Remote Sensing Methods for operational ET determinations in the NENA region, Christopher Neale
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Transcript of 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
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Corn 2011
Soybeans 2012
Soybeans 2012
Soybeans 2012
Soybeans 2012
Soybeans 2012
Soybeans 2012
Soybeans 2012
Soybeans 2012
Soybeans 2012
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
Corn 2013
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