Earth Observation for Agriculture
– State of the Art –
F. Baret
INRA-EMMAH
Avignon, France
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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
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The several needs for agriculture
Precision agriculture
Local
Tools
Seeds
Dealers
Consultants
Cooperatives
Statistics
Governments
Fertilizer Pesticide
Farmers
Traders
Food Industry
Regional/International
Control
Insurance
Food Industry
Governments
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From observations to applications
Assimilation of radiances
Structure
Atmosphere
Biochemical
content
Canopy
Functioning
Models
Soil
Biophysical variables estimates (Products)
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Assimilation of Products
Need for biophysical products (LAI, fAPAR, fCover, Albedo) and their dynamics
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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
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Observational requirements:
Variables targetted (and accessible!)
Biophysical variables of interest:
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LAI (actually GAI)
Green fraction (FAPAR, FCOVER)
Chlorophyll content
Water content
Soil related characteristics
Crop residue estimates
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Spectral requirements
• Correction for the atmosphere
• Sampling the absorption of main leaf constituants
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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
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Observational requirements:
Revisit frequency
• Providing information on crop state at specific
stages (± 1 week)
• Monitoring crops for resources management
Green Fraction
Green Fraction
Getting information every 100°C.day:
- One month in winter
- 5 days in summer
Accounting for clouds (≈50% occurence)
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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
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Retrieval of key variables from S2: Generic
algorithms applied to several sensors
Rapideye
SPOT4
IRS
SPOT4
Landsat
Landsat
SPOT4
SPOT4
DMC
Time
Grassland_1
Grassland_2
Shrubland
Forest (oak)
Capacity to build a consistent time series from multiple sensors
Virtual constellation
Possible spectral sensitivity residual effects
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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
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Calibration over radiative transfer models
Specific (3D)
Estimated LAI
Maize
Estimated LAI
Generic (Turbid)
Measured LAI
Estimated LAI
Estimated LAI
Vineyard
Measured LAI
Measured LAI
Measured LAI
Better use more realistic 3D model than turbid medium (generic) model
From Lopez-Lozano, 2007
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Use of (HT) phenotyping / agronomical
Experiments
Characterize specific structural traits
Green Fraction
Calibration over experiments
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Combination with crop models
Ancillary
Information/data
Radiance
observations
Radiative
Transfer
Model
Diagnostic
variables
Process
model
(dynamic)
Variables of
interest
Model
Parameters
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
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Combination with crop models
Example 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)
200 000 cas
Prior distribution of
inputs
Climate past'
Soil
Prior QN (kg/ha)
Assimilation of (RS) observations
Flight 3
Flight 2
Flight 1
Crop model
Cultural Pract.
Actual QN (kg/ha)
Remote sensing
Estimates
LAI, Cab
1 000 cases
Cost function
Posterior distribution of
inputs
Posterior QN (kg/ha)
Prior distribution of
outputs LAI, Cab
Flight 3
Flight 2
Flight 1
Actual QN (kg/ha)
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Conclusion & Recommandations
• S2 very well adapted to requirements for agriculture
• Following issues to be solved:
• 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:
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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)
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