remote sensing methods for operational et determinations in the nena region, christopher neale

Christopher Neale, Isidro Campos Water for Food Institute

University of Nebraska

• Discuss different remote sensing based models and

approaches for estimating evapotranspiration of vegetated

land surfaces

• Present preliminary results of a model inter-comparison

• Discuss applications using some of these models

Crop coefficient and reference ET: • Reflectance-based crop coefficient models where

vegetation indices are related to ET crop coefficients. Relationships are typically crop specific. Uses shortwave (Visible, NIR) bands of satellite instruments.

Energy balance models: • One layer models examples: empirical models (OLEM),

SEBS, SEBAL, METRIC, SSEBop

• Two-source models, ALEXI-DisALEXI

• Detailed Process models

Energy balance models require the use of both the thermal infrared and the visible/near-infrared bands

Hybrid Methodologies

Reflectance-based crop Coefficients

Are obtained by linearly relating the NDVI or SAVI of bare soil with the NDVI or SAVI at

effective full cover the point of maximum ET on a crop coefficient curve

Effective full cover occurs at LAI varying from 2.7 to 3.5 depending on the crop and with

percent cover around 80%, although this assumption is currently under review

SAVI and NDVI are vegetation indices estimated from Red and Near-Infrared bands of

satellite, airborne sensor or ground radiometers

Neale et al, 1989; Bausch and Neale 1989

Model overview: RS-Soil Water Balance

• Maintain a soil moisture budget in the root zone of the crop accounting for all water inputs and outputs.

• The following equation is written in terms of depletion, which is equal to zero when the soil moisture is at the soil’s field capacity value and becomes positive as the moisture is extracted

Di = Di-1 + ETa + DP - Pe - Ii – CR

• Di is the soil moisture depletion on day i,

• ETa is the actual crop evapotranspiration

• DP is deep percolation of water below the root zone

• Pe is the effective precipitation infiltrated into the root zone

• Ii is the infiltrated irrigation into the root zone and

• CR is capillary rise of water into the root zone from a nearby water table.

Model overview: RS-Soil Water Balance

Wright, 1982

Corn, 1982 The Kcb

represents the

average ET from

plant transpiration

with a dry soil

background and

no limitation of

soil moisture in

the root zone of

the crop (From

FAO56)

Models overview: RS-Soil Water Balance ETa = Kc .ETr,0 Kc = Kcbrf * Ks + Ke

Evolution of Reflectance-based Crop coefficient

Corn, 2010

Models overview: RS-Soil Water Balance

Use of Soil Adjusted Vegetation Index (SAVI) for Reflectance-based

Crop Coefficient 2013: Corn

Use of SAVI for Reflectance-based Crop Coefficient

Corn 2011

Corn 2011

Soybeans 2012

Corn 2013

Model Evaluation at Mead, NE experimental fields

RS-Soil water balance

• Field data analyzed:

• ET measurements in Mead 1 (irrigated corn) and

Mead 2 (irrigated soybeans) during the 2012

growing season

• Available data:

Eddy covariance ET, H, Rn and G fluxes

Irrigation applied

Temporal evolution of reflectance based Kcb

• Model evaluation:

• General good agreement with RMSE<1 mm/day

for both crops in both seasons

• Low levels of water stress in spite of the irrigation

applied

Mead 1, Corn

Mead 2, Soy

Re-analyzing the analytical approach to convert VI in crop

coefficients for irrigation management.

Application for ET and Irrigation assessment for Corn and Soybeans

RMSE< 1.1 mm RMSE< 1.0 mm RMSE<1.5 mm

Actual Irrigation

Irrigation requirements

Actual Irrigation

Irrigation requirements

Satellite/Sensor Time Resolution Image size Spatial resolution

Landsat 8 LDCM 16 days 185 km x 185 km 30-100 m

Landsat 7 ETM+ 16 days 185 km x 185 km 30-60 m

DMC constellation Up to daily revisit Up to 600 x 600 km Up to 20 m

Sentinel-2 15 days 290 km x 290 km 10 m

IRS-AWIFS-P6 6 days 740 x 740 km 56 m

IRS LISS III-1C 24 days 142km x 142km 23 m

IRS LISS III-1D 25 days 148km x 148km 23 m

CBERS CCD 26 days 113km x 113 km 20 m

SPOT 5 Up to daily revisit 60 km x 60 km 10 m

FORMOSAT Up to daily revisit 24 km x 24 km 8 m

Rapid eye Up to daily revisit 25 km x 25 km 5 m

IKONOS 3 days 13 km x 13 km 4 m

QUICKBIRD 1-5 days 16.5 km x 16.5 km 2.44 m

Operational EO satellites with medium to high spatial resolution

ENERGY BALANCE MODELS BASED ON

EARTH OBSERVATION DATA

• Are based on the solution of the energy balance equation using remote sensing to estimate some of the components.

• Net radiation is partitioned and used by different processes at the surface.

Rn = LE + H + G + P + ΔS

• LE is the latent heat flux or evapotranspiration, or energy used to

evaporate water

• H is the sensible heat flux or energy used to heat the air

• G is the soil heat flux or energy used to heat the soil

• P is energy used in photosynthesis (small component and typically ignored)

• ΔS is the energy stored within a very dense and tall vegetation canopy (only a factor for dense, tall forest vegetation)

Typically remote sensing is used in the estimation of Rn, H and G and LE is obtained as a residual

One Layer Energy Balance Model

441 ssaasn TTRR NIR0.418 + RED0.512=

7/1/*6/*2sin*06.022.1*1 aaa Temoclfclf

Gcorn, soy = {[(0.3324 + (-0.024 LAI)) (0.8155 + (- 0.3032 ln (LAI)))] Rn}

LE = Rn - G - H

)(

)1)((

LREDNIR

LREDNIROSAVI

H = a Cpa (Taero – Ta) / rah Ground Measured Data [Ta, U, Rs]

L = 0.16

LAI_air = (4 * OSAVI – 0.8)* (1 + 4.73E-6 * EXP [15.64 * OSAVI])1

LAI_sat = (2.88 * NDWI + 1.14)* (1 + 0.104 * EXP [4.1 * NDWI])1

hc_CORN air = (1.86 * OSAVI – 0.2)* (1 + 4.82E-7 * EXP [17.69 * OSAVI])1

hc_SOY air = (0.55 * OSAVI – 0.02)* (1 + 9.98E-5 * EXP [9.52 * OSAVI])1

Taero = [(0.534 Ts_RS) + (0.39 Ta) +

(0.224 LAI_RS) – (0.192 U) + 1.67]

G alfalfa = (038 * EXP [-1.65 * NDVI]) * Rn

1Anderson, M.C., C.M.U. Neale, F. Li, J.M. Norman, W. P. Kustas, H. Jayanthi, and J. Chavez, (RSE Vol. 92, pp. 447-464 2004)

Brest and Goward (1987)

Brutsaert (1975); Crawford and Duchon, 1999

hc_CORN sat = (1.20 NDWI + 0.6) (1 + 4.00E-2 EXP [5.3 NDWI])1

hc_SOY sat = (0.5 NDWI + 0.26) (1 + 5.0E-3 EXP [4.5 NDWI]) 1

)(

)(

SWIRNIR

SWIRNIRNDWI

Chavez et al, (2005)

Neale et al, (2005)

Chavez et al, (2005)

Surface Aerodynamic Resistance (rah) Iterative

Procedure based on the Monin-Obukhov Method

om

m

Z

dZLn

Uu

*

H = a Cpa (Taero – Ta) / rah kU

Z

d-Z

Z

d-Z

=r 2

oh

m

om

m

ah

lnln

Taero_RS

Hkg

CTuL apaa

OM

3

*

_

4

1

_

*161

OM

m

L

dZx

2

1*2

2xLnh

2tan*2

2

1

2

1*2

2

xa

xLn

xLnm

OM

om

m

OM

m

m

om

m

L

Z

L

dZ

Z

dZLn

Uu

__

*

*

__

u

L

Z

L

dZ

Z

dZLn

rOM

ohh

OM

mh

oh

m

ah

If rah_i-1 = rah_i

Zom = 0.123 hc

Zoh = 0.1 Zom

d = 0.67 hc

Instantaneous R.S. LE to daily ET

ETd = [EF (Rn – G)d] x [cf / v w]

EF = LEi / (Rn – G)i

Latent Heat Flux

LE = Rn – G – H

ETd = Daily or 24 hours evapotranspiration rate, mm d-1

(Rn – G)d = Measured mean 24 hr available energy, W m-2

cf = Time (unit) conversion factor equal to 86400 s d-1,

v = Latent heat of vaporization, W s kg-1

w = Density of Water, kg m-3

Identification and Review of Remote Sensing ET Models

• Review and selection of ET models includes:

– Selected models • ALEXI/DisALEXI/ TSEB (Anderson et al., USDA-ARS-HRSL)

• METRIC (Allen et al., University of Idaho)

• SEBS (McCabe et al., KAUST)

• SSEBop (Senay et al., USGS)

• Hybrid ET (Geli et al., Utah State University)

• PT-JPL (Fisher et al., NASA-JPL)

• ReSET (Elhaddad et al., Colorado State University)

• P-M / MODIS ET (Mu et al., University of Montana)

– Review report will provide

– Types of models based on methodology and application.

– Review of each candidate model algorithm.

– Comments on the required input data of each.

– Identify possible sources of uncertainties for each.

Selected Test Sites

Irrigated Agriculture

Palo Verde Irrigation District (PVID),

CA

Semi-arid Natural Vegetation

Walnut Gulch Experimental Watershed, AZ

Irrigated and Rain-fed

Agriculture

Mead, NE

Study Area (Site 1)

62

Palo Verde Irrigation District (PVID), CA

Location: Imperial and Riverside counties, CA.

Area: more than 500 km2.

Elevation: 67 m at South to 88 m at North.

Cover: Predominant crops: alfalfa (90 %), cotton

(5%), grains and mixed vegetables (5%).

Available data: Inflows, Outflows, Groundwater

Flux Towers, Ag and Riparian, Classification,

GIS Layers, TM Imagery, Data for 2007-2009

Requested/Expected Results from Modelers

Comparison of actual daily ET (mm/day) during summer of 2008 based on TSEB model

May 17 May 26 June18 July13 July 29 May 10

1. Estimates of surface energy balance fluxes (if any) and daily actual ET during satellite overpass

dates in terms of individual images. Including discerption of extrapolation method from

instantaneous to daily values of ET.

2. Estimates of total daily actual ET for the entire area for the entire year of 2008. only tabulated value

is needed.

0.00

5.00

10.00

15.00

20.00

25.00

1 31 61 91 121 151 181 211 241 271 301 331 361

Infl

ow

and o

utf

low

mm

/day

Day of year 2008

Inflow + Outflow

64

METRIC DisALEXI ReSET

SEBS SSEBop

Estimates of actual ET for May 10th , 2008 (DOY 131)

PT-JPL

HS

H = HS+ HC

HC

TS

TAC

TA

TC RX

TRAD() = f(TS,TC, C())

Prognostic Modified FAO-564 water balance of the root zone

ETa

P Irr.

D

P CR

RO

FC

PWP

Diagnostic SVAT Scheme

The Two-Source Energy

Balance Model (TSEB)2,3

Series Resistance Formulation

LE = Rn – G – H

Modified with reflectance -

based basal crop

coefficient (Kcbrf)5

2 Norman and Kustas (1995), 3Li , et al.(2005)

1Neale et al. (2012), Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach.

Advances in Water Resources.

4 Allen et al. (1998), 5Neale et al. (1989)

ETa = Kc .ET0 Kc = Kcbrf . Ka + Ke

TRAD(q) ~ fc(q)Tc + [1-fc(q)]Ts

(two-source approximation) Norman, Kustas et al. (1995)

Provides information on soil/plant fluxes and stress

TRAD(q) ~ fc(q)Tc + [1-fc(q)]Ts

Accommodates off-nadir thermal sensor view angles

Treats soil/plant-atmosphere coupling differences explicitly

Two-Source Energy Balance Model (TSEB)

RN

System and Component Energy Balance

= H + E + G

RNC = HC + EC

RNS = HS + ES + G

= = =

+ + +

TS

TC TAERO

SY

ST

EM

C

AN

OP

Y

SO

IL

Derived fluxes

Derived states

TRAD

0

2

4

6

8

10

02/09/02 04/09/02 06/09/02 08/09/02 10/09/02 12/09/02

ET (

mm

/d

ay)

Time (days)

The Hybrid Model1

1Neale et al. (2012), Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach. Advances in Water Resources.

The Spatial ET Modeling Interface (SETMI)1

1 Geli, H. M. E. and C.M.U. Neale, (2012), Spatial evapotranspiration modeling (SETMI),

Proc. IAHS 352, Remote Sensing and Hydrology (September 2010), ISSN 0144-7815

Models evaluation at Mead experimental fields

RS-Two Source Energy Balance

• Model evaluation:

• Good agreement for measured and modelled

instantaneous LE data (11:00 am) RMSE=66.7 W/m2 for mean values around 300 W/m2

• Better agreement for measured and modelled

daily ET data RMSE=0.4 mm/day for mean values around 5 mm/day

• Data post-processing:

• Energy balance closure of daily and hourly Eddy

data based on the Bowen ratio methodology

(Twine et al. 2000)

• Conversion of instantaneous to daily LE data

based on the evaporative fraction, LE/(Rn+G)

Temporal evolution of Kcb for Soy and Corn in the LN. (Analyzed area>18000 ha.)

PRELIMINARY RESULTS OF WATER BALANCE APPROACH IN NEBRASKA

Comparison of Simulated net irrigation requirements and actual irrigation (>200 fields

per year)

Analysis of the relationship between Yield (grain) and Actual Irrigation over Simulated

Irrigation Necessities.

PRELIMINARY RESULTS OF WATER BALANCE APPROACH IN NEBRASKA

Under-Irrigation Over-Irrigation

FINAL THOUGHTS

• Remote sensing based ET models have matured to be fairly accurate for regional and

global applications

• Near-real time applications for irrigated agriculture are now possible

• These models are continuously being improved and becoming more accurate

• Data fusion using multiple sources of remote sensing imagery at different pixel resolutions

will provide more continuous inputs and reduce the data gaps due to clouds

• Crop coefficient model is a viable interpolation scheme for agricultural crops

WHAT WE ARE PROPOSING FOR THE NENA REGION

• Use of ALEXI energy balance model to obtain daily surface ET at 375 m resolution from

the VIIRS Satellite Instrument

• This ET product will be used for drought early warning estimates, and water accounting in

watersheds and river basins

• Disaggregate ET using DisALEXI and SEBAL 3.0 models for field scale water productivity

estimates (crop yield and actual ET)

www.gwpforum.org

THANK YOU

remote sensing methods for operational et determinations in the nena region, christopher neale

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