Determining Phosphorus Concentration using Spectral Reflectance of grass and soil samples in the Lake Okeechobee basins in UV, VIS, and NIR regions

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1 Determining Phosphorus Concentration using Spectral Reflectance of grass and soil samples in the Lake Okeechobee basins in UV, VIS, and NIR regions I. Bogrekci, and W. S. Lee Agricultural and Biological Engineering Department University of Florida Gainesville, Florida ABSTRACT This study aimed to determine phosphorus (P) concentration using reflectance spectra of grass and soils in the Lake Okeechobee drainage basins, Florida. A total of 15 grass samples and 15 soil samples were collected from the three sites in the Lake Okeechobee drainage basins. The reflectance spectra of both fresh and dried samples for grass and soil were measured using UV, VIS, and NIR spectroscopy in nm with an interval of 1 nm. P concentrations of the samples were correlated with the reflectance of the same samples. Two third of both vegetation and soil samples were used for calibration and remaining one third of vegetation and soils were used for validation. Multiple stepwise linear regressions (MLR), and partial least squares (PLS) analyses were applied to the data sets in order to predict P concentrations for soil and grass. Tukey s means test was performed in order to compare the means of P concentrations for three sites. Also, actual and predicted P concentration maps of the fields for vegetation and soil were plotted. Strong relationships (R 2 =.778,.869, and.92, PLS) for validation data sets were obtained between absorbance and P concentrations in soils. Spatial variation in actual and predicted maps showed that P variability could be represented using diffuse reflectance spectroscopy in UV, VIS, and NIR regions. Keywords: Spectral Reflectance, Grass, Pasture,, Sensor, Phosphorus, Lake Okeechobee, UV, VIS, NIR, PLS, MLR. 1

2 INTRODUCTION Reflected light from soil and vegetation carries some information about the material interacted. This nature of electromagnetic radiation was studied by many researchers. Nitrogen (N) and phosphorus (P) content in spring barley was investigated using hyperspectral line scanning in visible range (Christensen et al., 24). Near infrared reflectance spectroscopy was used for determination of P concentrations in sugarcane leaves (Chen et al., 22). reflectance measurements were used to predict such soil properties: P and K for different soil orders (Lee et al., 23), soil moisture and organic matter (Varvel et al., 1999, and Hummel et al., 21), soil mineral N (Ehsani et al., 1999). However, no studies have been conducted to obtain soil and vegetation spectra in ultraviolet (UV), visible (VIS), and near infrared (NIR) regions combined. This study addressed the possibilities of P concentration determination by using diffuse reflectance spectroscopy in UV, VIS, and NIR regions and analyzing data by a linear partial least squares (PLS) method and stepwise multiple linear regressions (MLR) with water soluble, total, and Mehlich-1 P concentrations for soils and total P for vegetation. OBJECTIVE The objective of this investigation was to develop calibration models to be used in developing a P-sensor for the determination of soil and vegetation P contents in the lake Okeechobee drainage basins, Florida. The specific objectives were to: 1. Collect soil and vegetation samples in a wide range of P concentrations 2. Measure reflectance spectra of soils and vegetations 3. Remove noise from the spectra 4. Compute significant absorbance bands 5. Develop calibration models to predict P concentrations for soil and vegetation MATERIALS AND METHODS Data collection and grass samples were collected from three different sites (Candler Farm, Palaez Ranch South, and Woody Larson) in the Lake Okeechobee drainage basins in Florida. Grid center method was used to collect soils and vegetation from each site. A total of 15 soil and 15 vegetation samples were collected. Vegetation samples were collected using a gardener scissors. samples were collected using a soil-sampling auger at a 15 cm depth at each sampling point. samples were collected right underneath of the vegetation samples. Each soil sample weighed between 2-4 grams. Geo-coordinates of each sampling point for each site were recorded using a DGPS receiver (March II, Corvallis Microtechnology, Inc.). samples were analyzed for Mehlich-1 P, water soluble P, total P, and total organic P to determine soil P concentrations. 2

3 Vegetation samples were analyzed for total P, to determine vegetation P concentrations. Diffuse reflectance measurement A spectrophotometer (Cary 5 Scan UV-VIS-NIR, Varian Inc.) equipped with a diffuse reflectance accessory (DRA-CA-55, Labsphere) was used to collect spectral reflectance data for each soil sample. After reflectance measurement of the wet soils and fresh vegetations in nm, the wet soil samples were oven-dried at 14 C for 24 hours and fresh vegetation samples were oven-dried at 6 C for 72 hours. Prior to dry vegetation and soil reflectance measurement, soils were sieved using a.6 µm mesh size sieve and ground in order to remove the particle size effect on reflectance spectra. After drying, the vegetation samples were ground. Reflectance was measured for each dried soil and vegetation sample in nm with an increment of 1 nm. Spectral reflectance for each soil and vegetation sample was collected using baseline correction mode. Spectral signal processing Reflectance of all samples were converted into absorbance before further analysis in order to find relationship between P concentrations and absorption of lights at different wavelength using the Beer-Lambert s law (Williams and Norris, 21). The data was filtered using Savitzky-Golay polynomial convolution filter to remove the noise in the signal. Data analysis A stepwise multiple linear regressions (MLR) (Proc REG, SAS/STAT, SAS Inc.) and partial least squares (PLS) (Proc PLS, SAS/STAT, SAS Inc.) were used to develop calibration and prediction models for the determination of P concentration in soil and vegetation samples. Tukey s means test was conducted in order to compare the means of P concentrations in the soil samples (Proc ANOVA, SAS/STAT, SAS Inc.). A surface mapping system software (Surfer Version 7.2, Golden Software Inc.) was used to prepare P concentration maps of one of the three sampling sites, Candler Farm, in the Okeechobee County, Florida. An actual P and PLS-predicted P map were plotted in total P, water soluble P, and Mehlich-1 P for soils and in total P for vegetation using Kriging interpolation method. RESULTS AND DISCUSSION Vegetation and soil wet chemistry results and vegetation samples were collected from the three sites in the range of very low to extremely high P concentrations. Range, mean, standard deviation, and coefficient of variance of P concentrations in soil and vegetation for the three 3

4 sites are listed in table 1. With these three sites, very good distribution of P concentrations was obtained in order to proceed for developing calibration models. The highest P concentrations for soils were found in Candler Farm. samples with the lowest P were from Woody Larson. The highest standard deviation of P concentrations in soils was seen in Candler farm. Also means test for Mehlich-1 P concentrations of the soil samples showed that soils in Candler Farm is significantly different than the others. Table 1. Chemical analysis results of the vegetation and soil samples obtained from the Lake Okeechobee drainage basins (mg/kg). Field Sample Extraction method Range Mean Standard Deviation Coefficient of Variance Vegetation Total-P Candler Farm Total-P Mehlich 1-P a* Water-P Vegetation Total-P Palaez Ranch-S Total-P Mehlich 1-P b Water-P Vegetation Total-P Woody Larson Total-P Mehlich 1-P b Water-P * Means with the same letter are not significantly different (P>.1). Vegetation and soil spectra Most spectroscopy studies on vegetation and soils focused on visible and near infrared regions of the electromagnetic spectrum due to many reasons such as: cost, availability, and sensitivity etc. However, in this study, UV region was added to explore more possibility. UV-VIS-NIR absorbance spectra of fresh and dry vegetation are plotted in figure 1. Differences in absorbance of fresh and dry vegetation showed that water is strong absorber of light in NIR region. UV region in nm showed the least water absorption effect on spectra. Furthermore, vegetation absorbed more light in UV region than in VIS and NIR. Figure 2 shows the wet and dry spectra of a sandy soil sample in nm. Sandy soils 4

5 absorbed more light in UV than in VIS and NIR. Light absorption of a dried soil sample sharply decreased from UV to VIS. However, it flattened in NIR region. 1.9 Dry Fresh.8.7 Absorbance Wavelength (nm) Figure 1. Absorbance of a vegetation sample in fresh and dried conditions in nm. 1.9 Dry Wet.8.7 Absorbance Wavelength (nm) Figure 2. Absorbance of a soil sample in wet and dry conditions in nm. 5

6 P concentration Prediction A stepwise multiple linear regressions and linear partial least squares yielded strong relationships between absorbance and concentration of the soil samples for P concentration determination. PLS and MLR analyses results are listed in table 2. Three different types for P concentration in soils were also compared. Overall, PLS analysis results were better than MLR results. The errors (RMSE) were smaller for PLS than for MLR. The coefficients of determinations (R 2 ) were also higher for PLS than for MLR in the validation data sets. When the validation results for soils were compared using PLS, total P yielded a strong relationship with absorbance (R 2 =.92). Although root mean square error (RMSE) seemed higher in total P for soils as mg/kg, percent error was actually smaller when P range (table 1) was considered for total P in soils. The validation for the vegetation yielded R 2 of.425 and.232 for PLS and MLR, respectively. Although the R 2 of vegetations were low, errors (RMSE) were 19.1% and 22.9% for PLS and MLR, respectively. Table 2. PLS and MLR results of dry soil and vegetation for the calibration and validation data sets. Calibration Validation Sample Extraction method Statistical Method R 2 RMSE (mg/kg) R 2 RMSE (mg/kg) Mehlich1-P PLS MLR Total-P PLS MLR Water-P PLS MLR Vegetation Total-P PLS MLR

7 P (mg/kg) P (mg/kg) (a) (b) P (mg/kg) P (mg/kg) (c) (d) Figure 3. Actual (left) and PLS-Predicted (right) P concentration in Candler Farm, Okeechobee County, Florida: (a) water soluble P in soil, (b) total P in soil, (c) Mehlich-1 P in soil, and (d) total P in vegetation. Actual and PLS-predicted P concentrations for soil and vegetation using different chemical analysis methods are plotted in figure 3. These maps were generated using Kriging method. Figure 3a showed good spatial variability correlation between water-soluble P and absorbance predicted-p using PLS regressions. Overall high concentration areas in actual P maps were predicted as high P concentration areas while low concentration areas were estimated as low concentration areas. All three-soil chemical analysis methods were able to 7

8 represent P variability in Candler Farm (figures, 3a, 3b, and 3c). P variability for vegetation is shown in figure 3d. Spatial variability of actual and PLS-predicted P concentrations for vegetation were similar. High concentration areas were predicted as high while low concentration areas were predicted as low regions. Hence, absorbance derived P concentrations with PLS regressions were successfully able to identify spatial P variability in the same field in producing similar P maps to the chemical derived P maps. SUMMARY AND CONCLUSION and vegetation samples were collected from three sites in the Lake Okeechobee drainage basins and analyzed for total P, water-soluble P, and Mehlich-1 P. Diffuse reflectance spectroscopy was used to obtain reflectance spectra of the samples in nm. Absorbance and P concentration with different chemical analysis were analyzed by MLR and PLS regressions. Chemical and absorbance derived P concentrations were used to produce P maps for three sites. Overall, PLS analysis results were better than MLR results. The errors (RMSE) were smaller for PLS than for MLR. The coefficients of determinations (R 2 ) were higher for PLS than for MLR in the validation data sets. Predicted phosphorus maps showed that P content of soil and vegetation could be predicted using diffuse reflectance measurements in the range of nm. ACKNOWLEDGEMENTS The authors would like to thank the Florida Department of Agriculture and Consumer Services for funding this project. REFERENCES Chen, M., B. Glaz, R. A. Gilbert, S. H. Daroub, F. E. Barton II, and Y. Wan. 22. Agronomy Journal 94: Christensen, L. K., B. S. Bennedsen, R. N. Jørgensen, and H. Nielsen. 24. Modelling nitrogen and phosphorus content at the early growth stages in spring barley using hyperspectral line scanning. Biosystems Engineering 88(1): Ehsani, M. R., S. K. Upadhyaya, D. Slaughter, S. Shafii, and M. Pelletier A NIR technique for rapid determination of soil mineral nitrogen. Precision Agriculture 1(2):

9 Hummel, J. W., K. A. Sudduth, and S. E. Hollinger. 21. moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and Electronics in Agriculture 32: Lee, W. S., J. F. Sanchez, R. S. Mylavarapu, and J. S. Choe. 23. Estimating chemical properties of Florida soils using spectral reflectance. Trans. ASAE 46(5): Varvel, G. E., M. R. Schlemmer, and J. S. Schepers Relationship between spectral data from an aerial image and soil organic matter and phosphorus levels. Precision Agriculture 1: Williams, P., and K. Norris. 21. Near-infrared technology in the agricultural and food industries. 2nd ed. St. Paul, Minn.: American Association of Cereal Chemists, Inc. 9