Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Soil Sampling and Analysis
2.3. Satellite Remote Sensing Image Collection and Processing
2.4. Spectral Indices Construction
2.5. Optimal Variables Selection
2.6. Modelling Strategy
2.6.1. Partial Least Squares
2.6.2. Geographically Weighted Regression
2.6.3. Random Forest
2.7. Statistical Analysis and Model Evaluation
3. Results
3.1. SOM Content of the Soil Sampling Points
3.2. Characterization of the Soil Spectral Reflectance from Satellite Images
3.3. Selection of the Optimal Prediction Variables
3.3.1. Optimal Prediction Variables for the Single-Temporal Images
3.3.2. Optimal Prediction Variables for the Double-Temporal Images
3.4. Analysis of the SOM Estimation Model for the Single-Temporal Images
3.5. Analysis of the SOM Estimation Model for the Double-Temporal Images
3.6. SOM Mapping in the Plough Layer for the Cultivated Land throughout the Study Area
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Conflicts of Interest
References
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Soil Spectral Indices Computed Using Single-Temporal Images | Equation | Soil Spectral Indices Computed Using Multi-Temporal Images | Equation |
---|---|---|---|
Dataset | Number | Min | Max | Median | 1st Qu a | 3rd Qu b | Mean | SDc | CV (%) d | SKe | KUf |
---|---|---|---|---|---|---|---|---|---|---|---|
Whole dataset | 134 | 13.79 | 40.64 | 25.55 | 21.56 | 30.06 | 25.86 | 5.84 | 22.58 | 0.18 | −0.41 |
Calibration dataset | 94 | 13.79 | 40.64 | 25.14 | 20.55 | 28.56 | 25.30 | 5.89 | 23.29 | 0.43 | −0.12 |
Validation dataset | 40 | 14.20 | 36.63 | 27.24 | 24.28 | 31.17 | 27.18 | 5.56 | 20.47 | −0.45 | −0.27 |
Year | Single Band | Spectral Indices | |
---|---|---|---|
2016 | Variables | B8_1020, B11_1116, B8a_1116, B7_1020, B4_1020, B12_1020, B11_1020 | D1020-1116_48a, R1020-1116_411, ND1020-1116_411 |
Importance | 410.53 328.09, 306.77, 272.36, 256.80, 173.78, 156.68 | 80.30, 61.39, 57.65 | |
2018 | Variables | B8_1017, B6_1017, B7_1017, B4_1116, B8a_1017, B11_1017, B5_1017 | D1116-1017_48, D1017-1116_74, D1116-1017_411 |
Importance | 444.74, 397.53, 394.66, 337.85, 277.09, 211.02, 195.84 | 63.82, 49.69, 48.22 | |
2020 | Variables | B11_1016, B8_1115, B7_1016, B4_1016, B5_1016, B8a_1016, B4_1115 | D1115-1016_84, D1115-1016_47, D1016-1115_48 |
Importance | 361.97, 334.69, 333.94, 296.43, 278.03, 226.30, 210.70 | 65.64, 43.06, 41.89 |
Modelling Strategies | Year | Full Spectrum | Optimum Bands and Spectral Indices | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Calibration | Validation | Calibration | Validation | ||||||||
R2cal | RMSEcal | R2val | RMSEval | RPIQval | R2cal | RMSEcal | R2val | RMSEval | RPIQval | ||
PLS | 2016 | 0.43 | 2.84 | 0.40 | 2.91 | 2.13 | 0.54 | 2.53 | 0.47 | 2.89 | 2.38 |
2018 | 0.49 | 2.55 | 0.45 | 2.74 | 2.26 | 0.61 | 2.26 | 0.53 | 2.43 | 2.83 | |
2020 | 0.47 | 2.66 | 0.44 | 2.83 | 2.19 | 0.57 | 2.46 | 0.51 | 2.51 | 2.74 | |
GWR | 2016 | 0.51 | 2.67 | 0.48 | 2.57 | 2.42 | 0.60 | 2.27 | 0.56 | 2.42 | 2.85 |
2018 | 0.54 | 2.54 | 0.50 | 2.79 | 2.47 | 0.63 | 2.16 | 0.59 | 2.37 | 2.91 | |
2020 | 0.52 | 2.67 | 0.50 | 2.76 | 2.49 | 0.61 | 2.27 | 0.58 | 2.35 | 2.93 | |
RF | 2016 | 0.59 | 2.31 | 0.53 | 2.39 | 2.88 | 0.65 | 2.08 | 0.63 | 2.15 | 3.20 |
2018 | 0.61 | 2.1 | 0.55 | 2.28 | 3.02 | 0.68 | 1.89 | 0.67 | 2.05 | 3.36 | |
2020 | 0.59 | 2.2 | 0.54 | 2.16 | 3.19 | 0.66 | 1.98 | 0.64 | 2.12 | 3.25 |
Contents/(g kg−1) | Area/km2 | Percentage/% |
---|---|---|
16.17~22.47 | 96.28 | 10.14 |
22.47~25.36 | 230.46 | 24.27 |
25.36~28.12 | 388.53 | 40.91 |
28.12~32.50 | 147.31 | 15.51 |
32.50~38.98 | 87.08 | 9.17 |
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Wang, L.; Zhou, Y. Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land. Agriculture 2023, 13, 8. https://doi.org/10.3390/agriculture13010008
Wang L, Zhou Y. Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land. Agriculture. 2023; 13(1):8. https://doi.org/10.3390/agriculture13010008
Chicago/Turabian StyleWang, Li, and Yong Zhou. 2023. "Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land" Agriculture 13, no. 1: 8. https://doi.org/10.3390/agriculture13010008