The GRAPEX Project: A Multi-scale Approach to Water and Energy Exchange in Vineyards

William Kustas1, Martha Anderson1, Kyle Knipper1, Feng Gao1, Joe Alfieri1, Lynn McKee1, John Prueger2, Jerry Hatfield2, Chris Parry3, Andrew McElrone3,

Larry Hipps4, Alfonso F Torres-Rua5, Mac McKee5, Luis Sanchez6, Maria Mar Alsina6, Nick Dokoozlian6,

Forrest Melton7,8, Kirk Post7, Christopher Hain9

Héctor Nieto10 , Nurit Agam11, Ting Xia12

1USDA-Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD2USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA

3USDA-ARS Crops Pathology and Genetics Research Unit, Davis, CA4Plants, Soils and Climate Department, Utah State University, Logan UT

5Department of Civil and Environmental Engineering, Utah State University, Logan, UT6Viticulture Research, Ernest & Julio Gallo Winery, Modesto, CA

7School of Natural Resources California State University of Monterey Bay, Marina CA8NASA Ames Research Center, Moffett Field, CA

9NASA Marshall Space Flight Center, Huntsville AL10IRTA (Institute for Food and Agricultural Research and Technology) LLIEDA Spain

11Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of Negev, Israel12Department of Hydraulic Engineering, Tsinghua University, Beijing, China

USDA is an equal opportunity provider and employer.

Many participants are contributing to the GRAPEX project……

GRAPEX=Grape Remote sensing Atmospheric Profile Evapotranspiration eXperiment

DEC

MAR

JUN

April

May

Late Season:Veraison - Harvest

Off Season

Mid Season:Fruit Set - Veraison

Timing the start of Irrigation

July

SEP

Determining the amount of Irrigation

Water management decisions focus on:1) Timing of the start of irrigation in the spring2) Manage the quantity of water applied during

remainder of growing season

Complications arise from:1) Changing Weather/Climate2) Within field variations (soil texture, elevation)3) Local/state government imposed mandatory

water reductions

Irrigation Scheduling in Viticulture

Mon

itor

ing

Wat

er U

se in

U.S

. Vin

eyar

ds

𝐸𝐸𝐸𝐸 = 𝐾𝐾𝑐𝑐(𝐸𝐸𝐸𝐸0)

Crop Coefficient (~ NDVI)

Reference ET

Current Operational ET Estimates forIrrigation Scheduling

Mon

itor

ing

Wat

er U

se in

U.S

. Vin

eyar

ds

ET

𝐾𝐾𝑐𝑐 = 𝐸𝐸𝐸𝐸/𝐸𝐸𝐸𝐸0

𝐸𝐸𝐸𝐸 from TSEB FAO 56

Limitations of Crop Coefficient for actual ET

The measurements were collected in two vineyards located approximately 32 km (~20 mi) northeast of Lodi, CA (38.29°N, 121.12°W).

East-West Rows spaced ~3.3 m (11 ft). Vines spaced ~1.5 m (5 ft).

North Vineyard (site 1): ~ 34.4 ha (85 ac). Mature vines.

South Vineyard (site 2): ~21 ha (52 acre). Young vines.

GRAPEX Study siteG

RA

PE

X S

tudy

in U

.S. V

iney

ards

North Vineyard(site 1)

SouthVineyard(site 2)

GRAPEX Study siteB

are

soil,

cov

er c

rop

& v

ine

cano

py: 3

sou

rces

Tsoil surface

Wind speed profile

Wind direction

u, v, w, Sc

Tair

Tveg surface

Vin

e ca

nopy

str

uctu

re a

ffec

ting

tur

bule

nt e

xcha

nge

The two vineyards were heavily instrumented to measure meteorological, vegetation, and soil conditions.

Measurements included: meteorological quantities, e.g. wind speed, air temperature, and precipitation. surface energy balance. wind and friction velocity profiles at four† levels. surface/canopy temperature. vine water use via sap flow sensors. soil temperature/moisture at multiple depths.

† † During IOPs, wind profile data was also collected at a fifth level (~1.5 m) , beneath the canopy.

GRAPEX site measurementsP

lant

and

Hyd

rom

etO

bs. a

t G

RA

PE

X S

ite

GRAPEX site measurementsP

rofi

le S

oil M

oist

ure

and

Sap

flow

Net

wor

k

NASA Soil Moisture ProfileNetwork

NASA Met

NASA Cam

North VineyardUSDA/Gallo Soil Moisture Profiles

South VineyardUSDA/Gallo Soil Moisture Profiles

X

X X

X

X Gallo SMGallo SF

+++

++

X

++

++XX X

NASASR

USDAEC

USDAEC

ARSSR NASA

Cam

GRAPEX MeasurementsDuring IOPs

GR

AP

EX

Epi

sodi

c M

easu

rem

ents

Spectral & LAI

Canopy T & CO2

solar radiationdivergence

Below canopy wind& water vapor turbulence

Aircraft/UAV –based high resolution Vis/NIR and thermal-IR

Scintillometry

Tower-basedThermal/Optical

ScannerUAV

GR

AP

EX

Epi

sodi

c M

easu

rem

ents

Additional GRAPEX MeasurementsDuring IOPs

Leaf & Canopy Hyperspectral

Micro-Bowen ratio systems

IRT sensornetwork

Leaf

Are

a In

dex

tran

sect

s fo

r G

RA

PE

XGRAPEX LAI Measurements During IOPs (2014-16)

GR

AP

EX

Int

ensi

ve O

bser

vati

on P

erio

ds (

IOP

s)GRAPEX IOPs

IOP 1 ~April/May

IOP 2~June/July

IOP 3 ~August

LAI ~ 1.5

LAI ~ 1

LAI ~ 2

LAI ~ 1.5

IOP 1Flowering

IOP 2Fruit Set/Veraison

IOP 3Veraison

LAI ~ 0.5 to 2

LAI ~ 0.5 to 2

North Vineyard

South Vineyard

Tw

o-S

ourc

e E

nerg

y B

alan

ce A

ppro

ach

(TS

EB

) TSEB Approach

Provides information onsoil/plant fluxes and stress

Accommodates off-nadirthermal sensor view angles

Treats soil/plant-atmosphere coupling differences explicitly

TAERO

Tw

o-S

ourc

e E

nerg

y B

alan

ce A

ppro

ach

Advantages of TSEB

Met Forcing

Advantages of TSEB

Modification of radiation extinction for vine canopy architecture

Rad

iati

on M

odel

ing

in V

iney

ards

Measurements

Refined radiation algorithm

Model validation

Modification of wind profile in the canopy airspace for vine canopy for canopy architecture

Mod

elin

g B

elow

Can

opy

Win

ds in

Vin

eyar

ds Original wind extinction algorithm

Revised wind extinction algorithm

August 9, 2014

Usi

ng U

AV

Im

ager

y in

TS

EB

Original TSEB formulation

Modified TSEB formulation

TS

EB

-der

ived

ET

and

T U

sing

UA

V I

mag

ery

ET ET ET

T T T

Partitioning of ET into T and E: Observations and Model Estimates

TSEB vs EC-Flux PartitioningSap-flow vs EC-Flux Partitioning

TSEB vs EC/Micro Bowen Ratio

Low ET High ET

LandsatE

ffec

t of

Res

olut

ion

on M

appi

ng E

T P

atte

rns

Vernel pools

Old stream bed

Yield mapAircraft

PhoDAR (LIDAR-like) dataG

RA

PE

X U

AV

can

opy

Vol

ume

Automatic soil / vegetation discrimination, canopy volume relate to yield

GR

AP

EX

UA

V S

hado

w D

etec

tion

Shadow Detection Algorithm

True Color Image Shadowed areas highlighted = shadowed pixels

Exp

andi

ng G

RA

PE

X S

tudy

Sit

esGRAPEX Sites Expanded: to New Climate Zones & Varieties

Con

clus

ions

•Very high resolution imagery from UAVs may provide valuable information on landscape features and vine conditions not detectable at satellite resolutions.

• Crop coefficient-based techniques have limited utility for estimating ET and stress in vineyards.

• The capability of resolving vine transpiration from interrow evaporation/transpiration may depend on pixel resolution.

Preliminary Results from GRAPEX

• Refinements to TSEB model parameterizations for unique canopy structure, architecture and row spacing/orientation using the data collected from GRAPEX is improving model performance.