Crop water consumptive use in the Sacramento-San Joaquin ...calasa.ucdavis.edu/files/287345.pdf · Crop water consumptive use in the Sacramento-San Joaquin Delta: UAV applications

Crop water consumptive use in the

Sacramento-San Joaquin Delta: UAV

applications for field scale water

consumptive use estimations

J. Andrés Morandé, Josué Medellín-Azuara, Andreas

Anderson, Joshua H. Viers, Yufang Jin, Kyaw Tha Paw,

YangQuan Chen, Ricardo Trezza

Are UAVs a useful tool for estimating

water consumption in crops and

improving water management at field

scale?

Can UAV improve the current limited scientific data

developed under a particular time/site specific

situation to fit a particular farm condition?

Overview

• Measuring water consumption (evapotranspiration) in crops has been historically a challenge for growers

• Current methods lack of proper temporal or spatial resolution – Landsat 8: medium-high spatial; low temporal

– Weather stations: low spatial; high temporal

• Remote sensing methods for estimating evapotranspiration (ET) large spatial coverage and relatively low cost

Crop water consumptive use =

Evapotranspiration• Evaporation Process by which water is changed from

the liquid or solid state into the gaseous state through the

transfer of heat energy (ASCE, 1949).

Soil, water bodies, vegetation surfaces

• Transpiration the evaporation occurring through plant

leaves (stomatal openings).

Plants (within leaves)

Components of Evapotranspiration

ETc = ETo (ETr) x Kc (ETrF)

1. Reference crop ET effects of weather variables

ETo: ET of well watered grass (California)

ETr: ET of well watered alfalfa (Idaho)

2. Crop coefficient effects of

vegetated, bare, or open water surface

Kc: grass based

ETrF: alfalfa based

Kc: Kcb + Ke x Ks

Why measuring ET?• Primary link in the global hydrologic cycle between the land and the atmosphere

• Plays a key role in runoff and water availability for agriculture and natural systems

• Food supply relies primarily on irrigated agriculture Knowledge of transpiration (i.e.

efficient irrigation, crop selection)

• Multiple uses of water (urban, industrial, agriculture, environmental) political

decisions

infusionsoft.com Hanson , UC Davis

LE: Latent heat flux

Rn: Net radiation

G: Soil heat flux

H: Sensible heat flux

Biophysical basis

• METRIC (Mapping EvapoTranspiration at high Resolution using Internalized Calibration)

• Principle ET estimated as a residual of surface energy balance

LE(ET) = Rn – G – H

• Advantages energy balance can detect reduced ET caused by water shortage, salinity, drought or frost as well as evaporation from bare soil (unlike vegetation indexes).

Study GoalsImproving information on water use in agriculture at field scale by:

1. Estimating ET through UAV-METRIC high spatial resolution maps (0.05 and 1 m-pixel)

2. Comparing low resolution (30 m-pixel) Landsat 8 with high resolution UAV maps

3. Characterizing atmospheric horizontal and vertical profile of ET parameters air temperature and relative humidity

MethodsExperimental

site

• Staten Island,

Sacramento-San

Joaquin Delta,

California (38°11’33”,

-121°30’39”)

• Elevation: -3 m

• Crop blocks: ~3.5 ha

- Pasture

(perennial)

- Alfalfa (annual)

- Corn (annual)

Methods• Four UAV flights matched Landsat 8 overpasses

between July and October 2016.

• Two approaches:

1. METRIC and UAV thermal data: ET was derived from METRIC algorithm,

driven by high resolution multispectral

and thermal UAV images at

1 m resolution (alfalfa)

Methods

2. METRIC and UAV NDVI:

Landsat 8 images were

processed using METRIC to

produce maps of ETrF and

NDVI values in pasture and

corn crops. ET was later

calculated from high

resolution (0.05m-pixel)

UAV maps.

August 30, 2016

Alfalfa Stage: 5 - Early flower

Daily UAV ET: 5.06 mm/day

ETrF: 0.69

September 15, 2016

Alfalfa stage: 0 - Early vegetative

Daily UAV ET: 2.48 mm/day

ETrF: 0.48

Ground image RGB Landsat 8 (30m) UAV (1m)

• Full developed alfalfa ETrF maps

• ET Mean L8-UAV: delta -10%

• ET SD L8-UAV: delta 42%

RGB

Landsat 8 ETrF

UAV

August 30, 2016

Landsat 8 UAV Delta

ET L8

vs UAV

%ETrF

ET

(mm/day) ETrF

ET

(mm/day)

Min0.55 4.02 0.00 0.00

-100.0

Max0.92 6.69 1.05 7.69

14.8

Mean0.77 5.62 0.69 5.06

-10.0

SD0.09 0.68 0.16 1.17

METRIC-UAV Thermal - Alfalfa

Landsat 8

UAV

• Mature corn three weeks

• ET Mean L8-UAV: delta -1.6%

• ET SD L8-UAV: delta 74%

METRIC-UAV NDVI - Corn

September 15,

2016

Landsat 8 UAV Delta

ET L8

vs UAV

%ETrF

ET

(mm/day) ETrF

ET

(mm/day)

Min 0.84 4.36 0.56 2.90 -33.5

Max 0.91 4.71 1.32 6.84 45.4

Mean 0.88 4.56 0.86 4.49 -1.6

SD 0.02 0.08 0.06 0.31

RGB

Landsat 8

UAV

before harvest

ET-instantaneous UAV vs Weather stations

ET-instantaneous UAV vs Weather stations

METRICWeather

Station

Delta

METRIC-WSMETRIC

Weather

Station

Delta

METRIC-WSMETRIC

Weather

Station

Delta

METRIC-WS

30-Aug-16 0.49 0.56 -0.07 0.60 0.52 0.08 0.71 0.30 0.41

15-Sep-16 0.30 0.48 -0.08 0.55 0.48 0.08 0.54 0.33 0.21

1-Oct-16 0.44 0.41 0.03 NF NF NF NF NF NF

NF: No flight

Corn (mm/h)Pasture (mm/h)Alfalfa (mm/h)Date

12% 3% 33%

Spatial characterization of adjacent air layers (pasture)

• Three flight polygons: 6, 11, 15

meters height

• Air temperature (Celsius) and

Relative Humidity (%)

• ~ 3 minutes/flight (secondly

basis measurements)

Spatial characterization of adjacent air layers (pasture)

Spatial characterization of adjacent air layers (pasture)

• Spline Interpolation to each polygon

• Horizontal characterization: 6 - 11 -15 m heigh planes

Spatial characterization of adjacent air layers (pasture)

Negative vertical gradient of Actual vapor pressure (AVP)

3-D plot of actual secondly basis AVP

points in three polygon flights at 6, 10 and

15 m height.

Spline interpolated planes negative AVP

gradient

Flight Time Height

(m)

Saturation

vapor pressure

(Pa)

Average

AVP (Pa)

Polygon

Average

AVP (Pa)

Plane

Average AVP

(Pa) Weather

station

1 13:41 6 3126.03 1088.8 1095.6 1020

2 13:43 10 3118.87 1058.5 1068.0 1020

3 13:45 15 3131.48 1038.7 1036.3 1020

Results

• Alfalfa average water consumption in CA: 460-910

mm

• Assuming our daily measurements as monthly

averages for four of the most demanding months in

the season (July-October):

– Landsat 8-based water consumption estimates:

556.3 mm

– UAV-based estimation: 520.7 mm

Practical applications

1. Estimation of areas below optimal ETrF in alfalfa

Condition

(ETrF value)

Pixels (n) Fraction of

total (%)

Area

(m2)

Fraction of

Hectare (%)

Pixels lower

than 0.4 45 4.8 45 0.5

Pixels lower

than 0.5 257 27.6 257 2.6

Landsat 8 ETrF (30m) UAV (1m)

Pixel matrix

L8 Pixel 1m resolution histogram

0.5

ETrF

N°pixels

0.56 0.53 0.57 0.55 0.41 0.53 0.57 0.44 0.46 0.59 0.55 0.58 0.55 0.67 0.61 0.55 0.53 0.36 0.44 0.47 0.50 0.46 0.50 0.63 0.56 0.57 0.40 0.53 0.49 0.45

0.61 0.55 0.43 0.55 0.55 0.60 0.57 0.60 0.51 0.55 0.56 0.59 0.59 0.65 0.59 0.53 0.53 0.52 0.46 0.49 0.46 0.47 0.51 0.43 0.39 0.47 0.57 0.57 0.41 0.59

0.41 0.40 0.46 0.43 0.51 0.59 0.51 0.50 0.47 0.55 0.56 0.66 0.56 0.53 0.51 0.57 0.60 0.45 0.43 0.53 0.56 0.63 0.62 0.60 0.57 0.59 0.53 0.51 0.45 0.47

0.43 0.50 0.46 0.53 0.45 0.42 0.47 0.44 0.47 0.52 0.50 0.57 0.52 0.46 0.43 0.41 0.35 0.48 0.43 0.53 0.58 0.47 0.46 0.52 0.64 0.55 0.46 0.51 0.48 0.45

0.42 0.40 0.52 0.41 0.36 0.42 0.51 0.44 0.48 0.61 0.48 0.52 0.51 0.50 0.42 0.50 0.66 0.57 0.49 0.62 0.50 0.46 0.59 0.50 0.50 0.34 0.44 0.45 0.48 0.51

0.44 0.51 0.36 0.49 0.43 0.47 0.41 0.45 0.60 0.58 0.59 0.35 0.64 0.49 0.58 0.38 0.52 0.45 0.50 0.49 0.47 0.43 0.45 0.42 0.47 0.63 0.62 0.57 0.58 0.55

0.66 0.72 0.62 0.66 0.76 0.69 0.76 0.64 0.66 0.62 0.65 0.68 0.63 0.62 0.60 0.73 0.65 0.58 0.64 0.55 0.50 0.50 0.41 0.53 0.60 0.64 0.48 0.47 0.61 0.52

0.66 0.70 0.71 0.66 0.70 0.76 0.74 0.70 0.70 0.71 0.70 0.68 0.67 0.72 0.68 0.57 0.73 0.66 0.72 0.73 0.70 0.55 0.60 0.62 0.55 0.52 0.59 0.61 0.56 0.60

0.74 0.65 0.74 0.69 0.84 0.84 0.78 0.73 0.67 0.71 0.79 0.65 0.64 0.62 0.66 0.74 0.76 0.68 0.68 0.66 0.68 0.67 0.74 0.72 0.63 0.68 0.65 0.64 0.70 0.71

0.59 0.72 0.58 0.66 0.61 0.51 0.60 0.63 0.55 0.56 0.52 0.59 0.62 0.62 0.54 0.61 0.58 0.62 0.75 0.63 0.63 0.64 0.65 0.63 0.68 0.73 0.73 0.74 0.73 0.75

0.64 0.66 0.71 0.78 0.70 0.70 0.63 0.71 0.71 0.70 0.67 0.54 0.55 0.54 0.44 0.56 0.51 0.56 0.55 0.60 0.46 0.52 0.62 0.55 0.53 0.60 0.68 0.62 0.72 0.66

0.64 0.73 0.81 0.70 0.64 0.71 0.79 0.70 0.62 0.71 0.67 0.72 0.71 0.57 0.66 0.74 0.68 0.72 0.75 0.64 0.52 0.59 0.60 0.50 0.44 0.47 0.52 0.53 0.57 0.61

0.69 0.66 0.67 0.61 0.76 0.42 0.69 0.64 0.54 0.59 0.58 0.47 0.54 0.52 0.67 0.50 0.56 0.64 0.61 0.61 0.65 0.72 0.62 0.66 0.61 0.58 0.60 0.68 0.76 0.68

0.60 0.62 0.53 0.61 0.61 0.30 0.48 0.45 0.42 0.49 0.53 0.43 0.46 0.63 0.60 0.54 0.58 0.55 0.64 0.62 0.62 0.63 0.59 0.53 0.51 0.60 0.58 0.64 0.56 0.66

0.60 0.58 0.59 0.60 0.62 0.73 0.55 0.61 0.51 0.53 0.48 0.49 0.54 0.32 0.57 0.50 0.59 0.65 0.63 0.65 0.69 0.51 0.61 0.63 0.40 0.65 0.49 0.48 0.59 0.58

0.58 0.44 0.46 0.48 0.45 0.42 0.45 0.61 0.52 0.44 0.56 0.46 0.58 0.56 0.57 0.47 0.50 0.60 0.44 0.51 0.39 0.34 0.37 0.57 0.58 0.48 0.56 0.69 0.71 0.69

0.52 0.44 0.38 0.37 0.27 0.32 0.42 0.37 0.47 0.49 0.48 0.53 0.52 0.51 0.50 0.54 0.48 0.48 0.57 0.61 0.59 0.50 0.53 0.55 0.59 0.61 0.58 0.55 0.49 0.48

0.42 0.36 0.44 0.36 0.42 0.39 0.39 0.48 0.42 0.40 0.46 0.31 0.52 0.55 0.48 0.54 0.66 0.51 0.53 0.54 0.60 0.59 0.50 0.49 0.41 0.48 0.48 0.48 0.58 0.43

0.48 0.47 0.28 0.33 0.47 0.46 0.42 0.48 0.41 0.42 0.47 0.46 0.37 0.40 0.51 0.55 0.47 0.47 0.54 0.61 0.52 0.46 0.41 0.44 0.43 0.52 0.48 0.52 0.67 0.67

0.48 0.41 0.47 0.46 0.50 0.52 0.43 0.42 0.40 0.37 0.42 0.38 0.39 0.34 0.46 0.40 0.52 0.60 0.50 0.51 0.52 0.51 0.52 0.42 0.56 0.41 0.43 0.53 0.57 0.39

0.57 0.42 0.33 0.52 0.61 0.38 0.48 0.41 0.41 0.48 0.49 0.59 0.41 0.38 0.48 0.50 0.47 0.43 0.34 0.36 0.42 0.43 0.45 0.46 0.60 0.39 0.56 0.56 0.56 0.54

0.65 0.66 0.49 0.57 0.54 0.53 0.54 0.50 0.48 0.44 0.44 0.45 0.40 0.52 0.49 0.45 0.55 0.49 0.29 0.42 0.35 0.35 0.23 0.46 0.42 0.57 0.44 0.50 0.51 0.61

0.57 0.62 0.57 0.65 0.65 0.58 0.53 0.52 0.58 0.59 0.63 0.60 0.60 0.57 0.46 0.65 0.69 0.67 0.69 0.64 0.60 0.50 0.64 0.60 0.51 0.42 0.58 0.59 0.51 0.57

0.64 0.61 0.65 0.65 0.65 0.65 0.72 0.59 0.74 0.60 0.71 0.63 0.65 0.62 0.62 0.56 0.71 0.71 0.68 0.61 0.56 0.62 0.61 0.65 0.69 0.53 0.65 0.59 0.65 0.64

0.60 0.62 0.59 0.54 0.63 0.61 0.46 0.60 0.49 0.57 0.45 0.51 0.52 0.58 0.55 0.51 0.57 0.55 0.61 0.59 0.71 0.57 0.66 0.58 0.64 0.68 0.66 0.64 0.64 0.52

0.69 0.67 0.70 0.75 0.59 0.70 0.60 0.64 0.73 0.69 0.60 0.58 0.41 0.57 0.60 0.51 0.51 0.50 0.57 0.54 0.65 0.54 0.65 0.60 0.67 0.50 0.66 0.57 0.56 0.68

0.67 0.71 0.67 0.66 0.70 0.71 0.62 0.65 0.73 0.67 0.62 0.70 0.66 0.63 0.62 0.55 0.66 0.64 0.59 0.58 0.63 0.68 0.57 0.61 0.65 0.64 0.59 0.61 0.61 0.68

0.46 0.53 0.52 0.59 0.58 0.61 0.45 0.48 0.57 0.62 0.67 0.63 0.65 0.57 0.60 0.53 0.62 0.58 0.64 0.62 0.70 0.63 0.62 0.69 0.73 0.68 0.79 0.76 0.60 0.66

0.60 0.63 0.51 0.46 0.57 0.47 0.57 0.49 0.43 0.51 0.54 0.35 0.57 0.56 0.59 0.56 0.63 0.56 0.65 0.58 0.65 0.54 0.54 0.69 0.76 0.61 0.64 0.66 0.57 0.61

0.66 0.64 0.41 0.56 0.47 0.54 0.55 0.52 0.49 0.62 0.52 0.62 0.55 0.50 0.56 0.57 0.68 0.58 0.69 0.53 0.66 0.58 0.68 0.63 0.61 0.65 0.63 0.63 0.62 0.61

0.65 0.57 0.58 0.60 0.52 0.56 0.62 0.56 0.54 0.60 0.67 0.43 0.56 0.53 0.55 0.64 0.71 0.59 0.63 0.52 0.57 0.65 0.67 0.61 0.61 0.61 0.54 0.60 0.70 0.53

2. Estimation of economical impact in alfalfa

• Spatial differences in ET will affect the homogeneity of alfalfa

development Lower quality, lower prices

• Simulation: single pixel results scaled up to a hectare:

- Quality decline in 28% of the block quality level drops from

“Premium” ($220/ton) to “Good” ($170/ton) (USDA, 2017).

- California alfalfa average yield: 16.8 tons/ha

• Impact: gross income reduction of $840/ha.

Conclusions

• Enhanced spatial and temporal resolution in multispectral and thermal imagery using UAV improve information on water use and site conditions in agriculture.

• UAV high resolution provides reliable spatialcharacterization and estimates of crop ET and ETrF:decision of how much water and when and where should be applied

• UAV technology provides a cost-effective method for water estimates-decisions at farm level, non-intrusively, and consistently.

Thanks to:

Project: “Estimation of Crop Evapotranspiration in the Sacramento SanJoaquin Delta” funding and research support fromState Water Resources Control Board, California Department of Water Resources, DeltaProtection Commission, Delta Stewardship Council, North Delta Water Agency, Central DeltaWater Agency, and South Delta Water Agency

Jorge Andres Morande ([email protected])