Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Soil Sampling and Laboratory Analysis
2.3. Environmental Covariates
2.3.1. Soil and Parent Covariates
2.3.2. Climate Covariates
2.3.3. Organism Covariates
2.3.4. Relief Covariates
2.3.5. Position Covariates
2.3.6. Covariate Harmonization
2.4. Modeling Methodology
2.4.1. Feature Selection
- (1)
- Extend the variable database by adding >5 shadow attributes for each variable.
- (2)
- Shuffle all attributes to remove the correlations.
- (3)
- Perform RF on the extended database and calculate the Z scores of each attribute. The Z score for each variable were calculated based on the variable importance of each classification and regression tree (CART) with the following equations:
- (4)
- Find the maximum Z score (MZSA) and select attributes with a Z score better than MZSA.
- (5)
- Run the two-side test in undermined importance attributes with MZSA.
- (6)
- Remove unimportant attributes where the Z score is significantly lower than MZSA.
- (7)
- Retain important attributes where the Z score is significantly higher than MZSA.
- (8)
- Define important original variables as the selected database.
- (9)
- Repeat these procedures until attachment of assigned criteria.
2.4.2. Model Fitness
2.4.3. Model Performance
2.4.4. Model Environment
3. Results
3.1. Descriptive Statistics for Soil Samples
3.2. Model Accuracy and Uncertainty
3.3. Importance of Environmental Covariates
3.4. Spatial Distribution Pattern of SOC
4. Discussion
4.1. Feature Selection
4.2. Model Accuracy
4.3. Uncertainty Assessments
4.4. Spatial Distribution Pattern of SOC and Controlling Factors
4.5. Perspectives and Limitation
5. Conclusions
- (1)
- Boruta was a compelling feature selection method to eliminate redundant variables and develop the optimal QRF model.
- (2)
- SOC overall increased from the southern to the northern areas, with an average of 17.34 g kg−1 in the plough layer and 13.92 g kg−1 in the compacted layer. At the vertical scale, SOC decreased, with depths getting deeper. The average decreasing SOC is 3.41 g kg−1, and the northern area decreased more than the southern area.
- (3)
- Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period).
- (4)
- The average uncertainty values were 1.04 in the plough layer and 1.07 in the compacted layer. The high uncertainty appeared in the areas with relatively scattered fields, high altitudes, and complex landforms.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Abbreviation | Scale | Covariate a) | Type b) | Period | Reference |
---|---|---|---|---|---|---|
Soil erosion | SE | 1000 m | S&P | Q | 2002–2016 | [27] |
Silt content | Silt | 250 m | S&P | Q | 1960–2020 | [10,28] |
Sand content | Sand | 250 m | S&P | Q | 1960–2020 | [10,28] |
Brightness index | BI | 30 m | S&P | Q | 2015–2017 | [25] |
Bare soil index | BSI | 30 m | S&P | Q | 2015–2017 | [25] |
Carbonate index | CarI | 30 m | S&P | Q | 2015–2017 | [25] |
Gypsum index | GI | 30 m | S&P | Q | 2015–2017 | [25] |
Isothermality | BIO03 | 1000 m | C | Q | 1970–2000 | [29] |
Temperature seasonality | BIO04 | 1000 m | C | Q | 1970–2000 | [29] |
Temperature annual range | BIO07 | 1000 m | C | Q | 1970–2000 | [29] |
Mean temperature of wettest quarter | BIO08 | 1000 m | C | Q | 1970–2000 | [29] |
Mean temperature of driest quarter | BIO09 | 1000 m | C | Q | 1970–2000 | [29] |
Mean temperature of warmest quarter | BIO10 | 1000 m | C | Q | 1970–2000 | [29] |
Mean temperature of coldest quarter | BIO11 | 1000 m | C | Q | 1970–2000 | [29] |
Precipitation of wettest month | BIO13 | 1000 m | C | Q | 1970–2000 | [29] |
Daytime land surface temperature | LSTD | 1000 m | C | Q | 2002–2017 | [30] |
Nighttime land surface temperature | LSTN | 1000 m | C | Q | 2002–2017 | [30] |
Solar radiation | Sol | 1000 m | C | Q | 1970–2000 | [29] |
Average temperature | Tavg | 1000 m | C | Q | 1970–2000 | [29] |
Maximum temperature | Tmax | 1000 m | C | Q | 1970–2000 | [29] |
Minimum temperature | Tmin | 1000 m | C | Q | 1970–2000 | [29] |
Vapor pressure | VP | 1000 m | C | Q | 1970–2000 | [29] |
The average of CANI in the crop period | CroCANIa | 30 m | O | Q | 2015–2017 | [25] |
The average of NDRI in the non-crop period | NCroNDRIa | 30 m | O | Q | 2015–2017 | [25] |
The average of GPP in the non-crop period | NCroGPPa | 500 m | O | Q | 2015–2017 | [31] |
The average of NPP in the non-crop period | NCroNPPa | 500 m | O | Q | 2015–2017 | [31] |
The average of FPAR in the non-crop period | NCroFPARa | 500 m | O | Q | 2015–2017 | [32] |
The average of LAI in the non-crop period | NCroLAIa | 500 m | O | Q | 2015–2017 | [32] |
The variance of GPP in the non-crop period | NCroGPPv | 500 m | O | Q | 2015–2017 | [31] |
The variance of NPP in the non-crop period | NCroNPPv | 500 m | O | Q | 2015–2017 | [31] |
Elevation | ELE | 90 m | R | Q | 2000 | [33] |
Channel network base level | CNBL | 90 m | R | Q | 2000 | [33] |
Valley depth | VD | 90 m | R | Q | 2000 | [33] |
Terrain wetness index | TWI | 90 m | R | Q | 2000 | [33] |
Oblique geographic coordinate at 30° | OGC30 | 30 m | N | Q | / | [34] |
Oblique geographic coordinate at 45° | OGC45 | 30 m | N | Q | / | [34] |
Oblique geographic coordinate at 60° | OGC60 | 30 m | N | Q | / | [34] |
Oblique geographic coordinate at 105° | OGC105 | 30 m | N | Q | / | [34] |
Oblique geographic coordinate at 120° | OGC120 | 30 m | N | Q | / | [34] |
Oblique geographic coordinate at 165° | OGC165 | 30 m | N | Q | / | [34] |
Layer | Min | 1st Qu | Median | Average | 3rd Qu | Max | SD | %CV | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|---|---|---|
SOC PL | 2.78 | 9.95 | 14.93 | 16.33 | 20.76 | 48.65 | 8.80 | 53.88 | 1.12 | 4.56 |
SOC CL | 0.96 | 7.75 | 12.09 | 13.21 | 17.41 | 36.40 | 6.99 | 52.91 | 0.72 | 3.19 |
Depth | OOB | CV | Uncertainty | |||
---|---|---|---|---|---|---|
R2 | RMSE (g kg−1) | R2 | RMSE (g kg−1) | U | PICP | |
PL | 0.61 | 5.45 | 0.68 | 5.10 | 1.05 | 0.88 |
CL | 0.54 | 4.75 | 0.58 | 4.64 | 1.09 | 0.86 |
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Zhang, X.; Xue, J.; Chen, S.; Wang, N.; Xie, T.; Xiao, Y.; Chen, X.; Shi, Z.; Huang, Y.; Zhuo, Z. Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China. Remote Sens. 2023, 15, 5033. https://doi.org/10.3390/rs15205033
Zhang X, Xue J, Chen S, Wang N, Xie T, Xiao Y, Chen X, Shi Z, Huang Y, Zhuo Z. Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China. Remote Sensing. 2023; 15(20):5033. https://doi.org/10.3390/rs15205033
Chicago/Turabian StyleZhang, Xianglin, Jie Xue, Songchao Chen, Nan Wang, Tieli Xie, Yi Xiao, Xueyao Chen, Zhou Shi, Yuanfang Huang, and Zhiqing Zhuo. 2023. "Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China" Remote Sensing 15, no. 20: 5033. https://doi.org/10.3390/rs15205033
APA StyleZhang, X., Xue, J., Chen, S., Wang, N., Xie, T., Xiao, Y., Chen, X., Shi, Z., Huang, Y., & Zhuo, Z. (2023). Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China. Remote Sensing, 15(20), 5033. https://doi.org/10.3390/rs15205033