Sentinels Synergy for Agriculture

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Sentinels Synergy for Agriculture

H2020 EO-3-2016: Evolution of Copernicus services

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XVII Congreso de la Asociación Española de Teledetección. Murcia. 3-7 de octubre de 2017

Objetivos de SENSAGRI

• Desarrollar nuevas aplicaciones para el sector agrícola europeo, combinando datos de los

satélites del programa

Copernicus, Sentinel-1 (radar), Sentinel-2 (óptico)

y datos in-situ.

• Desarrollar

prototipos de servicios Copernicus

para humedad del suelo (

SSM)

, índice de

área foliar (

LAI)

y

mapas de cultivos estacionales,

y usar esos prototipos para

implementar

servicios agrícolas avanzados (“prueba de concepto”).

• Validar los servicios

y establecer

casos de

demostración

en áreas test, para mostrar el

potencial de éstos como futuros servicios

Copernicus .

• Interaccionar con Copernicus y con el sector

agrícola

para dar a conocer y enfocar

adecuadamente los servicios propuestos.

www.sensagri.eu

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Servicios de SENSAGRI

SENSAGRI products

Current Copernicus products

Prototype services (widely applicable) Target Spatial resolution Target Temporal resolution

Product Spatial resolution _resolutionTemporal

Surface soil moisture (SSM) 250-1000 m* weekly** Soil Water Index

(METOP-ASCAT)

>= 1000 m (prod)

300 m (in dev.) Daily

Green and brown LAI 20 m weekly** Leaf Area Index (SPOT-VGT, Proba-V)

>= 1000 m (prod)

300 m (in dev.) 10-daily

Seasonal crop mapping 20 m*** 2 or 3 per year None available

Proof-of concept services (limited

applicability outside test areas)

Dry Biomass & Yield 20 m

Dry biomass:

weekly at season end Yield: yearly

Dry matter productivity

(SPOT-VGT, Proba-V) 1000 m 10-daily

Irrigated/not irrigated area 250 m* 3 or 4 per year Corine Land Cover (three classes)

MMU of 5 to 25 ha

bi-yearly yearly

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Estrategia de validación

Four European agricultural test sites

Spain: ITACYL - Duero River-basin

France: UPS-CESBIO - OSR Auradé and Lamasqère

Italy: CREA/CNR - Apulian Tavoliere

Poland: IPP/NRI – Winna Góra

Three non-European test sites

Argentina: INTA – Hilario Ascasubi

Two JECAM sites (Morocco and Tunisia)

CLC agricultural classes. Source: EEA

Services Ground Variable Time SSM

Volumetric Soil Moisture at 0-5 cm

depth

Continuous monitoring

3 measurement campaigns at critical stages and during irrigation season

LAI

Leaf Area Index, with LAI-2000 Plant

Canopy Analyzer

3 measurement campaigns at critical stages and during growing season

Seasonal Crop

Mapping Crop types

3 measurement campaigns in March, June and September

Irrigated areas Irrigated and not

irrigated fields 3 measurement campaigns in March, June and September

Tilled areas Tilled and not

tilled fields

Yield Commercial yield 1 measurement campaign at crop harvest

Intermediate

crops Crop types

2 measurement campaigns in March and July

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LAI green & LAI brown

Current operational LAI products

focus on green vegetation

When crops senescence LAI stays

high even when chlorophyll

degrades to zero

Senescent vegetation represents a significant

amount of aboveground biomass and is a key factor

in the carbon cycle

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LAI green &

LAI brown

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• Non-parametric regression

method

• Training datasets of Input –

Output data pairs

• Kernel - based MRLA

method

Estimación de LAI mediante Gaussian Processes

Regression (GPR)

• Delivery

of

associated

uncertainties

• Evaluation of the spectral

bands relevance

- Input: Reflectance

observations

- Output: Biophysical

parameter

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Datos para calibración

• Barrax, Spain

• SPARC 12

th

_{to 14}

th

_{July 2003}

• LiCor-LAI-2000

• HyMap

• Corn, potato, garlic, onion

• Valencia, Spain

• L’Horta

• April 7

th

₂₀₁₇

• Pocket LAI

• Onion, orange tree, lettuce

LAI

green

• Barrax, Spain

• SPARC 12

th

_{& 14}

th

_July

2003

• LiCor-LAI-2000

• HyMap

• Valladolid, Spain

• 20

th

_{to 22}

nd

_{June 2017}

• LiCor-LAI-2200

LAI

brown

• Bare soil, roads, buildings

• S2 images:

Non-vegetated

(LAI=0)

LAI green 223 points

LAI brown 84 points

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XVII Congreso de la Asociación Española de Teledetección. Murcia. 3-7 de octubre de 2017 13

Software Package

K-Fold Cross

Validation

GPR model LAI green

GPR model LAI brown

Training dataset

divided into k

equal-sized

sub-dataset

Training  k-1 sub-datasets

Validation  single sub-dataset

The cross

validation

process is

repeated k

times

Validación

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Resultados: Evaluación de los modelos de GPR

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LAI green

LAI brown

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LAI green and LAI brown retrieval

LAI green

LAI brown

µ [m

2

_/m

2

_]

_{SD [m}

2

_/m

2

_]

_{CV [%]}

Valladolid, Spain

June 2017

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Composite LAI green/LAI brown maps

Valladolid, Spain

June 2017

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Toulouse, France

July 2016

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Composite LAI green/LAI brown maps

Poznań , Poland

June 2017

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Conclusiones

• SENSAGRI

es un proyecto que aspira a proporcionar prototipos

para

nuevos servicios de Copernicus en agricultura

.

• La

Universidad de Valencia

coordina el proyecto y es responsable

del desarrollo del prototipo para Índice de Área Foliar (

LAI)

• Se propone un índice que combina LAI verde y LAI “marrón” (Green

and Brown LAI)

• Se ha puesto a punto una cadena de proceso de S2, utilizando

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https://igarss2018.org/

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