smart irrigation technologies for irrigation water management

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Smart Irrigation Technologies for Irrigation Water Management Christopher Neale, Director of Research Daugherty Water for Food Global Institute

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Smart Irrigation Technologies for Irrigation Water

Management

Christopher Neale, Director of Research

Daugherty Water for Food Global Institute

Outline and Topics Covered

• Geospatial technologies for managing variable rate irrigation

• Infield Sensors

• Estimation of Evapotranspiration and Biomass using Satellite Remote Sensing

• Global Evapotranspiration Product

Variable Rate Irrigation (VRI)

Credit: Valmont

Sector/speed control: Zone control:

Video Demonstration:https://www.youtube.com/watch?v=u831kWRGVUk

Total Cost: ~$2,000(free with some new panels)

Total Cost: ~$18,000+

Disclaimer: UNL doesn’t endorse any particular brand of irrigation equipment.

Potential Benefits of VRI

• Avoid putting chemicals on waterways (create an “avoidance area”)

• Reduce pumping

• Energy savings

• Reduce nitrate leaching

• Prevent pivot from getting stuck

• Reduce yield losses due to over-irrigation

• Reduce water application rates on steep slopes (reduce runoff/erosion)

• Reduce over-application with corner arms

• Increased yield with given water allocation

=> Optimize the use of natural precipitation

Prescription Maps

How do we develop dynamic

prescription maps?

CropMetrics, North Bend, NE

VRI Zoning: Cosmic-ray Neutron Probes, huh?

and EC techniques

Dr. Trenton Franz, School of Natural Resources - UNL

CropMetrics, North Bend, NE

Precision AgricultureMapping done in 2 hours for both EC and VWC on 3/10/16, Brule Water Lab 1

Aerial Image SSURGO Database

EC Map VWC Map

2013

2014

Field Experiment - Methods

• Field Sites: ARDC, Mead NE and West Central Water Resources Field Laboratory near Brule, NE

ECa maps provided by J. Luck and T. Franz.

9

Soil Water Monitoring

Wireless NetworkSatellite

Communication

Data LoggerSensors

Excellent Recent Developments to

Improve Management to Conserve Water

and Energy, and Protect Water Quality

12

Tempest UAV Typical test flight pattern Thermal Infrared Natural Color

Biological Systems Engineering Department @ UNL

Normalized Difference Vegetation Index (NDVI) Image of Variable Rate Irrigation

Center Pivot at Mead, NE.

Image from the June 30th, 2016 flight

8 cm pixel resolution. NDVI uses red and

Near-infrared bands from the onboard

Micasense camera

14

RGB Image of VRI Pivot at Mead.

Image of September 8th, 2016 flight

8 cm pixel resolution.

Red, Green and Blue Bands

The Hybrid ET model 1

HS

H = HS+ HC

HC

TS

TAC

TA

TC RX

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

Prognostic Modified FAO-564

type water balance of the root zone

ETa

P Irr.

DP CR

RO

FC

PWP

Diagnostic SVAT SchemeThe Two-Source Energy Balance Model (TSEB)2,3

Series Resistance FormulationLE = 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. In Advances in Water Resources.

4 Allen et al. (1998), 5Neale et al. (1989), Campos et al. (2016)

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 onsoil/plant fluxes and stress

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

Accommodates off-nadirthermal sensor view angles

Treats soil/plant-atmosphere coupling differences explicitly

Two-Source Energy Balance Model (TSEB)

RN

System and Component Energy Balance

= H + lE + G

RNC = HC + lEC

RNS = HS + lES + G

= = =

+ + +

TS

TCTAERO

SY

ST

EM

CA

NO

PY

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

/day

)

Time (days)

SVAT

Image

Prognostic model

FAO 56

Assimilation of ET + Update of Soil Moisture

Diagnostic model

Kcb

The Hybrid Model1

1Neale et al. (2012), Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach. In Advances in Water Resources. Volume 50, Pages 152-161, ISSN 0309-1708, 10.1016/j.advwatres.2012.10.008.

Methods – SETMI Interface• Spatial EvapoTranspiration

Modeling Interface (SETMI)

• Operates in ArcGIS Environment

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

Multispectral Imagery from Satellite and UAV (Variable Spatial –and Temporal)

VRI Prescriptions from SETMI

ECa Map (High Spatial)

Weather Data (High Temporal)

SETMI –Based VRI

Model

Previous Irrigation Maps (Resolution Dependent on VRI)

Soil Moisture (Low Spatial, High Temporal)

New VRI Prescription and Schedule

21Valley Irrigation

Prescription Pushed to AgSense Grower Page – More User Friendly

Reflectance-based Kcb & Water BalanceWright,

1982

Corn,

1982 The Kcb

represents the

average ET

from plant

transpiration

with a dry soil

background

and no

limitation of

soil water in

the root zone

of the crop

(From FAO56)

ETa = Kc ETr

Kc = (Kcb*Ka +Ks)

Neale, C. M. U., W.C. Bausch and D.F. Heermann. 1989. Development of reflectance based crop coefficients for corn.

Transactions of the ASAE,Vol. 32(6):1891-1899

Evolution of Reflectance-based Crop coefficient (Kcbrf)

Corn, 2010

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

SAVI = (NIR – Red) (1+L)/ (NIR + Red + L)

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

percent cover around 80%

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

Bausch, 1993

Reflectance based Kcb Transformations

Rationale for re-examining Kbcrf for Corn

• Original research in the mid 1980’s was based on very different varieties with a more planophile leaf structure, shorter plants and planted to a lower plant population reaching lower maximum LAI values in the field

• New hybrid varieties have an erectophile upper canopy, are taller and planted at a higher density reaching higher LAI values in the field

New Reflectance based Kcb for Corn and Soybeans

Application for ET and Irrigation requirements for Corn and Soybeans

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

Actual Irrigation

Irrigation requirements

Actual Irrigation

Irrigation requirements

New Reflectance based Kcb for Corn and Soybeans

Reflectance-based Crop Coefficients Redux: For Operational Evapotranspiration Estimates In The Age Of High Producing

Hybrid Varieties. 2017. Isidro Campos; Christopher M.U. Neale; Andrew E. Suyker; Timothy J. Arkebauer; Ivo Z. Gonçalves.

Agricultural Water Management.. Vol. 187, Pages 140-153. http://dx.doi.org/10.1016/j.agwat.2017.03.022

CWP = Yieldact / ETact (kg m−3)

where Yieldact is the actual marketable crop yield (kg ha−1)

ETact is the actual seasonal crop water consumption by

evapotranspiration (m3 ha−1)

Crop Water Productivity

Yield/Grain

production (Y)

ET & abiotic

stress (Water, Tª)

Based on soil water balance

remote sensing driven and

hybrid model

Site specific

Harvest index (HI)

Existing models on

Yield partitioning and

experimental data

Biomass (B)

Normalized water

productivity (WP*)

Normalization of WUE for

atmospheric demand

High to Medium resolution EO data:Landsat Constellation

MODIS Constellation

Agro-meteorological dataExisting ground-networks

and satellite based estimates

Yield estimates from remote sensing

Y=B*HI

Light use

efficiency

On the conservative behavior of biomass water productivity (Steduto et al. 2007)

WP*b: normalized water productivity

Biomass, accumulated biomass

Tc: Crop transpiration (adjusted for water stress).

∆e: Lear-to-air vapor pressure deficit, approximated by VPD

Analyzing the relationship between SAVI & crop production

WP*b: normalized water productivity

Biomass, accumulated biomass

Tc: Crop transpiration (adjusted for water stress).

Eo: Reference ETo

Drawbacks of the normalization based on VPD∆e < VPD if high transpiration rates of advection

VPD > ∆e if water stress (high canopy temperature)

Effects of the data availability and VPD formulation

Simulation of biomass production

WP*B: Normalized water productivity (g/m2)Kcb: Transpiration coefficientKSW: Water stress coefficientKST: Air temperature water stress coefficient

Analyzing the relationship between SAVI & crop production

Water stressbased on thesoil water balance

Temperature stressbased on the air temperature

WP*

Crop Yield and Water Productivity Estimation:• 𝑾𝑷𝑩

∗ =𝑩𝒊𝒐𝒎𝒂𝒔𝒔

𝒏𝒊=𝟏

𝑻𝒂𝒅𝒋

𝑬𝑻𝒐

=𝑩𝒊𝒐𝒎𝒂𝒔𝒔

𝒏𝒊=𝟏𝑲𝒄𝒃,𝒂𝒅𝒋

• 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 = 𝑊𝑃𝐵∗ × 𝑛

𝑖=1𝐾𝑐𝑏 × 𝐾𝑆𝑇 × 𝐾𝑆𝑊

• 𝑌𝑖𝑒𝑙𝑑 = 𝐻𝐼 × 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 = 𝐻𝐼 ×𝑊𝑃𝐵∗ × 𝑛

𝑖=1𝐾𝑐𝑏 × 𝐾𝑆𝑇 × 𝐾𝑆𝑊

Water Productivity And Crop Yield: A Simplified Remote Sensing Driven Operational Approach. 2017.

Isidro Campos; Christopher M.U. Neale; Timothy J. Arkebauer, Andrew E. Suyker; Ivo Z. Gonçalves. Agricultural and Forest Meteorology.

https://doi.org/10.1016/j.agrformet.2017.07.018

Analyzing the relationship between SAVI & crop production

Experimental values ofHarvest Index

for corn and soybeansin Mead, Ne

• Mean values around 0.5 for bothcrops and irrigation managements

• Strong variability depending onmeteorological conditions???

• Difficulty to determine empiricallybecause of the dynamics ofbiomass accumulation

Orange: Corn Green: Soybeans

Interpolation of SAVI data for the assessment of ΣKcb

• Strong variability of crop growth (ET, Biomass and yield)

Differences in ET (ΣKcb) result in variability of crop yield at field scale

Differences in WP* for corn and soybean result in differences in yield

Comparison of measured and modeled yield for corn and soybeans (2011-2012)

• Relative good but noisy

agreement for both

analyzed campaigns

• Higher production in

2012, extremely dry

year (flash drought in

the mid-western US)

• Sensitive to the great

range of corn production

in 2012

Comparison of measured and modeled yield for corn

plots in 2011 (blue) and 2012 (red)

CWP = Yieldact / ETact (kg m−3)

where Yieldact is the actual marketable crop yield (kg ha−1)

ETact is the actual seasonal crop water consumption by

evapotranspiration (m3 ha−1)

Crop Water Productivity

Nebraska

Upper

Republican

Center Pivots

Location of the Upper Republican River Basin in southwest

Nebraska

Example of Application in Nebraska

UPPER REPUBLICAN RIVER BASIN

57 DOY

345 DOY265 DOY

249 DOY201 DOY (max. IAF)

281 DOY

Upper Republican River Basin, NE

Upper Republican River Basin, NE

Christopher Neale

Water for Food Institute

University of Nebraska

Christopher Hain

Earth System Science Interdisciplinary Center, University of

Maryland, NOAA-NESDIS

Martha C. Anderson

USDA-Agricultural Research Service, Hydrology and Remote

Sensing Laboratory

Supplementing ALEXI Capabilities with Polar Orbiting Sensors

Time of Day

Lan

d S

urf

ac

e T

em

pe

ratu

re

Local NoonSunrise

Morning LST Rise: ALEXI Window

VIIRS Nighttime

LST

VIIRS Daytime

LST

A technique has been developed and evaluated using GOES data to train a regression model to use day-night LST differences from MODIS to predict the morning LST rise needed by ALEXI. The

regression model can provide reasonable estimates of the mid-morning rise in LST (RMSE ~ 5 to 8%) from the twice daily VIIRS LST observations.

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

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

Provides information onsoil/plant fluxes and stress

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

Accommodates off-nadirthermal sensor view angles

Treats soil/plant-atmosphere coupling differences explicitly

Two-Source Energy Balance Model (TSEB)

The ALEXI model runs the TSEB

Development of a High-Resolution (375-m) VIIRS ET Product

1. Mid-morning change in Land Surface Temperature

Development of a High-Resolution (375-m) VIIRS ET Product

2. Leaf Area Index and Fraction of Green Vegetation Cover (fc)

Development of a High-Resolution (375-m) VIIRS ET Product

3. Land Surface Albedo

• Only available VIIRS product is at 750-m – mapped to 375-m grid – used

to calculate surface reflectivity in VIS/NIR spectrum as needed by ALEXI

Development of a High-Resolution (375-m) VIIRS ET Product

4. Incoming Solar Radiation

• Only available from geostationary platforms – Meteosat (3-km) / will use

CFS-4 daily insolation for scaling to daily fluxes as back-up data source

[currently used for other ALEXI applications; not ideal due to model-

based estimate but model/RS-based insolation tend to converge when

considering monthly-annual time scales]

Development of a High-Resolution (375-m) VIIRS ET Product

5. Meteorological Surface Fields (e.g., air temperature; wind speed; surface

pressure; incoming LW)

• Climate Forecast System Reanalysis (hourly; 0.50º)

6. Morning Profile of Potential Temperature

• Climate Forecast System Reanalysis (hourly; 0.50º)

CFS-R fields are currently being used for all data fusion results (30-m ET) so we

don’t expect any issues with this dataset.

Development of a High-Resolution (375-m) VIIRS ET Product

7. Landcover / Vegetation Type

• Only available VIIRS product is at 1-km – insufficient for 375-m product;

• So we’ll use 30-m Landsat-based classification (Chinese dataset) – we’ve

downloaded all tiles over MENA region and have gridded to 375-m

domain and calculated % of each land class in each VIIRS pixel.

• What happens when Landsat-based classification is not representative of

VIIRS EVI/NDVI information?

• We’re developing a processing check to ensure changes to

agricultural practices which can be determined by VIIRS vegetation

time series to ensure we’re processing pixels which may be classified

as “barren” but now include agricultural pixels.

Development of a High-Resolution (375-m) VIIRS ET Product

8. Cloud Mask

• Only available VIIRS product is at 750-m – mapped to 375-m grid.

• Once we acquire more years of VIIRS data we’ll develop additional

climatological-based QC metrics to remove cloud contamination that is

“missed” in VIIRS cloud mask.

Development of a High-Resolution (375-m) VIIRS ET Product

Example location

*Shading indicates 1-km percentage of cropland from global synthesis of several RS-based land use maps

Development of a High-Resolution (375-m) VIIRS ET Product

Current MODIS Capability (1000-m) VIIRS Capability (375-m)

Current VIIRS Latent Heat Flux (W m-2) Capability (375-m)

Development of a High-Resolution (375-m) VIIRS ET Product

VIIRS 375 m Annual ET (mm)

PROPOSED PRODUCTION TIMELINE FOR THE MENA REGION:

• ALEXI software has been ported to run on the HCC super computer at University of Nebraska-Lincoln

• September 2017: start producing the daily product at Nebraska

• September – December 2017: test and verify the product with ground data with partners in the MENA region and other countries: Spain, India, Brasil, USA

• January 2018: Launch the product through a website and make it available to registered users and collaborators

Disaggregation of VIIRS 375 m product to Higher Resolutions

• Disaggregation and data fusion downscaling to

higher resolutions will be done using the DisALEXI

model

• The 375 m product is the upper boundary

condition

• A new version of DisALEXI developed using open-

source tools (e.g., Python) is being developed for

this purpose called PyDisALEXI (no licensed

software required)

• We will train the cooperating agencies in the

different MENA countries to use this software and

downscale ET to agricultural and irrigated areas

0.00

3.75

7.50

11.25

15.00

Nile Delta Irrigation

Input data: ALEXI daily ET

VIIRS daily ET mm/d

• Daily ET calculated at

VIIRS 375 m data using

the ALEXI model.

0.00

3.75

7.50

11.25

15.00

Nile Delta Irrigation

Initial results: Landsat daily ET

Landsat daily ET mm/d

• Landsat Daily ET

downscaled from ALEXI

using the PyDisALEXI

model.

Proposed 15° x 15°Global processing tiles (375-m) VIIRS ET Product

The Future is Bright – Think of the Possibilities

High Producing HybridCrop Varieties

Encapsulated Fertilizer

Monitoring with Unmanned Vehicle

Auto Steer Vehicles

ET from Remote Sensing

Soil/Plant Monitoring Satellite Communication

Variable Rate Irrigation

Now we have to put it all together

Look at Recent Developments

Courtesy of Derrel Martin

THANK YOU