The Effects of Soil Moisture Content on Reflectance Spectra of Soils Using UV-VIS-NIR Spectroscopy ABSTRACT

Transcription

1 The Effects of Soil Moisture Content on Reflectance Spectra of Soils Using UV-VIS-NIR Spectroscopy I. Bogrekci, and W. S. Lee Agricultural and Biological Engineering Department University of Florida Gainesville, Florida ABSTRACT This study was conducted to investigate the effects of soil moisture content on reflectance spectra of sandy soils with different phosphorus (P) concentrations using UV-VIS-NIR reflectance spectroscopy. Sandy soils with average particle size of 25 µm were used. Investigations were conducted at, 12.5, 62.5, 175, 375, 75, and 1 mg/kg phosphorus application rates. Three soil moisture contents, i.e., 4%, 8%, and 12 % were investigated. P concentrations of the soil samples were analyzed and reflectance of the samples was measured between nm with a 1 nm interval. Dried soil sample reflected more lights than wet soils in nm. As moisture content of soils increased, reflectance from the soil sample decreased, which indicates that water is a strong light absorber than a sandy soil sample. Wet soil spectra was reconstructed by removing the moisture content effect and compared with the dry spectra of the same soil sample. Absorbance and reconstructed absorbance data were prepared as calibration and validation data sets in order to measure the performance of the spectral signal processing used in the removal of the effect of moisture content on absorbance spectra. A partial least squares (PLS) analysis was applied to the data to predict P concentration before and after processing the spectra. The results showed that spectral signal processing by removing the moisture effect improved considerably P prediction in soils. Keywords: Reflectance, Sensor, Phosphorus, Phosphates, Lake Okeechobee, UV, VIS, NIR, spectroscopy, PLS, moisture content. 1

2 INTRODUCTION Moisture content of soils has been always concern for measurement of soil properties. Many researchers used spectral reflection to determine moisture content of a soil sample. There have been a lot of such research activities, however, only few recent studies were mentioned here. Soil moisture and vegetation cover had a negative influence on the prediction capabilities for both soil properties, organic matter and clay content, using field spectroscopy (Kooistra et al., 23). Galvao and Vitorello (1998) investigated linear relationship (soil lines) between conventional red (R) and NIR in 5-11 nm for the effects of spectral positioning and widths of approximately simulated bands of some broad and narrow band sensors, and for the influence of the chemical constituent and moisture in the soil samples. Hummel et al., (21) studied soil moisture and organic matter prediction of surface and subsurface soils using an NIR sensor. Phosphorus (Lee et al., 23, and Varvel et al., 1999, Bogrekci et al., 23) and phosphates (Yoon et al., 1993) and their spectral sensing were studied. In addition to previous studies, this research investigates both the effect of moisture content on soil reflectance spectra in nm in determining P concentrations from spectral information and remove the effect of moisture on reflectance spectra by re-constructing the wet spectra of a soil sample in order to improve prediction capability of P in soils. OBJECTIVE The objective of this research was to investigate the effects of soil moisture content on reflectance spectra for a sandy soil sample to develop a calibration model for predicting P concentration of unknown samples using diffuse reflectance spectroscopy in ultraviolet (UV), visible (VIS), and near infrared (NIR) regions. MATERIALS AND METHODS Soil sample preparation In order to study the effects of moisture content on reflectance spectra of soils, pure sandy soil was obtained from Edgar, Putnam County in Florida. Sandy soil was graded into three particle sizes using a sieve shaker (RO-TAP, W. S. TYLER, Inc.). Sandy soil particle sizes were 125, 25, 6 µm for fine, medium, and coarse, respectively. Soil samples were leached using.1 molar HCl acid solutions and de-ionized water in order to remove existing P. After leaching, ph and P concentration of sandy soils were analyzed. Soil ph was measured using a ph/temperature meter (HI 991, Hanna Instruments) and soil P was determined using a soil test kit (Luster Leaf Products, Inc.). If P was detected in the soil samples, further leaching was applied. Soil samples were incubated for seven days with all P concentrations. P solution was prepared from potassium phosphate monobasic (KH 2 PO 4, Fisher Scientific). P rates were, 12.5, 62.5, 175, 375, 75, and 1 mg/kg. P solution was added into soil and mixed thoroughly.

3 After each reflectance measurement of the wet soil samples, samples were ovendried at 14 C for 24 hours. The soil samples were sent to a laboratory for chemical analysis of P concentrations. All soil samples were analyzed for total P. Moisture effect experiment Phosphorus application rates with different moisture contents are listed in table 1. After incubation of soil with different P concentrations for seven days, the soil samples were air-dried thoroughly. Different amount of water was added into the dried soil samples to prepare samples with different moisture contents (4%, 8%, and 12% wet basis) and reflectance of the same sample was measured for all soil samples. There were seven different P concentrations, four different moisture contents, and four replications, which produced 112 soil reflectance spectra. Table 1. Sample preparation with different phosphorus concentrations and moisture contents. P Concentration (mg/kg) No P Very low Low Medium High Very high Extremely high Particle Size (µm) Medium (25) ph 6 Moisture Content (%) No moisture (dry) Low Medium High Spectral range (nm) 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 from each soil sample. Reflectance was measured for each soil sample in nm with an increment of 1 nm. Reflectance of the soil samples was measured before and after drying. Spectral signal pre-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 (Matlab R12, The MathWorks Inc.). 3

4 Data analysis In order to obtain better performance in sensing P concentrations of soils, the effect of moisture content on absorbance spectrum of a soil sample was removed. P concentrations from absorbance spectra of soils were calculated using both original absorbance spectra of soils with different moisture content and processed absorbance spectra of soils after moisture effect removal. To do this, firstly, moisture content of a soil sample was computed from absorbance spectra. Secondly, the effect of 1% moisture content in soil spectra in nm was calculated. Thirdly, moisture effect on absorbance spectra was removed. Lastly, PLS (Proc PLS, SAS/STAT, SAS Inc.) analysis was conducted with the original and the processed absorbance spectra to predict P concentrations of soils and to measure the performance of water effect removal from the wet soil spectra in determining P concentrations of soils. There were 54 and 53 soil spectra in calibration and validation data sets, respectively. Five spectra were discarded due to being outliers and experimental error. Two wavelengths, 145 nm and 194 nm are well-known water absorption bands (Williams and Norris, 21). The reflectance at 34 nm also presented no change with regard to moisture content and P concentrations. Therefore, these wavelengths were used to calculate moisture determination ratio (MDR) for measuring moisture content of a soil sample. where ( Aλ Aλ 194) MDR = (1) A λ34 MDR = Moisture determination ratio A λ145 = Absorbance at 145 nm A λ194 = Absorbance at 194 nm A λ34 = Absorbance at 34 nm. MDR values were calculated for each spectrum. Based on the MDR values, the samples were classified into different classes. If MDR value of a soil spectrum was less than one, the sample was classified as a dry soil sample. If MDR value of a soil spectrum was between 1 and 2.6, the sample was classified as a soil with 4% moisture content. MDR value of a soil spectrum between 2.6 and 2.88 was classified as a soil with 8% moisture content. MDR value of a soil spectrum more than 2.88 was classified as a soil with 12% moisture content. Also success ratio was calculated as correctly classified number of data was divided by the number of data in the validation set. RESULTS AND DISCUSSION Average reflectance spectra of four wet soil samples with 4%, 8%, and 12% moisture contents and four dry soil samples with no P concentrations are plotted 4

5 in figure 1. Reflectance of dry soils was higher than wet soils in nm. Reflectance decreased with an increase in moisture content for all wavelengths. However, the amount of reflectance change was not constant at all wavelengths due to water-light absorption properties. The water effect was observed as expected on reflectance spectra. Average reflectance of soils with different P concentrations in nm are given in figures 2, 3, 4, and 5 for 4%, 8%, 12% and % (dry) soil moisture contents, respectively. Soil reflectance spectra in showed that reflectance decreased with an increase in soil moisture. As the Beer-Lambert s law explained, reflectance decreased with an increase in P concentrations of soils. This relationship was observed clearly in NIR region for P (figures 2, 3, 4, and 5). However, the amount of reflectance change for each P concentration increase in NIR region became smaller as moisture content increased. In other words, reflectance caused by P concentrations in soils was more distinct for each P concentration if the soil sample was drier. The decrease in the amount of reflectance change for the same P concentrations was caused by moisture increase in soils and therefore, moisture effect should be corrected to improve capability of P prediction models. Correlation coefficient spectra of absorbance and P concentrations are depicted in figure 6 for different soil moisture contents. As seen from correlation coefficient spectra in nm, there is a high correlation between reflectance and P concentrations as the soil sample becomes drier Dry 4% 8% 12% 7 Reflectance (%) Figure 1. Average reflectance of both four wet soil samples at different moisture contents and four dry soils with no P concentrations in nm. 5

6 Reflectance (%) No P Very low Low Medium High Very high Extremely high Figure 2. Average reflectance of four soil samples at 4% moisture content with different P concentrations in nm Reflectance (%) No P Very low Low Medium High Very high Extremely high Figure 3. Average reflectance of four soil samples at 8% moisture content with different P concentrations and in nm. 6

7 6 5 4 Reflectance (%) No P Very low Low Medium High Very high Extremely high Figure 4. Average reflectance of four soil samples at 12% moisture content with different P concentrations and in nm Reflectance (%) No P Very low Low Medium High Very high Extremely high Figure 5. Average reflectance of four dried soil samples with different P concentrations in nm. 7

8 1.8 Correlation coefficient (r) Dry 4% 8% 12% Figure 6. Correlation coefficient spectra between absorbance and P concentrations at different moisture contents in nm. The results for determination of moisture content using MDR (eq.1) are listed in table 2. Using MDR and the defined class range, success ratios were 98.7% and 88.7% for the calibration and validation data sets. Table 2. Classification results of the validation set for determining soil moisture contents using MDR equation. Predicted number of samples Moisture Contents Actual number of samples Dry 4% 8% 12% Equivalent MDR values Dry % % % 3 12 >2.88 Success ratio

9 .6 Dry Wet Re-constructed.5.4 Absorbance Figure 7. Absorbance of a soil sample in wet and dry conditions and reconstructed absorbance of the same soil sample in nm. Absorbance of a wet and dry soil sample and reconstructed spectra from the same wet soil spectra in nm are shown in figure 7. Reconstructed spectra resembled the dry spectra of the same soil sample. This showed that the moisture content removal algorithm successfully reconstructed spectra equivalent to the original dry spectra from the wet spectra in nm. Partial least squares (PLS) analyses were applied to the calibration and validation data sets for both the original and reconstructed absorbance spectra with P concentrations. Soil P concentrations were predicted better when the moisture content effect on absorbance spectra of a soil was removed. Strong relationship (R 2 =.97, figure 8d) between actual and predicted P concentrations of soils was found for the validation data sets. Removing the moisture content effect on absorbance spectra yielded better prediction than original spectra using PLS. Error (root mean square error, RMSE) decreased from 151 mg/kg to 62 mg/kg when the original and reconstructed signals were used, respectively. 9

10 y =.9936x R 2 = y =.9682x R 2 = Predicted P (mg/kg) Predicted P (mg/kg) y =.9714x R 2 =.97 Actual P (mg/kg) (a) y =.9666x R 2 =.97 Actual P (mg/kg) (b) Predicted P (mg/kg) Predicted P (mg/kg) Actual P (mg/kg) (c) Actual P (mg/kg) (d) Figure 8. PLS results for: (a) original absorbance spectra, calibration data, (b) original absorbance spectra, validation data, (c) reconstructed absorbance spectra, calibration data, and (d) reconstructed absorbance spectra, validation data. CONCLUSION Moisture content effect on soil reflectance spectra in sensing soil P concentrations was investigated in UV, VIS, and NIR regions and the influence of moisture on spectra was observed. Correlation coefficient spectra between absorbance and P concentrations showed high correlations in nm. The dry soil spectrum was reconstructed from the wet soil spectrum by removing the moisture content effect. The reconstructed dry soil spectra resembled the original dry soil spectra. Spectral signal processing by removing the moisture effect improved P prediction in soils considerably. Error (RMSE) was reduced from 151 mg/kg to 62 mg/kg when reconstructed absorbance spectra were used. 1

11 ACKNOWLEDGEMENTS This research was supported by the Florida Department of Agriculture and Consumer Services for funding this project. REFERENCES Bogrekci, I., W. S. Lee, and J. Herrera. 23. Assessment of P-concentrations in the Lake Okeechobee drainage basins with spectroscopic reflectance of VIS and NIR. ASAE paper No St. Joseph, Mich.: ASAE. Galvao, L. S. and I. Vitorello Variability of laboratory measured soil lines of soils from southeastern Brazil. Remote Sens. Environ. 63: Hummel, J. W., K. A. Sudduth, and S. E. Hollinger. 21. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and Electronics in Agriculture 32: Kooistra, L., J. Wanders, G. F. Epema, R. S. E. W. Leuven, R. Wehrens, and L. M. C. Buydens. 23. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Analytica Chimica Acta 484: 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. Yoon, R. H., G. T. Adel, G. H. Luttrell, R. O. Claus, and K. A. Murphy An optical sensor for on-line analysis of phosphate minerals. Pub. No Florida Institute of Phosphate Research. USA. 11