Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach
Abstract
:1. Introduction of Arsenic Contaminations
2. Arsenic Concentration in Rice Plants
3. Use of Remote Sensing
3.1. Hyperspectral Reflectance Measurement
Spectral Data Pretreatments
- Scr = Continuum-removed spectra
- S = Original spectrum
- C = Continuum curve
3.2. Spectra Collection
4. Methods for Arsenic Measurement
4.1. Hydroponic Method for Evaluating Leaf and Canopy Reflectance of Stressed Rice Plants for As Contaminants
4.2. Estimation of Soil As Using Generated Model and Hyperspectral Remote Sensing
4.3. Visible Near-Infrared Diffuse Reflectance Spectroscopy (VisNIR-DRS) Approach
4.4. Fuzzy Overlay and Spatial Anisotropy Approach
4.5. Multivariate Hyperspectral Vegetation Indices
5. Limitations of Hyperspectral Remote Sensing Data
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Country | As in Soil (mg/kg) | As in Crops and Vegetables (mg/kg) | References | |
---|---|---|---|---|
Rice | Vegetables | |||
Bangladesh | NA | 0.358 | 0.034 | [46] |
West Bengal, India | 11.35 | 0.245 | <0.0004–0.693 | [47] |
Bangladesh | NA | NA | 0.306–0.489 | [48] |
Bangladesh | NA | NA | 0.011–0.103 | [49] |
Bangladesh | 7.31–27.28 | 0.04–0.27 | 0.2–3.99 | [50] |
West Bengal, India | 7.0–38.0 | 0.30 | NA | [51] |
China | 6.04 | 0.117 | 0.003–0.116 | [52] |
Bangladesh | 14.5 | 0.5–0.8 | NA | [35] |
Nepal | 6.1–16.7 | 0.180 | <0.010–0.550 | [53] |
West Bengal, India | 1.34–14.09 | 0.16–0.58 | NA | [54] |
West Bengal, India | 5.70–9.71 | 0.334–0.451 | 0.030–0.654 | [55] |
Bihar, India | 0.027 | 0.019 | 0.011–0.015 | [56] |
West Bengal, India | NA | 0.156–0.194 | 0.069–0.78 | [57] |
West Bengal, India | NA | 0.01–0.64 | 0.03–0.35 | [58] |
Spectral Pretreatment Methods | Descriptions | References |
---|---|---|
Baseline correction (BC) | The most widely used spectral pretreatment approach in NIR. This approach removes the significance of the lowest level in the spectral range from all of the variables in each sampling and has been applied to smoothing and resampling reflectance spectra. | [88,89] |
Standard normal variate (SNV) | A row-oriented spectral treatment that centres and scales each wavelength to reduce dispersion effects. In this method, by splitting the spectral standard deviation and removing the spectral mean, each spectrum is transformed. | [89,90] |
Multiplicative scatter correction (MSC) | A spectral data processing approach that corrects the multiplicative and cumulative dispersion effects. SNV and MSC are the same functional criteria, but the main difference is SNV is used for individual spectral reflectance, whereas MSC has been applied for reference spectral reflectance. | [89,91,92] |
First and second derivation (FD and SD) | These significantly reduce background influences and improve spectral data inflexion characteristics and spectral overlapping. | [92] |
Continuum removal (CR) | This creates additional spectral information by splitting a continuum’s envelope curve on unprocessed reflectance spectra to develop new reflectance spectra. | [93] |
Savitzky–Golay (SG) smoothing | Applied to the pretreatment of spectra, this is a common smoothness filtering method. It is a low-pass filter that smooths spectra by removing high-frequency noise while allowing low-frequency signals to pass through. Before evaluating the FD and SD, SG smoothing is performed. | [92,94] |
log(1/R) | This is one of the most common spectral pretreatment transformation methods, where reflectance (R) enacts linearisation between the spectra and heavy metal content in soil by highlighting the edges of absorption bands. | [79,92] |
Methods | Models’ Algorithm | Results | Location | Conclusion | References |
---|---|---|---|---|---|
Standard Vis-NIR reflectance spectroscopy and indirect—Fe, Fe2O3 approach | Multiple linear regression (MLR) and artificial neural network (ANN) | Using MLR-R2 = 0.837, using ANN-R2 = 0.858 | Near Seville, Spain | Results suggest that by utilising quick and cost-effective reflectance spectroscopy, it is possible to anticipate heavy metals in soils affected by mining residues. | [60] |
Standard Vis-NIR reflectance and variations in the spectral absorption features of lattice OH and oxygen on the mineral surface | Pearson correlation coefficient | R2 = 0.876 | Rodalquilar gold-mining area, south-eastern Spain | The results suggest that the variables generated from spectral absorption characteristics might be useful in assessing and monitoring As heavy metal concentration. | [61] |
Standard Vis-NIR reflectance spectroscopy and indirect Fe2O3 approach | Partial least square regression (PLSR) | Root mean square error of cross-validation (RMSEcv) = 1.23 and root mean square error of prediction (RMSEP) = 1.65. | Nanjing area, China | The results suggest that remote sensing data might be used to map As-contaminated areas at a low cost. It is strongly suggested that future research using remote sensing data and field measurements be carried out. | [84] |
Vis-NIR hyperspectral reflectance spectroscopy along with FD, SD, and MSC spectral resampling transformation based on the proposed model | Partial least squares regression (PLSR), support vector regression (SVR), and back propagation neural network (BPNN) | Using PLSR-R2 = 0.77, RPD = 1.89 using SVR-R2 = 0.72, RPD = 1.03 using BPNN-R2 = 0.86, RPD = 2.53 | Shangluo and Weinan, Shaanxi Province, China | BPNN has the best modelling accuracy, according to the data. In conclusion, estimating soil AS concentration using BPNN and hyperspectral data is possible. The nonlinear issue between soil As concentration and reflectance spectra may be efficiently solved using the BPNN model. | [100] |
Standard Vis-NIR reflectance spectroscopy and indirect SOC, Fe2O3 approach | Partial least square regression (PLSR) | R2 = 0.72, RMSEP = 0.86, RPD = 1.90 | Baguazhou Island, Jiangsu Province, China | Reflectance spectroscopy is a nonanalytical technology that may be used not only to anticipate spectral active components but also trace components that have no spectral features. | [104] |
Standard Vis-NIR reflectance spectral absorption feature parameters (SAFPs) and kriging interpolation technique are used as the gridding method for producing measured and predicted As contour maps. | Stepwise multiple linear regression (SMLR) and enter multiple linear regression (EMLR) | Using SMLR-R2 = 0.372, using EMLR-R2 = 0.598 | Suncheon, Republic of Korea | The geographic patterns of As concentration contour map based on EMLR-derived values were comparable to those of a map based on observed values, and the EMLR model showed a better qualitative prediction performance than SMLR. | [105] |
Diffuse Vis-NIR reflectance spectroscopy and indirect Al2O3, Fe2O3, TOC approach | Univariate regression | Correlation coefficient (R) = 0.552 | Changjiang River Delta, China | This research implies that analysing DRS in the Visible-NIR region could be utilised to derive binding forms and estimate As heavy metal concentrations in agricultural soils. | [106] |
Vis-NIR hyperspectral reflectance spectroscopy along with SG, FD, CR, and standard normal variate (SNV) spectral resampling transformation based on hydride generation atomic fluorescence spectrometry (HG-AFS) analysis | Multiple linear regression (MLR), partial least squares regression (PLSR), and adaptive neural fuzzy inference system (ANFIS) | Using MLR-R2 = 0.87, RMSE = 1.25, using PLSR-R2 = 0.88, RMSE = 1.22, using ANFIS-R2 = 0.94, RMSE = 0.88 | Liuxin mining area, northwest of Xuzhou, Jiangsu Province, China | In order to improve public health, the ANFIS model and reflectance spectroscopy can map the geographic pattern of soil As concentration. | [107] |
Multivariate analysis Using PLSR and SVMR, cross-validation of Vis-NIR diffuse reflectance spectroscopy. | Partial least square regression (PLSR) and support vector machine regression (SVMR) | Using PLSR-Cross Validation (RMSEPcv) = 2.98, maximal coefficient of determination (R2cv) = 0.61 and residual prediction deviation (RPD) = 1.81, using SVMR-RMSEPcv = 1.89, R2cv = 0.89 and RPD = 2.63. | Bílina and Tušimice mine areas, Czech Republic | The results show that Vis-NIR reflectance spectroscopy, in combination with the first derivative and SVMR, is a potential technique for soil As monitoring in high-risk areas. | [108] |
Lab-based and field- based reflectance spectroscopy based on iteratively retaining informative variables (IRIV) and iteratively retaining informative variables coupled with Spearman’s rank correlation analysis (IRIV-SCA) | Partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), extreme gradient boosting (XGBoost) regression, and random forest regression (RFR) | Best model results are showing here- IRIV approach, for lab spectra (Bayesian ridge regression (BRR)-R2 = 0.79, RMSE = 0.44, MAE (mean absolute error) = 0.36 for field spectra (random forest regression (RFR))-R2 = 0.49, RMSE = 0.67, MAE = 0.56. IRIV-SCA approach, For lab spectra (support vector machine regression (SVMR))-R2 = 0.97, RMSE = 0.22, MAE = 0.11, for field spectra (extreme gradient boosting (XGBoost))-R2 = 0.83, RMSE = 0.35, MAE = 0.29. | Daye city area of the Jianghan Plain region, the southeast of Hubei Province, China | The suggested approach considerably enhances the effectiveness and consistency of the inversion of soil As concentration, and it may be utilised for reliable data for decision making for the remediation and restoration of As pollution across a vast region. | [109] |
Vis-NIR reflectance spectroscopy based on the traditional modelling method and transfer component analysis (TCA) | Partial least squares regression (PLSR) | Using the traditional modelling method— In first pair of study areas—R2 = 0.02, RPD = 0.65, in the second pair of study areas—R2 = 0.01, RPD = 1.0.1, using transfer component analysis (TCA) method— in first pair of study areas—R2 = 0.68, RPD = 1.54, in the second pair of study areas—R2 = 0.64, RPD = 1.66 | First pair of study areas—Yuanping in Shanxi Province and Baoding in Hebei Province, China. Second pair of study areas—Chenzhou and Hengyang, located in Hunan Province, China | The findings show that developing future implementations of transferable spectroscopic diagnostic models for predicting soil As concentrations in vast areas at a cheaper cost is a possible path forward. | [110] |
Vis-NIR reflectance spectroscopy and CNN model with convolutional autoencoder as a deep learning method | Convolutional neural network (CNN), artificial neural network (ANN), and random forest regression (RFR) | Using CNN-R2 = 0.82, RMSE = 0.359, using ANN-R2 = 0.63, RMSE = 0.725, using RFR-R2 = 0.64, RMSE = 0.564 | Geum River watershed of South Korea, Republic of Korea | Deep learning algorithms can estimate As concentrations in soil, according to this study and the CNN-model-acquired robust characteristics from the convolutional autoencoder, which disentangled the key characteristics of several heavy metal elements and generated generally accurate estimations. | [111] |
Hyperspectral reflectance spectroscopy based on the stable competitive adaptive reweighting sampling algorithm (sCARS) and sCARS coupled with the successive projections algorithm (sCARS-SPA) approach | Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) | Best model results are shown here- sCARS algorithm: for Honghu area—SFLA-RBFNN model-R2 = 0.85, RMSE = 0.96, MAE = 0.78 for Daye area—SFLA-RBFNN model-R2 = 0.84, RMSE = 0.30, MAE = 0.25 sCARS-SPA algorithm, for Honghu area—SFLA-RBFNN model-R2 = 0.88, RMSE = 0.85, MAE = 0.72 for Daye area—SFLA-RBFNN model-R2 = 0.93, RMSE = 0.22, MAE = 0.17 | Honghu and Daye, Hubei Province, China | The findings of the study suggest that the sCARS-SPA-SFLA-RBFNN model may be used to analyse the As concentration of soil. The model not only minimises spectral redundancy and eliminates collinearity, but it also has a better prediction performance. It gives a mechanism for predicting soil As concentration on a broad scale with great accuracy. | [112] |
Vegetation Indices | r | RMSE (mg/kg) | References | |
---|---|---|---|---|
Two-band vegetation indices | {(R792 − R806)/(R792 + R806)} × 103 | 0.71 | 16.24 | [142] |
(R792/R806) × 103 | 0.70 | 16.28 | ||
(R876 − R887) × 103 | 0.63 | 18.49 | ||
Three-band vegetation indices | R674/(R352 × R526) | 0.33 | 22.23 | |
{R908/(R860 + R930)} × 102 | 0.72 | 16.18 | ||
{(R806 − R792)/(R806 + R770)} × 103 | 0.62 | 18.45 | ||
(R716 − R568)/(R552 − R568) | 0.75 | 15.63 | ||
(R730 − R812)/(R730 + R812 − 2R746) | 0.72 | 16.17 | ||
Photochemical reflectance index (PRI) | (R531 − R570)/(R531 + R570) | 0.67 | 17.41 | [143,148] |
(R762 − R732)/(R732 − R640) | 0.52 | 20.89 | [141] | |
(D752 − D711)/(D711 − D640) | 0.51 | 21.44 | ||
(D732 − D702)/(D732 + D702) | 0.57 | 19.85 | ||
D752/D702 | 0.51 | 21.37 | ||
Red-edge position (REP) | 700 + 40 [{(R670 + R780)/(2 − R700)}/(R740 − R700)] * | 0.62 | 18.65 | [144] |
Methods | Model Algorithm | Results | Location | Conclusion | References |
---|---|---|---|---|---|
Vis-NIR reflectance spectroscopy with various vegetative indices for evaluating leaf and canopy reflectance of stressed rice plants | SAIL (scattering by arbitrarily inclined Leaves) | NDVI-R2 = 0.69, RMSE = 1.99 OSAVI-R2 = 0.73, RMSE = 1.84 MCARI-R2 = 0.85, RMSE = 1.23 TCARI-R2 = 0.88, RMSE = 1.10 PDR-R2 = 0.79, RMSE = 1.45 TCARI/OSAVI-R2 = 0.89, RMSE = 1.11 | USDA Beltsville Agricultural Research Facility, Beltsville, MD, USA | The combined index, TCARI/OSAVI, and red-edge-based Vis-MCARI and TCARI showed higher sensitivity to As levels and better resistance to soil backgrounds and LAI. | [30] |
Lab-based and field-based Vis-NIR reflectance spectroscopy with the data preprocessing methods, Savitzky–Golay smoothing (SG), first derivative (FD), and mean Center (MC) for the spectral pretreatment and normalized difference spectral index (NDSI) approach | Partial least square regression (PLSR) | Using PLSR— for laboratory spectra (FD + SG + MC) − R = 0.64, RMSEP = 14.7 mg/kg, RPD = 1.31, for field spectra (FD + SG + MC) − R = 0.71, RMSEP = 13.7 mg/kg, RPD = 1.43, using NDSI— for field spectra—R = 0.68, RMSEP = 13.7 mg/kg, RPD = 1.36 | Zhongxiang, Hubei Province, China | These findings suggest that, by using the reflectance spectra of rice plants, it is possible to detect As contaminants in agricultural soils. The association between As levels in soils and chlorophyll a/b levels and cell structure in rice plant leaves or canopies might be the prediction mechanism. The wavelengths of the spectra at the canopy level around 768, 939, 953, 1132, and 1145 nm are discovered as critical wavelengths for forecasting the As content in agricultural soils. | [79] |
Hyperspectral reflectance spectroscopy using random forests | Random forests | R2 = 0.84, MSE = 3.97 | Suzhou, Jiangsu Province, China | Hyperspectral remote sensing and random forests are effective ways to quickly estimate As concentrations in rice plants. | [82] |
The spectral data pretreatment methods and indirect Fe approach. (First and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), reused for the spectral reflectance data pretreatments) | Partial least square regression (PLSR) | No spectral pretreatment–RRMSE–0.26, R2- 0.55, FD-RRMSE–0.24, R2- 0.61, SD-RRMSE–0.25, R2- 0.58, CR-RRMSE–0.24, R2- 0.62, BC-RRMSE–0.25, R2- 0.59, SNV-RRMSE–0.30, R2- 0.37, MSC-RRMSE–0.30, R2- 0.38. | Guiyang suburb on periphery of the Baoshan Mine, southeast Hunan Province, China | In order to build final models, wavebands around 460, 1400, 1900, and 2200 nm are essential spectral variables. | [136] |
Proposed three-band hyperspectral vegetation index, normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and red-edge position (REP) approach | Successive projections algorithm (SPA) | For three-band vegetation index—R = 0.75, RMSE = 15.63 mg/kg, For NDVI–R = 0.71, RMSE = 16.24 mg/kg, For PRI–R = 0.67, RMSE = 17.41 mg/kg, For REP–R = 0.62, RMSE = 18.65 mg/kg | Zhongxiang region of Chinaon, China | The findings suggest that the newly developed proposed three-band vegetation index (R716-R568)/(R552-R568) may be used to estimate the amount of As in the soil in the study region. For monitoring soil, As pollution, PRI and REP may be utilised as universal vegetation indices. | [142] |
Diffuse Vis-NIR and MIR reflectance spectroscopy and indirect Al2O3, Fe2O3, TOC approach | Partial least square regression (PLSR) | R2 = 0.455, RMSEC (root mean square error of calibration) = 1.86, RMSEP (root mean squared error of prediction) = 1.607, RPD = 1.137 | Jiangsu Province, the Changjiang River Delta, China | Multivariate regression and PLSR algorithms for Vis-NIR spectra have superior prediction skills than the related MIR spectra and show potential for facilitating harmful mineral assessment of soil samples. | [149] |
Instrument Names | Spectral Range | Spectral Resolution | Sampling Interval (Bandwidth) | Scanning Time | FOV Options | Weight | Measurement Type | Wavelength Accuracy | Study References | Website References |
---|---|---|---|---|---|---|---|---|---|---|
ASD FieldSpec 3 | 350–2500 nm | 3 nm @ 700 nm; 10 nm @ 1400/2100 nm | 1.4 nm @ 350–1050 nm; 2 nm @ 1000–2500 nm | 100 ms | 1.5 m fibre optic (25° field of view) | 5.6 kgs (12 lbs) | Ground truthing; Remote sensing | 0.5 nm | [82,109,142] | [158] |
Portable visible NIR spectro-radiometer PSR-3500® | 350–2500 nm | 3.5 nm @350–1000 nm; 9.5 nm @1500 nm; 6.5 nm @2100 nm | 1.5 nm @ 350–1000 nm; 3.8 nm @ 1500 nm; 2.5 nm@ 2100 nm | 100 ms | 4, 8, or 14° lenses, 25° fibre optic, diffuser, or integrating sphere | 3.3 kg (7.3 lbs) | Ground truthing; Remote sensing | 0.5 nm | [95] | [159] |
SVC HR-1024 field spectrometer | 350–2500 nm | ≤3.5 nm @ 700 nm; ≤ 9.5 nm @ 1500 nm; ≤6.5 nm @ 2100 nm | ≤1.5 nm @ 350–1000 nm; ≤3.8 nm @ 1000–1890 nm; ≤2.5 nm @ 1890–2500 nm | 1 millisecond | 4° standard, 8° and 14° optional fibre optic, 25° optional armoured fibre optic | 3.3 kgs (7.3 lbs) | Ground truthing; Remote sensing | [109] | [160] | |
ASD FieldSpec 4 Hi-Res NG | 350–2500 nm | 3 nm @ 700 nm; 6 nm @ 1400/2100 nm | 1.4 nm @ 350–1000 nm; 1.1 nm @ 1001–2500 nm | 100 ms | 1.5 m fibre optic (25° field of view). Optional narrower field of view, fibre optics available | 5.44 kgs (12 lbs) | Ground truthing; remote sensing | 0.5 nm | [100] | [161] |
ASD FieldSpec 4 Hi-Res: High Resolution | 350–2500 nm | 3 nm @ 700 nm; 8 nm @ 1400/2100 nm | 1.4 nm @ 350–1000 nm; 1.1 nm @ 1001–2500 nm | 100 ms | 1.5 m fiber optic (25° field of view). Optional narrower field of view fibre optics available | 5.44 kgs (12 lbs) | Ground truthing; remote sensing | 0.5 nm | [111] | [162] |
ASD LabSpec 4 Hi-Res | 350–2500 nm | 3 nm @ 700 nm; 6 nm @ 1400/2100 nm | N.A. | 100 ms | N/A | 5.44 kgs (12 lbs) | Molecular structure | [163] | ||
ASD FieldSpec Pro FR | 350–2500 nm | 3 nm @ 700 nm; 10 nm @ 1500 nm; 10 nm @ 2100 nm | 1.4 nm @ 350–1000 nm; 2 nm @ 1000–2500 nm | 100 ms | 1.4 m in length with 25° full-angle cone of acceptance field of view | 8 kgs (17.6 lbs) | Ground truthing; remote sensing | 1 nm | [141,164] | [165] |
Instrument Sensor | Hyperion | Prisma | TianGong-1 | HISUI | EnMAP-HYSI | SHALOM | HyspIRI | HypXIM | CHRIS | MODIS | HysIS |
---|---|---|---|---|---|---|---|---|---|---|---|
Satellite platform | EO-1 | Prisma | Shenzhou-8 | HISUI | EnMAP | IMS-II | HyspIRI | HypXIM | Proba-1 | Terra and Aqua | HysIS |
Spectral range (nm) | 357–2576 | 400–2500 | 400–2500 | 400–2500 | 420–2450 | 400–2500 | 380–2510 | 400–2500 | 415–1050 | 400–1400 | 400–2400 |
Spectral bands | 220 | 249 | 128 | 185 | 244 | 275 | 214 | 210 | 200 | 36 | 316 |
Spatial resolution (m) | 30 | 30 | 30 | 10 (VNIR)/20 (SWIR) | 30 | 10 | 30 | 8 | 18 | 250/500/1000 | 30 |
Spectral resolution (nm) | 10 | 10 | 10 (VNIR)/23 (SWIR) | 10 (VNIR)/12.5 (SWIR) | 6.5 (VNIR)/10 (SWIR) | 10 | 10 | 10 | 1.3–12 | ||
Temporal resolution (days) | 16–30 | 7–14 | 2–60 | 4–27 | 4 | 5–16 | 3–5 | 8 | 2–3 | ||
Country agency | USA (NASA) | Italy (ASI) | China (CNSA) | Japan (JAXA) | Germany (GFZ-DLR) | Italy–Israel (ASI-ISA) | USA (NASA) | France (CNES) | UK (ESA) | USA (NASA) | India (ISRO) |
Satellite mission | 2000–2017 | 2019-present | 2011–2018 | 2019-present | 2019-present | expected launch: 2022 | expected launch: 2023 | expected launch: 2021 | 2001-present | 1999-present | 2018-present |
Data access | USGS-U.S. Geological Survey, 2021 [167] | Prisma, 2019 [168] | MSADC, 2021 [169] | Data & Tools-EnMAP, 2012 [170] | Earth Online, 2021 [171] | MODIS Web, 2021 [172] |
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Saha, A.; Sen Gupta, B.; Patidar, S.; Martínez-Villegas, N. Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach. Soil Syst. 2022, 6, 30. https://doi.org/10.3390/soilsystems6010030
Saha A, Sen Gupta B, Patidar S, Martínez-Villegas N. Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach. Soil Systems. 2022; 6(1):30. https://doi.org/10.3390/soilsystems6010030
Chicago/Turabian StyleSaha, Arnab, Bhaskar Sen Gupta, Sandhya Patidar, and Nadia Martínez-Villegas. 2022. "Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach" Soil Systems 6, no. 1: 30. https://doi.org/10.3390/soilsystems6010030