remote sensing of water requirements and water use … sensing of water requirements and water use...

8-4-2014

Challenge the future

Delft University of Technology

Remote Sensing of water requirements and water use in agriculture

Massimo Menenti

2 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014

Overview

Crop water use: water requirements, water productivity and

water shortage

1. Monitoring crop water requirements using multi-spectral

image data

2. Assessing water productivity using multi-spectral and

thermal infrared image data

3. Actual ET land surface energy balance

4. Monitoring water resources: EO + modeling

5. Drought detection and early warning using remote

sensing indicators of crop condition


Irrigation water management (Large Irrigation schemes)

Crop water Requirement (CWR) Net Irrigation water Requirement

(NIWR) Assessment of Irrigation performance

Remote Sensing: Time Series

Remote Sensing: Biophysical Variables

Soil water balance

Irrigation Adequacy and Effectiveness

Crop Water Requirements vs. Water Use


Multispectral satellite data

Time Series

MODELING “SWAP” Analytical Approach Kc-NDVI Method

IP3 IP2

Preprocessing: Geometric, Radiometric, Atmospheric Corrections

Work Flow


Allocation per unit area IP1

Allocation per water

requirements

IP2

Dealing with shortages IP3

Irrigation Performance Indicators

IP1ij

ij ij

i i

V A

V A

/

/

ij

nk

k

ijkk

ijV

AEp

1

10**

2IP

ij

nk

k

ijkkwk

jiV

AEaEa

IP

1

, 10**

3

In which:

Vi = Volume supplied to unit i (m3);

Vij = Volume received at reference unit j,

within higher order unit i (m3);

Ai = Irrigated area in unit i (ha);

Aij = Irrigated area in reference unit j,

within higher order unit i (ha);

ijkA = Area of crop k in unit j (ha);

n = Total number of crops k.

E pk = Potential evapotranspiration of crop k

(mm);

k,wEa = Actual evapotranspiration of crop k

following the application of an irrigation

volume Vij (mm);

kEa = Actual evapotranspiration that would

occur without any irrigation (mm);

8 Water Energy Food Nexus, Rome, Italy 25 – 27 March 2014 8

Hydraulic Basin of

Oum Er-rabia

High service of Irrigated perimeter Ce Low service of Irrigated perimeter

Al Massira Dam

Oum Er-rabia River

Sprinkler Irrigation

Localized Irrigation

Surface Irrigation Corn

Wheat

Sugar Beet Fourrages

Study Area & Data collection


RapidEye (REIS)

Landsat 8 (OLI)

SPOT4 (HRVIR1)

GEOMETRIC CORRECTION: - Projection System: UTM, WGS-84, zone 29. - Topographic map: (1/50 000).

RADIOMETRIC AND ATMOSPHERIC CORRECTION: -Radiometric calibration by conversion of DN values into TOA (Top of Atmosphere) radiance, -The TOC (Top of Canopy) reflectance was obtained using ENVI FLAASH model (Incorporate MODTRAN Model) -The MODTRAN Atmospheric Model based on Latitudinal/Seasonal Dependence of surface Temperature : Mid Latitude Summer/ Tropical. -The Visibility for Clear weather Condition: 40 to 100 Km

SENSOR DATE Area SPECTRAL RESOLUTION (um)

SR ORBIT

SPOT4-HRVIR1 FromJanuary to June2013

FAREGH XS1: 0.500 - 0.590 20m Altitude:832 km revisit: 5 days

XS2: 0.610 - 0.680

XS3: 0.790 - 0.890

SWIR (HRVIR):1.530 -1.750

RapidEye -REIS 12/10/2012 ZEMAMRA SIDIBENNOUR

Blue: 0.440-0.510 5m Altitude:630 km revisit:

Green: 0.520 - 0.590 Daily (off-nadir) ; 5.5 days (at nadir)

02/08/2013 Red: 0.630 - 0.685

Red-Edge: 0.690-0.730

NIR: 0.760-0.850

Landsat 8 - OLI 04/19/2013 SIDIBENNOUR ZEMAMRA

Coastal / Aerosol: 0.433 - 0.453

30 m Altitude:705 km revisit: 16 days

Blue: 0.450 - 0.515

04/26/2013 Green: 0.525 - 0.600

Red: 0.630 - 0.680

06/13/2013 NearInfrared: 0.845 - 0.885

SWIR: 1.560 - 1.660

06/29/2013 SWIR: 2.100 - 2.300

Cirrus : 1.360 - 1.390

07/15/2013 Panchromatic: 0.500 - 0.680

15m

Satellite Data (2012/2013)


10

SPOT4 (HRVIR1) Field Work

31/01/2013 Casier Faregh 18 -19 - 20 Décembre

2012

05/02/2013 Casier Faregh 27 - 28 Fevrier 2013

10/02/2013 Casier Faregh 3 - 4 Avril 2013

25/02/2013 Casier Faregh 23-24 Mai 2013

02/03/2013 Casier Faregh 20 Juillet 2013

17/03/2013 Casier Faregh





RapidEye (REIS)

10/12/2012 Sidi Bennour &

Zemamra

08/02/2013 Sidi Bennour & Zemamra

Landsat8 (OLI)

19/04/2013 Partie du périmétre

26/04/2013 Périmétre irrigué




SPOT4 (HRVIR1)











RapidEye (REIS)


Zemamra


Landsat8 (OLI)






SPOT4 (HRVIR1)











RapidEye (REIS)


Zemamra


Landsat8 (OLI)






Field Campaigns (2012/2013)


<

2.5σ

< 2σ

RMSE=0.99 RMSE=0.86

RMSE=0.89 RMSE=0.79

Validation of ETc estimates

In-situ ETc estimated with the dual kc approach and ETc estimated with the analytical approach (a) respectively the Kc-NDVI method (b)

c cET ET

c cET ET


Water Productivity

GLOBAL-WP model

No separate calculation, directly WP:

All key variables obtained from routine satellite measurements

Ag. Water Man. Zwart et al., 2010


Water Productivity: definitions

4/8/2014

Variable Definition Units

HI harvest index, crop specific -

aNDVI + b

APAR/PAR fraction -

NDVI mean Normalized Difference Vegetation Index -

PAR / SEXO fraction -

SEXO extraterrestrial radiation W m-2

max maximum light use efficiency g MJ-1

T1 Dependence of light use efficiency on air temperature

-

T2 Dependence of light use efficiency on air temperature

-

grain grain water content fraction -

mean broadband surface albedo -

135 estimate of atmospheric absorption and scattering W m-2


SEBAL for Water Productivity

7 countries, 8 projects the same methodological framework was applied

water productivity in wheat dominated areas


Definition of WPS


Wheat water productivity

16


Remote Sensing of actual ET

• Soil water balance

• Surface energy balance

• Water vapour balance atmospheric column

• Vegetation control of leaf level transpiration

• Constrained by available energy: Rn, Penman – Monteith,

Priestley - Taylor

Rate – limiting process vs. state variables?


Parameterization of canopy conductance

(MOD – 16)

(Mu et al., 2007 and 2011)

Penman – Monteith

Transpiration limited by response of canopy conductance to vapour pressure deficit and minimum air temperature

Transpiration scaled by leaf area

Evaporation calculated independently from net radiation at the soil

Soil water availability is not included in the ET algorithm


Parameterization with observations of

state variables: soil water content

(Miralles et al., 2011)

Soil water balance model

Priestley – Taylor: P – M for potential ET + assumptions to collapse aerodynamic into adjusted radiative term

State variable: root zone soil water content

Top soil water content adjusted by assimilating microwave observations of soil moisture

Stress factor: normalized soil water deficit

E = S x Ep (plus interception)


Parameterization with observations of state

variables: Land Surface Temperature

(Menenti, 2001)

Residual energy balance

State variable: Land Surface Temperature (LST)

Scale ET vs LST based on context

Scale ET vs LST based on (NDVI, LST)

Scale ET vs LST based on Penman – Monteith

Separate parameterizations of transpiration (leaves) and of evaporation (soil)

Dual source models with observations of foliage and soil temperatures


SEB: Outstanding Issues

• LE scales with LST IF radiative and convective forcing is normalized first

• Additional constraints needed to solve SEB

• Additional equations by segmenting images and assuming some parameters (e.g. ra) constant within the segment

• Add experimental constraints by using limiting cases (reference system states)

• Dry and wet reference states assumed to exist within image (SEBAL)

• Dry and wet reference states evaluated from theory (SEBI SEBS MSSEBS)


Multi-Scale Energy Balance • SEBI based

• Physical calculation of the extreme

boundaries following the SEBS approach

(Menenti and Choudhury, 1993; Su,

2000)

• Calculation grid size depends on the

length scale of physical processes and

ABL development (MSSEBS approach,

Colin 2006)

• Expected results:

• 1km resolution Surface Energy

Balance components

• Time series on a week to 10 days

basis

• Automated processing chain,

including interface with data

providers & results repositories

Meso-scale Atmospheric Forcing grid [15-100 km]

Ta, q, u, v, p

Sw, Lw incoming radiance

ABL Calculation grid [10 x ABL height]

Full resolution calculation grid [TIR Resolution]

LST, albedo, fc, LAI, emissivity, DEM


Effect of spatial correlation in gap filling


Frequency and spatial distribution of gaps LST: FY-2 hourly TIR data


Daily ET


Daily ET 2008 – 2010


Daily ET: how accurate?


Gaps ground measurements


ETest vs. ETec: Error statistics

Daily ET with Ef assumption, not hourly ET


Model domain

• Model extent 7,221,600 km2 (Qinghai-Tibet Plateau 2,527,166 km2)

• 5 x 5 km spatial resolution

• Simulated period: 2008-2010, daily time step

0 200 400 600 800 1000km

Indus

Ganges-Brahmaputra

Salween Mekong

Yangtze

Yellow


Integration of data

CEOP-AEGIS WP8: FutureWater, ARIESpace, IGSNRR, NIT Rourkela

WP 5: Precipitation

WP 6: Snow and ice

Model forcing

WP 4: Soil moisture

Model calibration / validation

Rescale EO data to model resolution

Prototype water balance monitoring system

Water balance and yield of the entire TP

WP 3: ET

River discharges

Monitoring the water balance

and water yield of the

Plateau

2008-2010

Air Temperature


Water balance 2008-2010

P

1260

Major draining basins Tibetan Plateau (mm/yr)

ET

300

Base flow 360

ΔS +30


Annual water yield 2008-2010

Indus 45 km3

Ganges-Brahmaputra

257 km3

Salween 90 km3

Mekong 97 km3

Yangtze 608 km3

Yellow 232 km3


Drought Detection and Early Warning

Water use

Damage on ecosystem

Crop yield

Dro

ug

ht

evo

luti

on

pro

ce

ss

es

Cause

Drought impact severity

Impact evaluation


Drought Remote Sensing Indicators

LST anomaly: Δ LST = LST – LSTmean

Normalized Temperature Anomaly Index (NTAI): N_ΔLST = Δ LST / (LSTmax - LSTmin)

(Jia et al.

2011) LST as a drought indicator

NDVI as a drought indicator

NDVI anomaly : Δ NDVI = NDVI – NDVImean

Normalized Vegetation Anomaly Index (NVAI) : N_ΔNDVI = Δ NDVI / (NDVImax - NDVImin)


Comments

• Assessment and monitoring of crop water requirements is mature

for operational use, multiple data sources available (Landsat 8,

Sentinel 2)

• Water use, water productivity, water resources, water scarcity:

substantial contributions of satellite data

• SEB data products by combining multi-spectral observations of

the land surface and high spatial resolution CBL fields

• GEO – satellites provide redundant time series with sparse but

sufficient coverage in space and time for daily SEB data products

• GEO observations avoid assumptions on the Evaporative Fraction

• LEO – satellites should be used as a virtual SEB constellation

remote sensing of water requirements and water use … sensing of water requirements and water use...

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