Simplified Tools and Low-Hanging Data for Useful …...Simplified Tools and Low-Hanging Data for...
Transcript of Simplified Tools and Low-Hanging Data for Useful …...Simplified Tools and Low-Hanging Data for...
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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
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Why remote measurements?
• Quick, non-destructive assessment
• Information at the field scale
• Objective comparisons
• Economic efficiency?
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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
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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
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Specific applications may require specific toolsExamples:• Crop N status• Maturity for harvest decisions
• Yield estimates• Detecting pest / disease outbreaks
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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.
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Case 1: Optimizing Yield and Quality in Alfalfa
Rel
ativ
e C
hara
cter
istic
s(ASA, 2011)
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Experimental ApproachMeasured canopy reflectance prior to destructive sampling in a wide range of alfalfa maturityRosemount, MN (2014-2015)
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Reflectance Data Collection
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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
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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
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Limiting economic factors for spectral sensors
• Spectral Range
• Spectral resolution
• Number of bands
$
==
=
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• 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
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Balancing Simplicity with Specialization
Read et al. (2002)
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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
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GDUs alone can inform timing of first alfalfa harvest
20
25
30
35
40
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0 200 400 600 800 1000 1200 1400
% N
DF
Cumulative GDD (base 41 ᵒF)
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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
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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.
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Canopy height estimations are underutilized
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Canopy height estimations are underutilized
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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)
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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)
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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)
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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)
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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
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2017 Lint Yield
Slide credit: David Daughtry
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2017 Leaf N Correlation
1st Square
1st WOB
7th WOB5th WOB
3rd WOB
Slide credit: David Daughtry
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Cotton tissue N: Same “day after planting”
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Cotton tissue N across the “mid-season”
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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)
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• 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)
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0
0.2
0.4
0.6
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)
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V4 Stage<10% of N needs
Where should our expectations be?
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R2=0.65
SPAD at V4 vs. Grain yield
Paiao et al. (2017)
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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
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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
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* 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)
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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)
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aba a ab ab
b
c
b abab a a
0
2
4
6
8
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)
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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).
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• 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)
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Supporting work
• Used 49 sites to illustrate how weather and soil information can improve in-season N recommendations.
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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
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References
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• 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.
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• 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.