A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.1.1. Study Area
2.1.2. Soil Observations
2.1.3. Environmental Variables
2.2. Modeling and Prediction
2.2.1. Statistical Analysis
2.2.2. Geostatistical Models
2.2.3. Machine Learning Methods
2.2.4. Two-Step Hybrid Approaches
2.2.5. Model Testing and Comparison
3. Results
3.1. Statistics Analysis
3.2. Models Training of GWR, SVR and ANN
3.3. OK, GWRK and ANNK Training and Six Models Validation
3.4. Spatial Prediction and Mapping of SOC Content
4. Discussion
4.1. Comparison between Intra-Classes of Three DSM Techniques
4.2. Comparison among Geostatistical, ML and Hybrid Techniques
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Sources | Variables | Description |
---|---|---|
Field Observation | BD | Bulk Density |
Weather stations | W | Mean relative humidity |
T | Average temperature | |
P | Average precipitation | |
Landsat 8 OLI | b2 | Blue, 0.450–0.515 μm |
b3 | Green, 0.525–0.600 μm | |
b4 | Red, 0.630–0.680 μm | |
b5 | NIR, 0.845–0.885 μm | |
b6 | SWIR1, 1.560–1.660 μm | |
b7 | SWIR2, 2.100–2.300 μm | |
EVI | 2.5 × (b5 − b4)/(b5 + 6 × b4 − 7.5 × b2 + 1) | |
NDVI | (b5 − b4)/(b5 + b4) | |
MSAVI | {2 × b5 + 1 – sqrt[(2 × b5 + 1) ^ 2 − 8 × (b5 − b4)]}/2 | |
ASTER GDEM | H | Altitude |
β | Slope | |
α | Aspect | |
sinα | Sine of aspect, the extent of the location toward the east | |
cosα | Cosine of aspect, the extent of the location toward the north | |
C | Curvature | |
Cv | Vertical curvature | |
Ch | Horizontal curvature | |
SOS | Slope of the slope, the curvature of the surface | |
SOA | Slope of aspect, the curvature of the contour line | |
RLD | Relief of land surface, Hmax − Hmin | |
M | Surface roughness, 1/cosβ | |
TWI | Topographic wetness index, Ln[Ac/tanβ], Ac is the catchment area directed to the vertical flow | |
SPI | Stream power index, Ln[Ac × tanβ × 100] |
Algorithms | Software | Necessary Parameters |
---|---|---|
Ordinary kriging (OK) | GS+ | Model type, nugget, sill, range |
Geographically weighted regression (GWR) | GWR | Kernel type, bandwidth selection criteria (AICc) |
Artificial neural network (ANN) | MATLAB | Learning algorithm, hidden layers, learning rate, training time |
Support vector machine for regression (SVR) | C (the regularization parameter), kernel (Gaussian radial basis kernel) and its σ (the bandwidth parameter) | |
Geographically weighted regression kriging (GWRK) | GWR, GS+ | Kernel type, bandwidth selection criteria (AICc), model type, nugget, sill, range |
Artificial neural network kriging (ANNK) | MATLAB, GS+ | Learning algorithm, hidden layers, learning rate, training time, model type, nugget, sill, range |
Statistics | Mean | SD 1 | CV 2 | Skewness | Kurtosis | Minimum | Maximum |
---|---|---|---|---|---|---|---|
SOC (g·kg−1) | 18.41 | 9.61 | 0.52 | 1.62 | 3.61 | 2.54 | 68.94 |
BD (g·cm−3) | 1.35 | 0.19 | 0.14 | −0.21 | −0.18 | 0.81 | 1.89 |
Parameters (g·kg−1) | Model | Range (km) | Nugget | Nugget/Sill | R2 | RSS |
---|---|---|---|---|---|---|
Training samples | Exponential | 0.09 | 32.2 | 0.30 | 0.92 | 8.60 |
RGWR | Spherical | 0.04 | 18.6 | 0.26 | 0.85 | 4.03 |
RANN | Exponential | 0.05 | 23.4 | 0.23 | 0.97 | 0.99 |
Evaluation Index | ANNK | ANN | GWRK | GWR | SVR | OK |
---|---|---|---|---|---|---|
Mean error (g·kg−1) | −0.38 | −0.59 | −0.61 | 1.63 | −0.53 | −0.71 |
RMSE(g·kg−1) | 8.89 | 9.47 | 9.54 | 9.92 | 9.13 | 9.81 |
R2 | 0.60 | 0.48 | 0.47 | 0.30 | 0.51 | 0.32 |
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Chen, L.; Ren, C.; Li, L.; Wang, Y.; Zhang, B.; Wang, Z.; Li, L. A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content. ISPRS Int. J. Geo-Inf. 2019, 8, 174. https://doi.org/10.3390/ijgi8040174
Chen L, Ren C, Li L, Wang Y, Zhang B, Wang Z, Li L. A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content. ISPRS International Journal of Geo-Information. 2019; 8(4):174. https://doi.org/10.3390/ijgi8040174
Chicago/Turabian StyleChen, Lin, Chunying Ren, Lin Li, Yeqiao Wang, Bai Zhang, Zongming Wang, and Linfeng Li. 2019. "A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content" ISPRS International Journal of Geo-Information 8, no. 4: 174. https://doi.org/10.3390/ijgi8040174