ground validation of crop water productivity: developing a protocol, christopher neale

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Christopher Neale Director of Research Daugherty Water for Food Institute at the University of Nebraska

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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

Typically provide hourly averages of weather parameters and ET0

4 sets of soil heat flux plates

distributed in rows and furrows

Corn Soybean

From: NEBRASKA WATER AND ENERGY FLUX MEASUREMENT, MODELING, AND RESEARCH NETWORK (NEBFLUX) by Suat irmak

-150

-50

50

150

250

350

-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)

200

300

400

500

600

700

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

2013

2014

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

www.yieldgap.org

• 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

15

Provided by USDA NASS, based on Landsat Thematic mapper and other satellite image data

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.

22

Over 260 piezometers

1 mile by 1 mile grid

Monthly measurements

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

24

EToF

25 Total volume of water consumption by PVID crops for 20 dates of Landsat overpass

based on SEBAL estimates of evapotranspiration

26

Precipitation

27

0

400

800

1200

1600

2000

2400

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

28

0

3

6

9

12

15

18

J-0

8

F-0

8

M-0

8

A-0

8

M-0

8

J-0

8

J-0

8

A-0

8

S-0

8

O-0

8

N-0

8

D-0

8

J-0

9

F-0

9

Dep

th (

mm

) Precip.

Inflow

0

3

6

9

12

15

18

J-0

8

F-0

8

M-0

8

A-0

8

M-0

8

J-0

8

J-0

8

A-0

8

S-0

8

O-0

8

N-0

8

D-0

8

J-0

9

F-0

9

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

Courtesy of Dr. Wim Bastiaanssen

Water Productivity Score – Continental Wheat

Courtesy of Dr. Wim Bastiaanssen

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

www.gwpforum.org

THANK YOU