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Earth Observation for Agriculture – State of the Art –. F. Baret INRA-EMMAH Avignon, France. Outlook. The several needs for agriculture Observational Requirements Variables targeted / accessible Spatial Temporal Retrieval of key variables from S2 observations Generic algorithm - PowerPoint PPT Presentation
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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: