Iowa Soybean Association Farmer Research Conference

Transcription

1 Iowa Soybean Association Farmer Research Conference February 7, 2018 Josh Pritsolas and Randy Pearson, Ph.D. Laboratory for Applied Spatial Analysis (LASA) Southern Illinois University Edwardsville Edwardsville, IL

2 In 2015, Dr. Peter Kyveryga of ISA, posed the question: Is NDVI the best vegetation index (VI) for assessing corn and soybean? Major obstacle to answering this question: Image calibration virtually all VIs being produced in 2015 were being calculated using uncalibrated raw digital numbers. VIs were initially designed to utilize calibrated data in percent reflectance.

3 Growing Season 2015: Worked closely with ISA to identify remote sensing test site in central Iowa. Purchased four calibration tarps (3, 6, 12, 22, 44, and 56%). Flew two airborne imaging system over test site during growing season (approximately twice per month). Developed calibration model to convert digital data to percent reflectance. Applied this model to all aerial imagery acquired for temporal comparison.

4 Growing Season 2016: Developed automation process for calibration resulting in the computation of up to 15 vegetation indices. Identify a second remote sensing test site. Purchased a second set of tarps (3, 6, 12, 33, 56, and 66%) to allow for comparison between the two sites. Increased data providers from 2 to 4. Added elements of research beyond developing calibration methods (spatial resolutions, spectral resolutions, image mosaicking, and the impact of sun angle on digital imagery).

5 Calibration Site: Story County, Iowa Corn Field 2 N Corn Field 3 Soybean Field 2 Corn Field 1 Soybean Field 1

6 Tetracam Commercial Grade Calibration Tarps 3, 6, 12, 22, 44, and 56% 3, 6, 12, 33, 56, and 66%

7 When data was nonlinear it was most likely an effect of the post processing conversion of high bit level data (i.e. 12-bit or 16- bit) to a lower and more manageable bit level (i.e. 8-bit)

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9 May 22 Jun 01 Jun 09 Jul 05 Jul 21 Aug 13 Sep 01 Sep 11 Sep 14

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12 Growing Season 2017: Calibration: Continued collection of data from multiple image providers. Revisions and refinement of calibration methods. Assessment of VIs: Quantitative evaluation of VIs and individual reflectance bands for crop health (as related to final crop yield). Identification of important image capture dates (based on different crop stages) for determining crop health.

13 Theoretical NDVI Values at Various Corn Stages < 0.4 Graphic Obtained from blog.extension.uga.edu Graphic Obtained from earthobservatory.nasa.gov NDVI = "#$ & $'( "#$ ) $'( Calibrated NDVI Theoretical NDVI Values at Various Soybean Stages < 0.4 Graphic Obtained from blog.extension.uga.edu

14 Vegetation Index Name Type Abbrev. Equation References Ratio vegetation index (also Jordan (1969); Pearson called simple ratio) Red & NIR RVI Rn/Rr and Miller (1972) Difference vegetation index Red & NIR DVI Rn Rr Normalized difference vegetation index Red & NIR NDVI (Rn Rr)/(Rn + Rr) Richardson and Wiegand (1977) Rouse et al. (1974); Deering (1978); Tucker (1979) Soil adjusted vegetation index Red & NIR SAVI ( ) (Rn Rr)/(Rn + Rr + 0.5) Huete (1988) Modified soil adjusted vegetation index Optimized soil adjusted vegetation index Triangular vegetation index Second modified triangular vegetation index Chlorophyll vegetation index Green normalized difference vegetation index Red & NIR MSAVI 0.5{2*Rn + 1 [(2*Rn + 1)² 8(Rn Rr)]} Qi et al. (1994) Red & NIR OSAVI ( ) (Rn Rr)/(Rn + Rr ) Rondeaux, Steven, and Baret (1996) Vis & NIR TVI 0.5[120(Rn Rg) 200(Rr Rg)] Broge and Leblanc (2000) Vis & NIR MTVI2 1.5[2.5(Rn Rg) 2.5(Rr Rg)]/ Haboudane et al. [(2*Rn + 1)² 6*Rn 5* (Rr) 0.5] (2004) Vis & NIR CVI Rn*Rr/Rg² Vincini, Frazzi, and D Alessio (2008) Green & NIR GNDVI (Rn Rg)/(Rn + Rg) Gitelson, Kaufman, and Merzlyak (1996) Chlorophyll index-green Green & NIR CIG Rn/Rg 1 Gitelson, Gritz, and Merzlyak (2003) Normalized green red Vis NGRDI (Rg Rr)/(Rg + Rr) Tucker (1979)

15 Remote sensing (12 VIs and 3 individual bands) and yield monitor data were aggregated to 0.15 acre zones. Ran correlation and regression analysis for each individual VI and individual date (to identify potential variables to eliminate). To increase model strength, multiple linear regression was desired, but it was suspected that the independent variables would have high multicollinearity. Data exploration was conducted using Factor Analysis. Validation of high multicollinearity. Analyzed each individual index over all 9 dates of imagery capture. Analyzed all indices collectively for a given date. Tested different rotation methods and factor extractions. Determined a rank order of significance for each index on a given date. Factor Analysis eliminated multicollinearity and the resulting factors were able to be utilized in multiple linear regression models.

16 Soybean - Field 2 Index Date r R² Trend CVI 9-Jun Jun Jun Jul Jul Aug Sep Pos 3-Sep Pos 12-Sep Corn - Field 3 Index Date r R² Trend GNDVI 9-Jun Jun Jun Pos 12-Jul Jul Pos 15-Aug Pos 1-Sep Pos 3-Sep Pos 12-Sep Soybean - Field 2 Index Date r R² Trend NDVI 9-Jun Jun Jun Jul Jul Aug Sep Pos 3-Sep Pos 12-Sep Pos Corn - Field 3 Index Date r R² Trend MTVI2 9-Jun Jun Pos 30-Jun Pos 12-Jul Jul Aug Sep Sep Pos 12-Sep Soybean - Field 2 Index Date r R² Trend SAVI 9-Jun Jun Jun Jul Jul Aug Sep Pos 3-Sep Pos 12-Sep Pos Corn - Field 3 Index Date r R² Trend NIR 9-Jun Jun Jun Pos 12-Jul Jul Aug Sep Sep Sep

17 Testing the multicollinearity of all VIs identified that 86 percent of the 9,045 different combinations of independent variables in the correlation matrix were significantly correlated (p < 0.05). * Kaiser-Meyer-Olkin Test = and Bartlett's Test of Sphericity was significant at p < 0.001

18 Data exploration was conducted using Factor Analysis. Validation of high multicollinearity. Analyzed each individual index over all 9 dates of imagery capture. Analyzed all indices collectively for a given date. Tested different rotation methods and factor extractions. Determined a rank order of significance for each index on a given date.

19 Example of SAVI for Soybean Field 2 Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % Rotated Component.040 Matrix Component Jun9SAVI Jun16SAVI Jun30SAVI Jul12SAVI Jul31SAVI Aug15SAVI Sep1SAVI Sep3SAVI Sep12SAVI

20 Used transformed factors in linear regression (these data no longer display any correlation). Example of regression model for Difference Vegetation Index (DVI) from Soybean Field 2 (2015): N = 265 data points R² = Standard Error = 1.76 bu/a Yield estimate equation from DVI Y = (Jun30 Factor) (Sep1 Factor) (Sep12 Factor) (Aug15 Factor) (Jul12 Factor) (Sep3 Factor) (Jun9 Factor) (Jun16 Factor)

21 Index Soy1 R² Rank Index Soy2 R² Rank Sum Rank SAVI SAVI OSAVI OSAVI NDVI NDVI TVI TVI DVI DVI NIR NIR RVI RVI GNDVI GNDVI MSAVI MSAVI Red Red MTVI MTVI CIG CIG CVI CVI NGRDI NGRDI Green Green

22 Calibration Site: Story County, Iowa Corn Field 2 N Corn Field 3 Soybean Field 2 Corn Field 1 Soybean Field 1

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25 Predicted Yield Map from SAVI Yield Map from Yield Monitor Bu/A R² = S.E. = 2.00 Predicted Yield Map from TVI > <45 R² = S.E. = 1.77

26 Example of GNDVI for Corn Field 3 Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % Rotated Component.033 Matrix Component Jun9GNDVI Jun16GNDVI Jun30GNDVI Jul12GNDVI Jul31GNDVI Aug15GNDVI Sep1GNDVI Sep3GNDVI Sep12GNDVI

27 Used transformed factors in linear regression (these data no longer display any correlation). Example of regression model for GNDVI from Corn Field 3 (2015): N = 282 data points R² = Standard Error = 7.49 bu/a Yield estimate equation from GNDVI Y = (Jun9 Factor) 2.17 (Jul12 Factor) (Sep12 Factor) (Sep3 Factor) (Jun30 Factor) (Jul31 Factor) (Aug15 Factor) (Jun16 Factor) (Sep1 Factor)

28 Index Corn1 R² Rank Index Corn3 R² Rank Sum Rank GNDVI GNDVI CIG CIG RVI RVI OSAVI OSAVI SAVI SAVI NDVI NDVI Green Green CVI CVI MTVI MTVI MSAVI MSAVI NGRDI NGRDI DVI DVI TVI TVI Red Red NIR NIR

29 Calibration Site: Story County, Iowa Corn Field 2 N Corn Field 3 Soybean Field 2 Corn Field 1 Soybean Field 1

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32 Predicted Yield Map from GNDVI Yield Map from Yield Monitor Bu/A >220 R² = S.E. = 7.49 Predicted Yield Map from CIG <160 R² = S.E. = 7.33

33 How early in the growing season can vegetation indices be used to reliably assess crop health (and potential crop yield), based on deviation from normal temporal VI curves? What crop stages and indices are optimal for addressing this question?

34 Model Classification Using Early Season Dates Error Matrix for Early Season Crop Health Assessment Pred Low Pred High Low 87.8% 7.9% High 22.2% 92.1%

35 Model Classification Using Early Season Dates Error Matrix for Early Season Crop Health Assessment Pred Low Pred High Low 85.3% 2.4% High 14.7% 97.6%

36 True vegetation indices should be calculated from imagery that has been converted into percent reflectance. There is a very strong relationship between multi-date remotely sensed imagery and soybean yield. There is a very strong relationship between multi-date remotely sensed imagery and corn yield. NDVI is not necessarily the best vegetation index for identifying crop stressed that relate to yield. Image acquisition date (crop stage) plays an important role in identifying crop stress that relates to yield loss.

37 Randy Pearson: Josh Pritsolas