josé a. sobrino - copernicus · 2019-07-25 · josé a. sobrino image processing laboratory,...

EU agriculture Workshop -YH Kerr June 30th 2016

Land thermal imaging applications for agriculture

Yann KERR (CESBIO) for

José A. SobrinoImage Processing Laboratory, Global Change Unit,

Faculty of Physics, University of Valencia

and

Jean-Pierre LagouardeINRA /ISPA Bordeaux France

Telephone: +34 96 354 3115Email: [email protected]

Web Site: http://www.uv.es/ucg

Parque CientificoC/Catedrático José Beltrán, 246198 PaternaValencia. SPAIN


Atmosphere Control

(Radiation)

Surface Control

(Soil Moisture)

Surface Control

(Freeze/Thaw)

Main processes impacting vegetation

growth

Churkina & Running, 1998

Where are we?


Less attention has been given (in comparison to VNIR-SWIR data exploitation)

Necessary for a better understanding of land surface processes and land-atmosphere

interactions

(most of the fluxes at the surface/atmosphere interface can only be parametrized through the

use of TIR data).

Key parameters:

LAND SURFACE TEMPERATURE (LST)

LAND SURFACE EMISSIVITY (LSE, ) – spectral magnitude!

Low Resolution: (AATSR, SEVIRI, MODIS, AVHRR, etc) SLSTR (Sentinel 3)

LST: climatology, land cover change

LSE: not exploited. Used as input to LST algorithms.

High Resolution: (ASTER, ETM+ and TIRS, Airborne sensors, etc) … ?

LST: energy balance (heat fluxes => ET => water management), urban heat island,

etc

LSE: geology (mineral mapping), identification of characteristic features in the

spectrum,

and also land cover change

INTRODUCTION: Thermal Remote Sensing


Need of both revisit and spatialreolution, but no system presenlyoffering these capacities.

Since the 90’s studies conducted tobetter understand LST and modelingsurface fluxes, with the goal ofdefining specifications of a new TIRmission com bining both high spatialresolution and revisit.

Projects : IRSUTE (CNES ,Seguin et al.,1999), MISTIGRI (CNES, Lagouarde etal., 2013), TIREX (ESA, Sobrino &Lagouarde, 2010), THIRSTY (CNES –JPL/NASA, Crebassol et al., 2012)

Introduction : a TIR mission, long history…

LAI

ETR

LAI

ETR

Soybean, Avignon, 1990 : TIR and VNIR behave differently

Puducherry region

Revisitneeded Spatial resolution

needed


● Agriculture/forestry

- water stress detection / water needs / irrigation optimisation

- water resources management

- growth/ crop production, food security (see 2011 G20)

- impact of agricultural practices on water use, climate change adaptation

- forest fire risks, frost detection…

● Biogeochimical cycles- water quality

- soil pollution

- carbon cycle ↔ global warming processes

- arctic permafrost

● Hydrology- link with meteorology (mesoscale circulation)

- water budgets and biogeochimistry at watershed scale

● Ecosystem monitoring, ecology

- microclimates, biodiversity

- natural vegetation droughts

Testing pre-operational services

Scientific objectives - Ecosystem stress and water use

Trends in water use by sector (UNEP)(http://www.unep.org/dewa/vitalwater/article43.html)

20% arable land irrigated → 45% food production (FAO)

No Data < 5% 5-10% 10 -

20% 20-40% > 40%

% agric. water use, 2001

No Data < 1% 1-5%

5-10% 10-50% > 50%

% irrig. surf, 2003


Important methodological know-how → Efforts to develop practical tools for ET mapping

s

From Richard Allen (METRIC/SEBAL workshop Fort Collins, Colorado, February 7, 2005)

Scientific objectives - Ecosystem stress and water use

Operational applications to water management developed in USA

EVASPA (A. Olioso, France, TOSCA)Gallego-Elvira et al., 2013


WATER STRESS

Water stress detection in an olive

orchardWater stress is considered to be a major environmental factor limiting plant productivity worldwide. Develops in plants when evaporative losses cannot be sustained by extracting water from the soil by the roots


12 Olive trees

WATER STRESS

R (2.8 mm/day)

Stress: 25% : S1 (0.7 mm/día)

Stress: S2 (R/S1)

Water stress detection

in an olive orchard

using AHS imagerySepulcre-Cantó et al., 2006,

Agric. For. Meteorol., 136, 31-

44.

Different irrigationtreatments


● A high priority level is assigned to:

-Water management: water stress & ET

-Fire monitoring

● Applications related to water management relies on an

accurate retrieval of LST, using at least two TIR bands at

moderate to high spatial resolution. VNIR bands and

meteorological data are required in most cases.

● Fire monitoring applications are based on detection of High

Temperature Events (HTE), which typically requires SWIR-MIR

bands (to avoid saturation on TIR bands) and high revisit time.

EU agriculture Workshop -YH Kerr June 30th 2016 10

Big cities: Washington, Shanghai, Tokyo, etc,

From 30-80 years, Tmax summer increases 0.5 ºC each 10 years

• Day < 2 - 3oC.

• Night> 6 –10oC

Satellites

Urban RuralSUHI LST LST


•Strong societal demand for high resolution missions in the TIR

•More than 40 applications identified

•Good scientific maturity of the TIR community (models, experiments)

•Most of the requirements presented are covered with multispectral TIR concept (3 bands), daily revisit time and spatial resolution around 50 m. Basic VNIR bands (blue, green, red, near-infrared) are also required for atmospheric correction purposes and visual identification of the scene.

•Current TIR missions do not satisfy the users requirements for most of the applications (poor spatial resolution or poor revisit time).

•Need to fill the GAP

CONCLUSIONS


User requirements for soil moisture measurements:

what space based observation could do

Yann H. Kerr and the SMOS team

CESBIO, INRA-ISPA, IsardSat, TVU, ECMWF, WSL, LTHE, …


IntroductionExpression of users’ requirements

Will focus on agriculture (time constraint)

And only a few examples

And will not cover

No forestry (though interesting results biomass estimates)

No risk mitigation (fires , floods)

Very little climate monitoring and anticipation

Focus on one key variable

SOIL MOISTURE

Surface (meteorology, risks, erosion,…)

Root zone

And best way to obtain it


Unicity of passive L band measurementsPROS

Passive microwaves at low frequency Reduced sensitivity to atmosphere and sun irradiance (all weather) reduced sensitivity to structure

Vegetation canopy Surface roughness

L band measurements (passive: Radiometry different from RADAR) Reduced sensitivity to vegetation canopy Good penetration depth Sensitivity to sea salinity High sensitivity to soil moisture

Direct measurements of Soil moisture and Sea Surface salinity (no proxy, no scaling, …) hence usable in applications

CONS Spatial resolution (antenna diameter)

Meaning different options (i.e., SMOS, Aquarius, SMAP) and different price tags (€ 315 M, $400 M - instrument, $ 915 M) Radio frequency interferences

…See ITU actions and SMAP approach


L band measurementsA Short long story

Initiated in 1977 S194 on SKYLab

A few days of partial acquisitions

Very coarse resolution (125 km HPBW)

Interesting results but spatial resolution limited

waiting for new technologies

Rekindled in the 90’s ESTAR and 2D interferometry

Large deployable antennas

SMOS selected in 1997 (CNES) and 99 (ESA) CNES TAOB programme

ESA Earth Explorer opportunitymission (ESA, CNES CDTI)

Launched in 2009

Aquarius Selected by NASA ESSP in 2001

Launched in June 2011

End of life July 2015

SMAP Heritage of HYDROS (NASA ESSP 1999) cancelled in 2005

Reinitiated after 2007 Decadal survey

Launched in 2015

Radar failure in 2015

15

Drush et al., 2009


So What?FactsL band = New measurements not available beforeProxies not always adequate

New measurements New scienceo A wealth of new results in very various fields

Land , ocean, cryosphere

Science oriented missions butMany operational applications

o Showing the real impact of this data set real gap filler

Main issueContinuity

o There are no real existing alternatives

Illustration/ examples


Models and “proxy” sensors give erroneous estimates


ENHANCING WEATHER FORECAST(ECMWF)

NWP

18

© ECMWF

SMOS data assimilation in the IFS

Config.3

Config.2

Config.1

Normalized change in rms of fc error:

SH- extratropics Tropics NH- extratropics

imp

rov

em

en

td

eg

rad

ati

on

Based on short experimentsLonger experiment under evaluation

Near Real time available


AGRICULTURE APPLICATIONS: ROOTZONE SOIL MOISTURE, DROUGHTINDEX, CROP YIELD, RISKMANAGEMENT (FLOODS AND FIRES), FOOD SECURITY…

Agriculture

20

moderat mild extrem

AustraliaJune 2015

BrazilMay 2015

CA, USASept. 2015

IndiaOct. 2015

SouthAfricaApril 2015

(m3/m3)0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Root zone soil moisture Drought index

Al bitar


Root zone soil moisture anomaly: India

201520142013

201220112010

JJAS JJAS JJAS

JJAS JJAS JJAS

Al Bitar


Credit: USDA FAS

FOOD SECURITY

Credit: USDA FAS, Soil moisture in southern Africa in mid-April 2014.

SM data used to predict drought and improve crop yieldby US Department of Agriculture, Crop Explorer website:http://www.pecad.fas.usda.gov/cropexplorer/

W Crow USDA


ENHANCING RAINFALL PRODUCTS AND ESTIMATING YIELDS

Land

24

Rainfall estimates using SMOS in Africa

6am

6pm

Soil moisture = precipitation signature Optimisation of the rainfall amount at the event scale

Particle filter assimilation scheme

Rainfall observationSat. prod.Sat. Correted prod.

Cumulative rainfall (Niger – 2011)

T. Pellarin

R=0.98

R=0.51

Crop yield estimates using SMOS in Africa

T. Pellarin

Simulation usingCMORPH

Real-Time Product

Simulation usingCMORPH

Adjusted Product

From 2010, SMOS isexpected to be able to correct for Real-time satellite product to provide better soilmoisture estimates

Crop yield estimates using SMOS in Africa

T. Pellarin


HIGH RESOLUTION SURFACE SOILMOISTURE:IRRIGATION, STRESS, PHYTOSANITARY

Enhanced products

28

Soil Moisture 1 km Morocco

Land Surface Temperature

Optic/Thermic

Soil Moisture

SMOS

1 km / 1 day40 km / 3 days

DisPATCh-SM actual

Soil MoistureSMOS Land Surface Temperature

MODIS (Aqua/Terra)

J. Malbeteau

Soil moisture – Irrigated area

Root zone Soil moisture (average of 145 days)m3/m3

Irrigated area

Irrigated area is well visible

130km

80

km

J. Malbeteau

Marrakesh

SMELLS 1 km v2

• SMOS soil moisture downscaled at 1km (DisPATCh) produced over the entire region (2010 -2015)

• SMELLS 1km can explain Desert Locust (DL) presence

• Current approaches rely on NDVI at 3km

• Introduction of SM increases resolution (1km)SM precedes vegetation by 2 months -> high impact on DL management

M.J. Escorihuela


Time-lag between SMOS water fraction & TRMM rainfall

wee

ks

Time-lag between SMOS water fraction & in-situ discharge at outlet

wee

ks

A. Al Bitar, M. Parens


What L band brings us

Net gain over active or higher frequency data sets for soil moisture and hydrology hence agriculture

Complementary to optical Thermal and radarWhich either see the “envelope” or the structure

ANDLittle sensitivity to cloud/ rain all weather all time (~twice

every 2 days in Europe)Reduced sensitivity to vegetation coverAppreciable penetration depth anticipate!

CaveatsSpatial resolution

But temporal coverageSolutions exist


Summary

Need for soil moisture (and sea surface salinity and thin sea ice and …) measurement continuity

Why L band?Because of its characteristics and inherent qualitiesThe most appropriate tool as shown by all the products

stemming from itTemporal stability and robustness

L band radiometry proof of concept demonstratedUniqueness of the measurements hence

Many science outstanding resultsA very large number of operational or pre operational demonstration

products (only a few were presented)

Very efficient means to reach user’s requirements

BUT …No follow on mission currently Data gapWhile many possibilities are available


Requirements

Thermal infra red

2 wavebands

Better than 50 m – global coverage

Better than 3 day revisit

noon - 2 pm overpass time

Soil moiture

L band radiometry

10-40 km global coverage

Better than 3 day revisit

6 am -6 pm


GENERAL conclusionWater is an issueAgriculturesustainability

We need tools to address this issueSatellites a good option together with other meansUse the right tool

But, though proven, adequate and proven concepts are facing non continuity

NEED L band radiometry and Hight temporal/ spatial resolution IRT

Do not be shy and avoid the easy way outJust improving what we have been doing might not be

the right path old ideas can be re thought (spatial resolution is the Graal)It is not necessarily true the new stuff = more risks


2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 20322008

L-Band radiometry missions

PrecipitationPrecipitationGravity

SMOS (ESA CNES) (40 km / 3days / L-band / global )

SMAP (NASA) (10-60 km / 3days / L-band / global)

Thermal

Color codes

Altimeters

L-Band PassiveOptical

Radar

Thermal

Color codes

Altimeters


Radar

Aquarius (NASA) (100km / 8days / L-Band/ global)

Kerr and Al Bitar


2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 20322008

Thermal missions

LDCM - LandSat8 (NASA) (optical & thermal / 100m / 16 days)

Aster (JAXA NASA) (Thermal/ 100m /local)

Also AVHRR Modis VIRSS etc

Thermal

Color codes

Altimeters


Radar

Precipitation

Thermal

Color codes

Altimeters


Radar

PrecipitationGravity

Kerr and Al Bitar


2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 20322008

Altimetry missions

Jason 1 (CNES NASA)

Altika (CNES ISRO) (P-Band)

Jason 2 (CNES NASA)

Jason 3 (NOAA Eumetsat)

CryoSat2 (ESA) (SAR)

Thermal

Color codes

Altimeters


Radar

Precipitation

Thermal

Color codes

Altimeters


Radar

PrecipitationGravity

SWOT (NASA CNES CSA) (SAR Ka-Band)

Sentinel -3 (ESA) (SAR C-Band)

Jason CS (ESA/Eumetsat/Cnes/Noaa/Nasa)

IceSat-2 (NASA) ) (LiDAR)

AirSWOT

Kerr and Al Bitar


Example of Tropical storm Nov 2013

Rains > 20 mm/h for ECMWF

visit http://www.cesbio.ups-tlse.fr/SMOS_blog/

Richaume


Operational implementation by CapGemini and CESBIO

41Storm risk by NOAA for 07 Oct. 2014 at 12h45

SMOS flood risk on 07 Oct. 2014 at 12h45 for the next 5 days


CLIMATE MONITORING:EL NINO AND LA NINA

Climate

42


El Nino Impact

A. Mahmoodi


Africa South, DscDry 11-03

A. Mahmoodi


Africa (East), AscWet 10-01

A. Mahmoodi


Iran Dsc, MW = 11, RFI <= 1

A. Mahmoodi

josé a. sobrino - copernicus · 2019-07-25 · josé a. sobrino image processing laboratory,...

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