (± 1 week)

• Monitoring crops for resources management

Green Fraction

Gree

n Fr

actio

n

Getting information every 100°C.day:- One month in winter- 5 days in summer

Accounting for clouds (≈50% occurence)

9/20

Retrieval of key variables from S2: Generic algorithms

• Applicable everywhere with variable accuracy but good consistency• Allows continuity with hectometric/kilometric observations• Based on simple assumptions on canopy structure

10/20

Retrieval of key variables from S2: Generic algorithms applied to several sensors

Capacity to build a consistent time series from multiple sensors Virtual constellation Possible spectral sensitivity residual effects

Time

SPOT4Rapideye IRS SPOT4 Landsat Landsat SPOT4 SPOT4 DMC

Grassland_1 Shrubland Forest (oak)Grassland_2

11/20

Retrieval of key variables from S2: Specific algorithms

• Need knowledge of land-use (species / cultivars)– On the fly land-use (continuously updated)

• Allows using prior distribution of canopy characteristics– Canopy Structure– Leaf properties (structure, chlorophyll, SLA, water, surface effects …)

• Need calibration over – detailed radiative transfer model– Comprehensive experiments

12/20

Calibration over radiative transfer modelsGeneric (Turbid) Specific (3D)

Measured LAI Measured LAI

Measured LAI Measured LAI

Estim

ated

LAI

Estim

ated

LAI

Estim

ated

LAI

Estim

ated

LAI

Maize

Vineyard

From Lopez-Lozano, 2007

Better use more realistic 3D model than turbid medium (generic) model

13/20

Calibration over experiments

Gree

n Fr

actio

n

Use of (HT) phenotyping / agronomicalExperiments

Characterize specific structural traits

14/20

Combination with crop models

? Variables of interest

Radiance observations

Process model

(dynamic)

ModelParameters

Diagnostic variables

Radiative Transfer Model

Ancillary Information/data

Assimilation allows to:• input additional information in the system:

– Knowledge on some processes– Exploitation of ancillary data (climate, soil, …)

• exploit the temporal dimension: process model as a link between dates• access specific processes / outputs (biomass, yield, nitrogen balance)• Run process models in prognostic mode : simulations for other conditions

Combination with crop modelsExample of assimilation

Question: How to optimize the nitrogen amount for a field crop ?

Inputs: • Climate (past) • Soil (Prior knowledge of characteristics, but no spatial variability)• Technical practices (sowing date, …)• Crop model (STICS) and some crop parameters• 3 flights with CASI instrument

Outputs:• Map of nitrogen content (QN)

16/20

Assimilation of (RS) observationsPrior distribution of

inputs

Climate past'

Soil

Cultural Pract.

Crop model

Prior distribution of outputs LAI, Cab

200 000 cas

Cost functionRemote sensing

EstimatesLAI, Cab

Posterior distribution of inputs1 000 cases

Actual QN (kg/ha)

Post

erio

r QN

(kg/

ha)

Flight 1

Flight 2

Flight 3

Actual QN (kg/ha)

Prio

r QN

(kg/

ha)

Flight 1

Flight 2

Flight 3

17/20

Conclusion & Recommandations

• Organize the validation / calibration to capitalize on the work done• Build an archive (anomalies)• Fusion with other missions for improved revisit frequency at the level of biophysical

variables (or higher) products– decametric missions (Rapid-eye, DMC, Venµs, , SPOT6/7, LDCM…)– hectometric resolution observations (PROBA-V, S3 …)

• Development of algorithms for:– Top of canopy fused products at 10 m resolution and original resolutions– on the fly classification (continuously updated)– specific products per crop/cultivar– Patch (object) oriented algorithm to take into account

• the continuity within patches• The variability within patches (texture)

• Development of combination of S2 data with crop models (Assimilation)– Improved description of canopy structure by models in relation to function– Simplification of crop models (meta-model)

• S2 very well adapted to requirements for agriculture

• Following issues to be solved: