Simplified Tools and Low-Hanging Data for Useful Remote Sensing Applications

Rain by Nicolas LEULIET and Sun by Vadim Solomakhin from the Noun Project

Reagan NolandWesley PorterDavid Daughtry IIGabriel Paiao

InfoAgJuly 24, 2019

Why remote measurements?

• Quick, non-destructive assessment

• Information at the field scale

• Objective comparisons

• Economic efficiency?

What tools have become common?

• Spectral reflectance• NDVI, NDRE, etc.

• Thermal sensors• Image analysis

Advantages• Valuable indicators of plant

physical properties. • Easily integrated with UAVs

Hammer by Rflor and nail by iconix from the Noun Project

“We have a hammer, now everything looks like a nail.” – Randy Taylor, Oklahoma State

Specific applications may require specific toolsExamples:• Crop N status• Maturity for harvest decisions

• Yield estimates• Detecting pest / disease outbreaks

Need the right tool for the job

• Blackmer et al. (1996)- Identified wavebands near 550 (green) and 710 (red edge) nm as more predictive of corn N deficiencies than 450 (blue) or 650 (red) nm.

• Tarpley et al. (2000) – Showed best predictions with red edge and NIR reflectance for cotton leaf N concentration.

Blackmer, T.M., J.S. Schepers, G.E. Varvel, and E.A. Walter-Shea. 1996. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron. J. 88:1-5.Tarpley, L., K.R. Reddy, and G.F. Sassenrath-Cole. 2000. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Sci. 40:1814-1819.

Case 1: Optimizing Yield and Quality in Alfalfa

Rel

ativ

e C

hara

cter

istic

s(ASA, 2011)

Experimental ApproachMeasured canopy reflectance prior to destructive sampling in a wide range of alfalfa maturityRosemount, MN (2014-2015)

Reflectance Data Collection

Wavelength

VIS NIR SWIR

Selecting Predictive Wavebands

0.1

0.2

0.3

0.4

Spec

tral

cor

rela

tion

(R)

to c

rude

pro

tein

The first iteration

• Used stepwise variable selection to identify models

• The best model used 11 wavebands (350 – 2500 nm)

• Specialized but now need to simplify

R² = 0.8968

10

15

20

25

30

35

10 15 20 25 30 35Ac

tual

Cru

de P

rote

in (%

)

Predicted Crude Protein (%)

Alfalfa crude protein estimated using 11 wavebands

Limiting economic factors for spectral sensors

• Spectral Range

• Spectral resolution

• Number of bands

$

==

=

• N stress led to increased reflectance at 695 +- 2.5 nm and decreased reflectance at R410

• A three-waveband canopy reflectance model explained 80% of the variability in leaf N

• Emphasized the use of ratios between bands

Read, J.J., L. Tarpley, J.M. McKinion, and K.R. Reddy. 2002. Narrow-waveband reflectance for remote estimation of nitrogen status in cotton. J. Environ. Qual. 31:1442-1452.

Simplified Tools and Low-Hanging Data for Useful Remote Sensing Applications

Balancing Simplicity with Specialization

Read et al. (2002)

What low-hanging data?

• Any information that is free or easy to obtain and relevant to the growth of the crop

• Planting date• Seeding rate• GDUs• Rainfall• Growth stage• Soil type / analyses• Fertility history

• Rain by Nicolas LEULIET and Sun by Vadim Solomakhin from the Noun Project

GDUs alone can inform timing of first alfalfa harvest

20

25

30

35

40

45

50

55

0 200 400 600 800 1000 1200 1400

% N

DF

Cumulative GDD (base 41 ᵒF)

Integrating Predictors

• Sharratt et al. (1989) indicate that the optimum base temperature changes throughout the growing season.

• We developed an alternative GDU calculation using a temporally graduating base temperature from 3.5 C on April 1 to 10 C on July 31

Integrating other sensors

• Colaco and Bramley (2018) – “New approaches to sensor-based site-specific N management are needed and it is likely the best approaches will arise from the use of multiple sensors.”

• Added remote measurements of canopy height

• LiDAR-Lite + Arduino UNO

Colaco, A.F. and R.G.V. Bramley. 2018. Do crop sensors promote improved nitrogen management in grain crops? Field Crops Res. 218; 126-140.

Canopy height estimations are underutilized

Canopy height estimations are underutilized

Value of simplifying and integrating

Yield CP NDF NDFd

R2 λ R2 λ R2 λ R2 λ

GDU base 5 0.26 - 0.76 - 0.81 - 0.31 -

GDU base scaled 0.47 - 0.87 - 0.87 - 0.48 -

VIS + NIR + SWIR full 0.80 5 0.84 7 0.84 13 0.81 11

VIS + NIR full 0.73 5 0.85 12 0.76 11 0.79 6

VIS + NIR reduced 0.64 3 0.72 5 0.71 6 0.70 5

VIS + NIR + GDU base scaled 0.66 3 0.91 5 0.89 6 0.76 5

VIS + NIR + LIDAR 0.89 3 0.66 5 0.67 6 0.70 5

VIS + NIR + LIDAR + GDUbase scaled 0.89 3 0.85 5 0.79 6 0.87 5

Noland et al. (2018)

Value of simplifying and integrating

Yield CP NDF NDFd

R2 λ R2 λ R2 λ R2 λ

GDU base 5 0.26 - 0.76 - 0.81 - 0.31 -

GDU base scaled 0.47 - 0.87 - 0.87 - 0.48 -

VIS + NIR + SWIR full 0.80 5 0.84 7 0.84 13 0.81 11

VIS + NIR full 0.73 5 0.85 12 0.76 11 0.79 6

VIS + NIR reduced 0.64 3 0.72 5 0.71 6 0.70 5

VIS + NIR + GDU base scaled 0.66 3 0.91 5 0.89 6 0.76 5

VIS + NIR + LIDAR 0.89 3 0.66 5 0.67 6 0.70 5

VIS + NIR + LIDAR + GDUbase scaled 0.89 3 0.85 5 0.79 6 0.87 5

Noland et al. (2018)

Value of simplifying and integrating

Yield CP NDF NDFd

R2 λ R2 λ R2 λ R2 λ

GDU base 5 0.26 - 0.76 - 0.81 - 0.31 -

GDU base scaled 0.47 - 0.87 - 0.87 - 0.48 -

VIS + NIR + SWIR full 0.80 5 0.84 7 0.84 13 0.81 11

VIS + NIR full 0.73 5 0.85 12 0.76 11 0.79 6

VIS + NIR reduced 0.64 3 0.72 5 0.71 6 0.70 5

VIS + NIR + GDU base scaled 0.66 3 0.91 5 0.89 6 0.76 5

VIS + NIR + LIDAR 0.89 3 0.66 5 0.67 6 0.70 5

VIS + NIR + LIDAR + GDUbase scaled 0.89 3 0.85 5 0.79 6 0.87 5

Noland et al. (2018)

Value of simplifying and integrating

Yield CP NDF NDFd

R2 λ R2 λ R2 λ R2 λ

GDU base 5 0.26 - 0.76 - 0.81 - 0.31 -

GDU base scaled 0.47 - 0.87 - 0.87 - 0.48 -

VIS + NIR + SWIR full 0.80 5 0.84 7 0.84 13 0.81 11

VIS + NIR full 0.73 5 0.85 12 0.76 11 0.79 6

VIS + NIR reduced 0.64 3 0.72 5 0.71 6 0.70 5

VIS + NIR + GDU base scaled 0.66 3 0.91 5 0.89 6 0.76 5

VIS + NIR + LIDAR 0.89 3 0.66 5 0.67 6 0.70 5

VIS + NIR + LIDAR + GDUbase scaled 0.89 3 0.85 5 0.79 6 0.87 5

Noland et al. (2018)

Case 2: Cotton N status (Daughtry et al.)

• Site: Tifton, GA

• 2 years (2017 – 2018)

• 6 fertilizer N rates

• Spectral data (Sequoia) collected with UAV at 5 growth stages

• Tissue nutrient analyses accompanied each flight

• Average NDVI and NDRE extracted per plot

2017 Lint Yield

Slide credit: David Daughtry

2017 Leaf N Correlation

1st Square

1st WOB

7th WOB5th WOB

3rd WOB

Slide credit: David Daughtry

Cotton tissue N: Same “day after planting”

Cotton tissue N across the “mid-season”

Objectives:1. Assess growth stage and different

canopy sensing tools for predictions of yield and N demand.

2. Evaluate soil N content as a model parameter.

Approach:• 9 site-years from 2014-2015

• Varying soil types and environmental conditions

• Varying N fertilization levels

Case 3: Minnesota corn N management (Paiao et al., 2017)

• Active canopy sensing:• SPAD 502• GreenSeeker – 505 (GS-NDVI)• RapidSCAN CS-45 (RS-NDVI and RS-NDRE)• V4, V8, V12 and R1

MeasurementsSoil N content:

• NO3- and NH4

+

• 0-30 and 0-60 cm• V4, V8 and V12

Paiao et al. (2017)

0

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0.8

1

V4 V8 V12 R1 V4 V8 V12 R1 V4 V8 V12 R1 V4 V8 V12 R1

SPAD GS-NDVI RS-NDVI RS-NDRE

R2Grain yield predictions by sensor and growth stage

Paiao et al. (2017)

V4 Stage<10% of N needs

Where should our expectations be?

R2=0.65

SPAD at V4 vs. Grain yield

Paiao et al. (2017)

R2=0.65

R2=0.62

R2=0.57

R2=0.63

Sensors at V4 vs. Grain yield

Paiao et al. (2017)

SPAD

RS-NDVI RS-NDRE

GS-NDVI

R2=0.85

R2=0.77 R2=0.83

R2=0.75

Sensors at V8 vs. Grain yield

Paiao et al. (2017)

SPAD

RS-NDVI RS-NDRE

GS-NDVI

* Lower AIC means better fit

Predicitve Tool AIC* R2

Sensor only 784 0.34Sensor + 0-60 cm TIN 729 0.78Sensor + 0-30 cm TIN 735 0.74Sensor + 0-60 cm NO3

- 731 0.79Sensor + 0-30 cm NO3

- 741 0.76

V4 Soil NO3- @ 0-30 cm is the best approach to improve

predictive power

Integrating soil N measurements

Paiao et al. (2017)

Predicted ND (kg N ha-1)

Obs

erve

d N

D (K

g N

ha-1

)

RMSE = 42 Kg N ha-1

R2 = 0.67 RMSE = 75 Kg N ha-1

R2 = 0.15

• Including measurements of soil NO3 improves the utility of remote measurements

Adding parameters for estimations of corn N demand

Figures from Paiao (2017)

aba a ab ab

b

c

b abab a a

0

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4

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10

12

PP V2 V4 V6 V8 V12

Gra

inY

ield

(Mg

corn

ha-1

)

Application Timing

Normal Years Wet Years

N-timing vs. rainfall impacts on corn yield

Paiao et al. (2017)

Integrating weather data – Predicting Tissue N

V4 measurement R2 V8 measurement R2

NDRE 0.77 NDRE 0.80

GDUs 0.63 GDUs 0.26

NDRE + GDUs 0.81 NDRE + GDUs 0.82

NDRE + GDUs + Rainfall 0.82 NDRE + GDUs + Rainfall 0.83

Yield 0.14 Yield 0.56

*Raw correlations among means (Calibration data = Validation data).

• Earlier measurements had lower the predictive power, but the greater flexibility for N management

• Later measurements the greater predictive power, but the lowest flexibility for N management

• Soil 0-30 cm NO3- @ V4 stage holds

potential by itself or in combination with early-season sensor measurements to improve ND predictions

General Conclusions (Paiao et al., 2017)

Supporting work

• Used 49 sites to illustrate how weather and soil information can improve in-season N recommendations.

Implications Moving Forward

• Be critical of which wavebands and indices we emphasize

• We have immediate ability to assess the value of different predictors • Environmental factors• Other simple sensors / measurements• Underutilized wavebands

• Keep in sight a balance of simplification, efficacy, and overall value

References

• Bean, G.M., N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A.M. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf, J. Schepers, and J.S. Shanahan. 2018. Improving an active-optical reflectance sensor algorithm using soil and weather information. Agron. J. 110:2541–2551.

• Blackmer, T.M., J.S. Schepers, G.E. Varvel, and E.A. Walter-Shea. 1996. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron. J. 88:1-5.

• Colaco, A.F. and R.G.V. Bramley. 2018. Do crop sensors promote improved nitrogen management in grain crops? Field Crops Res. 218; 126-140.

• Noland, R.L., M.S. Wells, J.A. Coulter, T. Tiede, J.M. Baker, K.L. Martinson, and C.C. Sheaffer. 2018. Estimating alfalfa yield and nutritive value using remote sensing and air temperature. Field Crops Res. 222:189-196.

• Paiao, G.D. 2017. Can active canopy sensing technologies and soil nitrogen content help us improve corn-nitrogen management in Minnesota? M.S. Thesis. University of Minnesota.

• Read, J.J., L. Tarpley, J.M. McKinion, and K.R. Reddy. 2002. Narrow-waveband reflectance for remote estimation of nitrogen status in cotton. J. Environ. Qual. 31:1442-1452.

• Sharratt, B.S., C.C. Sheaffer, and D.G. Baker. 1989. Base temperature for the application of the growing-degree-day model to field-grown alfalfa. Field Crops Res. 21:95-102.

• Tarpley, L., K.R. Reddy, and G.F. Sassenrath-Cole. 2000. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Sci. 40:1814-1819.

Questions?

• Email: reagan.noland@ag.tamu.edu• Twitter: @WTXAgronomy