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Earth Observation for Agriculture – State of the Art – F. Baret INRA-EMMAH Avignon, France 1.

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Presentation on theme: "Earth Observation for Agriculture – State of the Art – F. Baret INRA-EMMAH Avignon, France 1."— Presentation transcript:

1 Earth Observation for Agriculture – State of the Art – F. Baret INRA-EMMAH Avignon, France 1

2 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 2

3 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 3

4 From observations to applications Structure Biochemical content Soil Atmosphere Canopy Functioning Models 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 4

5 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 5

6 Spectral requirements Correction for the atmosphere Sampling the absorption of main leaf constituants 6

7 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 7

8 Observational requirements: Revisit frequency Providing information on crop state at specific stages (± 1 week) Monitoring crops for resources management Green Fraction Getting information every 100° -One month in winter -5 days in summer Accounting for clouds (≈50% occurence) 8

9 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 9

10 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 SPOT4 Rapideye IRS SPOT4Landsat SPOT4 DMC Grassland_1ShrublandForest (oak)Grassland_2 10

11 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 11

12 Calibration over radiative transfer models Generic (Turbid)Specific (3D) Measured LAI Estimated LAI Maize Vineyard From Lopez-Lozano, 2007 Better use more realistic 3D model than turbid medium (generic) model 12

13 Calibration over experiments Green Fraction Use of (HT) phenotyping / agronomical Experiments 13 Characterize specific structural traits

14 Combination with crop models ? Variables of interest Radiance observations Process model (dynamic) Model Parameters 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 14

15 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)

16 Assimilation of (RS) observations Prior distribution of inputs Climate past' Soil Cultural Pract. Crop model Prior distribution of outputs LAI, Cab 200 000 cas Cost function Remote sensing Estimates LAI, Cab Posterior distribution of inputs 1 000 cases 16 Actual QN (kg/ha) Posterior QN (kg/ha) Flight 1 Flight 2 Flight 3 Actual QN (kg/ha) Prior QN (kg/ha) Flight 1 Flight 2 Flight 3

17 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) 17 S2 very well adapted to requirements for agriculture Following issues to be solved:

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