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![One Layer Energy Balance Model
44
1 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
LREDNIR
OSAVI
H = a Cpa (Taero – Ta) / rahGround 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
SWIRNIR
NDWI
Chavez et al, (2005)
Neale et al, (2005)
Chavez et al, (2005)](https://image.slidesharecdn.com/14etfromrsforthenenaregioncneale-151116063411-lva1-app6892/75/Remote-Sensing-Methods-for-operational-ET-determinations-in-the-NENA-region-Christopher-Neale-57-2048.jpg)
![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](https://image.slidesharecdn.com/14etfromrsforthenenaregioncneale-151116063411-lva1-app6892/75/Remote-Sensing-Methods-for-operational-ET-determinations-in-the-NENA-region-Christopher-Neale-59-2048.jpg)
![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)](https://image.slidesharecdn.com/14etfromrsforthenenaregioncneale-151116063411-lva1-app6892/75/Remote-Sensing-Methods-for-operational-ET-determinations-in-the-NENA-region-Christopher-Neale-66-2048.jpg)
Uploaded byNENAwaterscarcity
Remote Sensing Methods for operational ET determinations in the NENA region, Christopher Neale
The document discusses various remote sensing models for estimating evapotranspiration of vegetated land, presenting model inter-comparisons and their applications. It outlines energy balance methods, reflectance-based crop coefficients, and soil water balance modeling to derive accurate ET assessments for crops like corn and soybeans. Evaluation of models such as TSEB is included, demonstrating their effectiveness in describing ET through the use of satellite data and highlighting discrepancies between estimated and actual field measurements.
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Remote Sensing Methods for operational ET determinations in the NENA region, Christopher Neale
- 1. Christopher Neale, IsidroCampos Water for Food Institute University of Nebraska
- 2. • Discuss differentremote 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
- 3. Crop coefficient andreference 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
- 4. Reflectance-based crop Coefficients Areobtained 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
- 5. • Maintain asoil 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
- 6. Wright, 1982 Corn, 1982The 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
- 7. Evolution of Reflectance-basedCrop coefficient Corn, 2010 Models overview: RS-Soil Water Balance
- 8. Use of SoilAdjusted Vegetation Index (SAVI) for Reflectance-based Crop Coefficient2013: Corn
- 9. Use of SAVIfor Reflectance-based Crop Coefficient Corn 2011
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- 53. Model Evaluation atMead, 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
- 54. Re-analyzing the analyticalapproach 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
- 55. Satellite/Sensor Time ResolutionImage 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
- 56. ENERGY BALANCE MODELSBASED 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
- 57. One Layer EnergyBalance Model 44 1 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 LREDNIR OSAVI H = a Cpa (Taero – Ta) / rahGround 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 SWIRNIR NDWI Chavez et al, (2005) Neale et al, (2005) Chavez et al, (2005)
- 58. Surface Aerodynamic Resistance(rah) Iterative Procedure based on the Monin-Obukhov Method om m Z dZ Ln U u * H = a Cpa (Taero – Ta) / rah kU Z d-Z Z d-Z =r 2 oh m om m ah lnln Taero_RS Hkg CTu L apaa OM 3 * _ 4 1 _ *161 OM m L dZ x 2 1 *2 2 x Lnh 2 tan*2 2 1 2 1 *2 2 xa x Ln x Lnm OM om m OM m m om m L Z L dZ Z dZ Ln U u __ * * __ u L Z L dZ Z dZ Ln r OM oh h OM m h oh m ah If rah_i-1 = rah_i Zom = 0.123 hc Zoh = 0.1 Zom d = 0.67 hc
- 59. Instantaneous R.S. LEto 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
- 60. Identification and Reviewof 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.
- 61. Selected Test Sites IrrigatedAgriculture Palo Verde Irrigation District (PVID), CA Semi-arid Natural Vegetation Walnut Gulch Experimental Watershed, AZ Irrigated and Rain-fed Agriculture Mead, NE
- 62. Study Area (Site1) 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
- 63. Requested/Expected Results fromModelers Comparison of actual daily ET (mm/day) during summer of 2008 based on TSEB model May 17 May 26 June18 July13 July 29May 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 Inflowandoutflowmm/day Day of year 2008 Inflow + Outflow
- 64. 64 METRICDisALEXI ReSET SEBS SSEBop Estimatesof actual ET for May 10th , 2008 (DOY 131) PT-JPL
- 65. 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
- 66. 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)
- 67. RN System and ComponentEnergy Balance = H + E + G RNC = HC + EC RNS = HS + ES + G = = = + + + TS TCTAERO SYSTEMCANOPYSOIL Derived fluxes Derived states TRAD
- 68. 0 2 4 6 8 10 02/09/02 04/09/02 06/09/0208/09/02 10/09/02 12/09/02 ET(mm/day) 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.
- 69. The Spatial ETModeling 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
- 70. Models evaluation atMead 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)
- 71. Temporal evolutionof 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)
- 72. Analysis ofthe relationship between Yield (grain) and Actual Irrigation over Simulated Irrigation Necessities. PRELIMINARY RESULTS OF WATER BALANCE APPROACH IN NEBRASKA Under-Irrigation Over-Irrigation
- 73. FINAL THOUGHTS • Remotesensing 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
- 74. WHAT WE AREPROPOSING 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)
- 75.