Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area and Sampling Locations
2.2. Soil Sampling and Laboratory Analysis
2.3. Description and Preprocessing of Landsat-8 Images, and Soil Spectral Data Transformations
2.4. Data Sources, Software, and Extraction of Environmental Covariates
2.5. Statistical Analysis, Spatial Modeling, and Validation
3. Results
3.1. Spatial Prediction of SOC and Model Validation
3.2. Importance of Environmental Variables
3.3. Influence of Topographic and Vegetation Indices
3.4. SOC Distribution across Landform, Land Use, and Lithology
4. Discussion
4.1. Spatial Variability of SOC
4.2. Importance of Environmental Variables
4.3. Influence of Topographic and Vegetation Indices
4.4. SOC Distribution across the Landform, Land Use, and Lithology
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable Explanation | Formula (Value) | Reference |
---|---|---|
Normalized difference built-up index | [20] | |
Topographic position index (TPI) | [21] | |
Greenness index | [22] | |
Curvature | [23] | |
Vegetation temperature condition index | [24] | |
Proportion of Vegetation | [25] | |
Adjusted transformed soil-adjusted Vegetation Index | [25] | |
Emissivity | [25] | |
Soil-adjusted vegetation index | [26] | |
Aspect | [27] | |
Normalized difference vegetation index | [28] | |
Land surface temperature | [29] | |
Profile curvature | [30] | |
Brightness temperature | [25] | |
Slope | [31] | |
Enhanced vegetation index | [28] | |
Brightness index | [22] | |
Wetness index | [32] | |
Mass balance index | [33] | |
Topographic wetness index | [34] | |
Length slope (LS) factor | [35] | |
Thiam’s transformed vegetation index | [36] | |
Corrected transformed vegetation index | [37] | |
Normalized ratio vegetation index | [38] | |
Normalized ratio vegetation index | [38] | |
Ratio vegetation index | [38] | |
Difference vegetation index | [39] | |
Transformed soil-adjusted vegetation index (Baret and Guyot, 1991) | 2) | [40] |
Transformed soil-adjusted vegetation index (Baret et al. 1989) | [26] | |
Perpendicular vegetation index (Perry and Lautenschlager, 1984) | [41] | |
Perpendicular vegetation index (Qi, et al-, 1994) | [41] | |
Perpendicular vegetation index (Richardson and Wiegand, 1977) | [42] | |
Distance from industries, landfill | DST_Inds = proximity analysis | [43] |
Distance from primary roads | DST_PR = proximity analysis | [43] |
Distance from water bodies, canal, dam, ditch, drain, riverbank, river, stream, waterfall, weir | DST_RCD = proximity analysis | [43] |
Aerosol band 1 | Coastal aerosol (0.443 µm) | [44] |
Blue band | Blue (450–510 nm) | [44] |
Green band | Green (530–590 nm) | [44] |
Red band | Red (640–670 nm) | [44] |
NIR band | Near infrared (NIR) (850–880 nm) | [44] |
SWIR-1 band | SWIR-1 (1570–1650 nm) | [44] |
SWIR-2 band | SWIR-2 (2110–2290 nm) | [44] |
Land-use types | Supervised the maximum-likelihood method | [45] |
Landform types | TPI-based landform classification | [21] |
Soil pH | pH meter | |
Lithology | FAO soil database | |
Spectral soil data | Wavelength (from 350 to 2500 nm with 1 nm interval) |
Main Land Cover | Area (ha) | Area Proportion (%) | Com. Error | Om. Error | Acc. |
---|---|---|---|---|---|
Agriculture | 28,198.71 | 21.80 | 3.04 | 0.58 | |
Forest | 44,562.51 | 34.44 | 2.13 | 0.74 | 95.52 |
Water bodies | 18,580.59 | 14.36 | 0.15 | 0.38 | |
Urban land | 38,034.36 | 29.40 | 1.2 | 3.08 |
Datasets | Indicators | Min | 25% | Mean | 75% | Max. | SD |
---|---|---|---|---|---|---|---|
Training set | ME | 0.05 | 0.04 | 0.04 | 0.03 | 0.06 | 0.003 |
RMSE | 1.35 | 1.36 | 1.37 | 1.37 | 1.38 | 0.026 | |
R2 | 0.68 | 0.76 | 0.033 | ||||
Test set | ME | 0.3 | 0.4 | 0.4 | 0.43 | 0.36 | 0.001 |
RMSE | 0.94 | 0.95 | 0.96 | 0.96 | 0.97 | 0.024 | |
R2 | 0.88 | 0.91 | 0.92 | 0.021 |
Variables (Unit) | Min. | Max. | Mean | zSD | CV | yr | L.B. | U.B. | Coeff. | p−Values | R2 |
---|---|---|---|---|---|---|---|---|---|---|---|
SOC (mg·g−1) | 0.70 | 45.80 | 11.70 | 8.92 | 0.81 | 1 | - | - | - | - | - |
Slope (°) | 0.00 | 103.51 | 37.55 | 23.55 | 0.58 | −0.16 | −0.33 | 0.02 | 0.02 | 0.09 | 0.03 |
Curvature | −5.80 | 5.94 | 0.22 | 2.11 | 0.36 | −0.19* | −0.36 | −0.01 | 0.04 | 0.04 | 0.04 |
Aspect | −1.00 | 357.92 | 160.70 | 117.89 | 0.67 | 0.16 | −0.02 | 0.33 | 0.03 | 0.08 | 0.03 |
TPI | −2.29 | 2.10 | −0.01 | 0.93 | 0.41 | 0.10 | −0.08 | 0.27 | 0.01 | 0.29 | 0.01 |
MBI | −0.84 | 0.87 | 0.06 | 0.54 | 0.61 | −0.18* | −0.35 | 0.00 | 0.03 | 0.05 | 0.03 |
TWI | −1.95 | 11.55 | 6.79 | 3.66 | 0.43 | 0.17 | −0.01 | 0.34 | 0.03 | 0.06 | 0.03 |
LS Factor | 0.00 | 21.791 | 10.633 | 7.473 | 0.68 | −0.06 | −0.23 | 0.12 | 0.68 | 0.96 | 0.01 |
NDVI | −0.47 | 0.89 | 0.64 | 0.24 | 0.19 | 0.20* | −0.36 | −0.02 | 0.04 | 0.03 | 0.04 |
ASTAVI | −0.70 | 1.35 | 0.96 | 0.35 | 0.18 | 0.23* | −0.39 | −0.05 | 0.05 | 0.01 | 0.05 |
WI | 0.01 | 0.21 | 0.11 | 0.04 | 0.39 | −0.14 | −0.31 | 0.04 | 0.02 | 0.13 | 0.02 |
GI | −0.09 | 0.27 | 0.12 | 0.08 | 0.35 | 0.28* | −0.44 | −0.11 | 0.08 | 0.00 | 0.08 |
BI | 0.02 | 0.39 | 0.20 | 0.07 | 0.40 | −0.11 | −0.28 | 0.07 | 0.01 | 0.23 | 0.01 |
EVI | −0.07 | 0.55 | 0.29 | 0.15 | 0.38 | 0.28* | −0.43 | −0.10 | 0.08 | 0.00 | 0.08 |
SAVI | −0.48 | 1.13 | 0.78 | 0.29 | 0.20 | 0.24* | −0.40 | −0.07 | 0.06 | 0.01 | 0.06 |
CTVI | 0.42 | 1.12 | 1.00 | 0.11 | 0.16 | 0.22* | −0.39 | −0.05 | 0.05 | 0.01 | 0.05 |
TVI | 0.09 | 1.37 | 1.21 | 0.17 | 0.28 | 0.22* | −0.38 | −0.04 | 0.05 | 0.01 | 0.05 |
NRVI | −0.32 | 0.76 | 0.52 | 0.19 | 0.20 | 0.24* | −0.40 | −0.07 | 0.06 | 0.01 | 0.06 |
RVI | 0.51 | 7.18 | 3.77 | 1.61 | 0.46 | 0.27* | −0.43 | −0.10 | 0.07 | 0.00 | 0.07 |
TSAVI_91 | 0.01 | 0.73 | 0.53 | 0.15 | 0.25 | 0.26* | −0.42 | −0.08 | 0.07 | 0.00 | 0.07 |
PV | 0.00 | 0.59 | 0.05 | 0.08 | 1.68 | 0.14 | −0.04 | 0.31 | 0.02 | 0.13 | 0.02 |
LST (°C) | 6.25 | 15.15 | 10.65 | 2.11 | 0.47 | 0.09 | −0.09 | 0.27 | 0.01 | 0.32 | 0.01 |
VTCI | 0.04 | 0.99 | 0.51 | 0.23 | 0.48 | 0.09 | −0.09 | 0.27 | 0.01 | 0.32 | 0.01 |
NDBI | −0.65 | 0.07 | −0.30 | 0.16 | 0.44 | 0.26* | 0.08 | 0.41 | 0.07 | 0.00 | 0.07 |
DST_INDST (km) | 1.05 | 62.59 | 16.35 | 12.15 | −0.12 | −0.29 | 0.06 | 0.01 | 0.20 | 0.01 | |
DST_PR (km) | 0.07 | 43.64 | 9.77 | 9.01 | 0.88 | −0.05 | −0.23 | 0.12 | 0.00 | 0.55 | 0.00 |
DST_RCD (km) | 0.04 | 25.05 | 3.38 | 4.92 | 1.46 | −0.13 | −0.31 | 0.05 | 0.02 | 0.14 | 0.02 |
Band2 | 824.94 | 1552.34 | 1057.66 | 172.56 | 0.70 | 0.16 | −0.02 | 0.33 | 0.03 | 0.08 | 0.03 |
Band3 | 496.94 | 1503.39 | 826.82 | 213.15 | 0.61 | 0.10 | −0.08 | 0.28 | 0.01 | 0.25 | 0.01 |
Band4 | 308.63 | 1526.16 | 647.02 | 270.15 | 0.77 | 0.13 | −0.04 | 0.31 | 0.02 | 0.14 | 0.02 |
Band5 | 404.82 | 3859.93 | 2202.38 | 780.76 | 0.42 | −0.23* | −0.39 | −0.05 | 0.05 | 0.01 | 0.05 |
Band6 | 123.38 | 2683.87 | 1230.17 | 543.24 | 0.50 | −0.02 | −0.19 | 0.16 | 0.00 | 0.87 | 0.00 |
Band7 | 54.04 | 1817.46 | 671.75 | 397.67 | 0.64 | 0.09 | −0.09 | 0.26 | 0.01 | 0.35 | 0.01 |
pH | 0.00 | 9.10 | 4.82 | 1.81 | 0.38 | 0.12 | −0.06 | 0.29 | 0.01 | 0.19 | 0.01 |
Landform | Min | Max | Mean | SD | CV (%) |
---|---|---|---|---|---|
TM | 3.70 | 34.06 | 12.64 | 8.52 | 67.43 |
LP | 1.65 | 29.47 | 11.94 | 6.78 | 56.79 |
SH | 0.70 | 45.80 | 11.88 | 9.28 | 78.09 |
SM | 2.29 | 26.24 | 10.90 | 13.31 | 122.08 |
TH | 1.70 | 1.70 | 1.70 | - | - |
WR | 3.83 | 11.12 | 7.48 | 5.15 | 68.96 |
Land Use | Min | Max | Mean | SD | CV (%) |
---|---|---|---|---|---|
Agriculture | 0.70 | 39.57 | 10.43 | 8.37 | 80 |
Forestland | 1.65 | 45.80 | 13.60 | 8.49 | 62 |
Urban area | 1.70 | 26.5 | 9.74 | 9.83 | 101 |
Water bodies | 0.6 | 8.89 | 4.55 | 5.86 | 129 |
Lithology | Min | Max | Mean | SD | CV (%) |
---|---|---|---|---|---|
Pyroclastic, ignimbrite | 1.34 | 45.80 | 12.04 | 9.73 | 80.80 |
Sandstone, greywacke, arkose | 1.70 | 14.27 | 5.54 | 4.53 | 81.80 |
Fluvial | 2.63 | 29.47 | 13.57 | 9.97 | 73.51 |
Gneiss, migmatite | 0.70 | 31.47 | 11.91 | 8.61 | 72.30 |
Granite | 4.39 | 15.24 | 11.61 | 4.98 | 42.87 |
Shale | 3.70 | 34.06 | 14.52 | 9.58 | 65.98 |
Siltstone, mudstone, claystone | 1.65 | 12.03 | 7.93 | 4.45 | 56.14 |
Inland water or lakes deposits | 3.83 | 11.12 | 7.48 | 5.15 | 68.96 |
Marine unconsolidated rock | 8.73 | 13.26 | 11.00 | 3.20 | 29.10 |
Weathered residuum, bauxite, laterite | 2.12 | 35.67 | 14.69 | 11.06 | 75.30 |
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Sodango, T.H.; Sha, J.; Li, X.; Noszczyk, T.; Shang, J.; Aneseyee, A.B.; Bao, Z. Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China. Remote Sens. 2021, 13, 1682. https://doi.org/10.3390/rs13091682
Sodango TH, Sha J, Li X, Noszczyk T, Shang J, Aneseyee AB, Bao Z. Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China. Remote Sensing. 2021; 13(9):1682. https://doi.org/10.3390/rs13091682
Chicago/Turabian StyleSodango, Terefe Hanchiso, Jinming Sha, Xiaomei Li, Tomasz Noszczyk, Jiali Shang, Abreham Berta Aneseyee, and Zhongcong Bao. 2021. "Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China" Remote Sensing 13, no. 9: 1682. https://doi.org/10.3390/rs13091682