ground validation of crop water productivity: developing a protocol, christopher neale
TRANSCRIPT
Christopher Neale Director of Research
Daugherty Water for Food Institute
at the University of Nebraska
• Ground data for validating each component of water
productivity: crop yield and transpiration (evapotranspiration)
• Data needed preferably at field scales to integrate and match
remotely sensed information at pixel level
• Multi-year records under different climatic conditions (normal,
drought, wet)
• Irrigated and dryland crops
• Data from automated weather stations that estimate reference
evapotranspiration using Penman-Monteith equation, their
location and surrounding vegetation
• Historical records from these weather stations
• Evapotranspiration data for different regional crops from
lysimeters if available and the systems are well managed
• Energy Balance Fluxes from Eddy Covariance or Bowen Ratio
flux towers, their location and surfaces they represent
• Water flow measurements in irrigation systems (canal inflows,
lateral canal flows, drainage and operational spills,
groundwater levels etc.) to establish a water balance
From: NEBRASKA WATER AND ENERGY FLUX MEASUREMENT, MODELING, AND RESEARCH NETWORK (NEBFLUX) by Suat irmak
-150
-50
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150
250
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-150 -50 50 150 250 350
Observed H (W m-2)
OL
EM
pre
dic
ted
H (
W m
-2)
d
d
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn-150
-50
50
150
250
350
-150 -50 50 150 250 350
Observed H (W m-2)T
SM
pre
dic
ted
H (
W m
-2)
d
d
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
Results: Energy Balance Models Using Remote Sensing
(closure forced with the residual method)
Compared with Eddy Covariance flux tower measurements
Sensible Heat Flux (H)
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400
500
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800
200 300 400 500 600 700 800
Observed LE (W m-2)
OL
EM
pre
dic
tedL
E(W
-2)
dd
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
200
300
400
500
600
700
800
200 300 400 500 600 700 800
Observed LE (W m-2)T
SM
pre
dic
ted
LE
(W
m-2
) d
d
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
Latent Heat Fluxes
50
150
250
50 150 250
Observed daily LE (W m-2)
OL
EM
pre
dic
ted
daily L
E (
W m
-2)
d
d
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn50
150
250
50 150 250
Observed daily LE (W m-2)T
SM
pre
dic
ted
daily L
E (
W m
-2)
d
d
174 soy
182 soy
189 soy
174 corn
182 corn
189 corn
Daily Evapotranspiration Integrated
Using the Evaporative Fraction
• Total grain yield or production for individual fields
• Spatial yield from GPS yield monitors on harvesting equipment
(would be fantastic!)
• Representative biomass, leaf area index for individual fields
and different crops
• Aggregated yield statistics by county and crop type
• Crop classification layers at field scales by season
Measurements of concurrent biomass and leaf area index and other
canopy biophysical parameters along with ET measurements
Experimental analysis in corn (C4) and soybeans (C3) in eastern Nebraska
• Quality of seeds
• Lack of inputs (fertilizers) or micro-financing to purchase inputs
• Inappropriate agricultural practices
• Low value of crops or lack of accessibility to markets
• Water deficit in rainfed areas, over or under irrigation
• Poor infrastructure (roads, maintenance of irrigation systems)
• Poor water management of irrigation systems
• Low soil fertility, depleted organic matter
16
Northeastern Nebraska Corn/Soybean Rotation
2013 2014
Many satellite-based evapotranspiration and yield models require the knowledge
of the crop type at the surface
Source: USDA Natural Resource
Conservation Service
(http://websoilsurvey.sc.egov.usda.gov/Ap
p/WebSoilSurvey.aspx)
Map of water holding capacity in the 1st m. profile RGB color composition, L8 Date 07/19/2913
Variables include: Soil type, texture, depth,
layers, water holding capacity, infiltration
rates, organic matter content etc.
ICBA-MOA, Qatar Training Course May 15-18, 2011, Doha, Qatar International Center for Biosaline Agriculture, Dubai, UAE
Soil moisture measurements in Tunisia
Soil Water Content 0-60 cm
80
100
120
140
29-Jun 30-Jun 1-Jul 2-Jul 3-Jul 4-Jul 5-Jul
SW
C (
mm
)
Depletion of soil water content during daytime from ET
Source: Makram Belhaj Fraj and Ian McCann (ICBA)
Example of Use of Water Balance Data of an Irrigated
Area for verifying remote sensing based ET models
Palo Verde Irrigation District, CA
20 Saleh Taghvaeian* and Christopher M. U. Neale. 2011. Water balance of irrigated areas: a remote sensing approach.
Hydrological. Process. (2011) Published online in Wiley Online Library, (wileyonlinelibrary.com) DOI: 10.1002/hyp.8371;
21
Water balance of irrigation schemes
I + P = ET + DP + RO + ΔS
I: Applied irrigation water;
P: Precipitation;
ET: Evapotranspiration;
DP: Deep percolation;
RO: Surface runoff; and,
ΔS: Change in soil water storage.
23
Remote Sensing of Energy Balance
Rn = H + G + LE
Rn: Net Radiation
H: Sensible Heat Flux
G: Soil Heat Flux
LE: Latent Heat Flux (Evapotranspiration)
Surface Energy Balance Algorithm for Land (SEBAL)
Developed by Dr. Wim Bastiaanssen, Wageningen, The Netherlands
25 Total volume of water consumption by PVID crops for 20 dates of Landsat overpass
based on SEBAL estimates of evapotranspiration
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0
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J-08 M-08 M-08 J-08 S-08 N-08 J-09D
ail
y A
ver
age
Flo
w R
ate
(cf
s)
Main Canal
Outfall Drain
Operational Spills
Surface Water Inflows and Outflows
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0
3
6
9
12
15
18
J-0
8
F-0
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M-0
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A-0
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M-0
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J-0
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J-0
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A-0
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S-0
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O-0
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N-0
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D-0
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J-0
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F-0
9
Dep
th (
mm
) Precip.
Inflow
0
3
6
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J-0
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F-0
8
M-0
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A-0
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M-0
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J-0
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J-0
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A-0
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S-0
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O-0
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N-0
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D-0
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J-0
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F-0
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Dep
th (
mm
)
ETa
Outflow
Depth (mm) Percentage
Precipitation 71 3
Surface inflow 2479 97
Σ Inputs 2550 100
Canal Spills 284 11
Drainage 998 39
Evapotranspiration 1286 50
Σ Outputs 2568 100
Σ Inputs – Σ Outputs -18 -0.7
Closing the Water Budget
29
Depleted fraction (DF)
- DFg = ETa / (Pg + Vd)
- DFn = ETa / (Pg + Va)
0.0
0.2
0.4
0.6
0.8
1.0
DF
DFg
DFn Nilo Coelho:
DFn = 0.60
PVID:
DFn = 0.55
Estimation of System Performance Indicators
• Allows for checking remote sensing based ET models over
larger scales
• Diversions into main canal and later canals are useful even if
no drainage or groundwater levels are measured
Analysis of the relationship between Yield (grain) and Actual Irrigation over Simulated
Irrigation Requirements.
FIELD WATER BALANCE APPROACH USING RS: PRELIMINARY RESULTS IN NEBRASKA
Under-Irrigation Over-Irrigation
Find the local champion in Doukalla Irrigation Scheme, Morocco
Farmer Ahmed is with 1.33 kg/m3 the most productive Courtesy of Dr. Wim Bastiaanssen
Standardization by crop zones
CV=0.41
CV=0.30
CV=0.27
CV=0.21
CV=0.17
CV=0.13
CV=0.08
Courtesy of Dr. Wim Bastiaanssen
• Identify high and low end users in different agricultural regions
• Work with country government agencies, regional and local
water management and agricultural agencies
• On the ground visits to interview farmers and identify sources
of problems, farmers with good practices
• Identify technical solutions and policy changes that will
improve local agriculture production and water management
practices
• Implement practices through training, demonstrations, change
of governance structure etc.
Christopher Neale Director of Research
Daugherty Water for Food Institute
at the University of Nebraska
• Based on the VIIRS (Visible Infrared Imaging Radiometer Suite)
Satellite Instrument – Launched in 2013, expected lifetime is 15 years
• Uses thermal infrared and shortwave bands of VIIRS
• Daily global coverage with improved spatial resolution (375 m) over
MODIS (250 m, 1000 m)
• ALEXI (Atmospheric Land Exchange Inverse model) remote sensing
based surface energy balance model
• To be run at the University of Nebraska-Lincoln supercomputer center
for the lifetime of the VIIRS instrument (approximately 15 years)
• Partners:
Dr. Martha Anderson, USDA-ARS Hydrology Laboratory, Beltsville
Dr. Christopher Hain, NOAA/University of Maryland
ICBA, NDMC at UNL, CALMIT at UNL, UNESCO-IHE
Funding: USAID, WFI, FAO
• Water balance of River basins and watersheds => water
accounting
• Drought early warning systems – ESI evaporative stress index
Composite Drought Index
• Upper boundary condition for downscaling of ET to higher
spatial resolutions to allow for estimates of crop water use and
water productivity
• Crop water productivity (Kg/m3, g/m2)
• Will require downscaling of daily ET product using DisALEXI
model, or SEBAL 3.0 using Landsat Thematic Mapper Satellite
Imagery and other higher resolution systems
• Initially produced for selected irrigated areas of participating
countries
• Partners:
Dr. Martha Anderson, USDA-ARS Hydrology Laboratory, Beltsville
Dr. Christopher Hain, NOAA/University of Maryland
Dr. Wim Bastiaansen, UNESCO – IHE
Funding: WFI, FAO, USAID
• Joint venture, multiple countries
• Ground verification data needs to be, collected, analyzed
and interpreted jointly so the learning is mutual
• Training can be provided on QC and analysis of data,
interpretation
• Joint publications
• Solutions need to be pursued with appropriate government,
regional and local agricultural and water management
agencies
Collaborative approach is the best way to obtain impact on
the ground and meaningful change
Products needs to be verified in the field, to allow for
feedback, model modifications and improvements