Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Airborne Hyperspectral Data
2.3. Satellite Hyperspectral Data
2.4. Field Data
2.5. Spectral Indices
2.6. SOC Modelling Using Airborne Hyperspectral Data
2.7. SOC Modelling Using Satellite Hyperspectral Data
3. Results
3.1. Descriptive Statistics of SOC
3.2. Correlation between SOC and Reflectances/Spectral Indices
3.3. SOC Predictive Models
3.4. Relevant Wavelengths for SOC Prediction
3.5. SOC Mapping
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Ground Observation Dataset Used for AHS (n = 39) | Dataset Predicted by AHS Used for Hyperion (n = 200) | |||
---|---|---|---|---|
TOC (%) | OC (%) | TOC (%) | OC (%) | |
Minimum | 4.3 | 2.7 | 5.3 | 5.6 |
Maximum | 51.7 | 37.9 | 51.6 | 37.4 |
Mean | 33.3 | 22.5 | 34.4 | 22.9 |
Standard deviation | 14.6 | 10.3 | 10.6 | 5.9 |
Sensor | Soil Property | Regression Technique | Prediction Equation | n | R2cv | RMSEcv (%) | RPD |
---|---|---|---|---|---|---|---|
AHS | TOC (%) | SLR-1 channel | TOC = 66 − 1008 × ρ679 nm | 39 | 0.35 | 11.69 | 1.25 |
SLR-1 index | TOC = −101 + 186 × SI1001–679 nm | 39 | 0.52 | 10.02 | 1.46 | ||
SMLR-3 channels | 39 | 0.51 | 10.13 | 1.44 | |||
SMLR-8 indices | See Table 3 | 39 | 0.62 | 9.05 | 1.62 | ||
PLSR-3 factors | 39 | 0.49 | 10.39 | 1.41 | |||
OC (%) | SLR-1 channel | OC = 43 – 640 × ρ679 nm | 39 | 0.27 | 8.70 | 1.18 | |
SLR-1 index | OC = −72 + 131 × SI1001–679 nm | 39 | 0.52 | 7.02 | 1.46 | ||
SMLR-3 channels | 39 | 0.52 | 7.06 | 1.46 | |||
SMLR-2 indices | See Table 3 | 39 | 0.60 | 6.44 | 1.60 | ||
PLSR-3 factors | 39 | 0.48 | 7.34 | 1.40 | |||
Hyperion | TOC (%) | SLR-1 channel | TOC = 58 − 356 × ρ610 nm | 200 | 0.34 | 8.55 | 1.24 |
SLR-1 index | TOC = −46 + 2276 × SI1023–973 nm | 200 | 0.23 | 9.26 | 1.14 | ||
SMLR-15 channels | 200 | 0.44 | 7.91 | 1.33 | |||
SMLR-18 indices | See Table 3 | 200 | 0.49 | 7.58 | 1.39 | ||
PLSR-4 factors | 200 | 0.32 | 8.68 | 1.22 | |||
OC (%) | SLR-1 channel | OC = 33 − 177 × ρ681 nm | 200 | 0.40 | 4.59 | 1.29 | |
SLR-1 index | OC = −20 + 1218 × SI1033–973 nm | 200 | 0.43 | 4.48 | 1.32 | ||
SMLR-10 channels | 200 | 0.54 | 4.02 | 1.48 | |||
SMLR-20 indices | See Table 3 | 200 | 0.61 | 3.74 | 1.58 | ||
PLSR-4 factors | 200 | 0.44 | 4.45 | 1.33 |
AHS | Hyperion | ||||||
---|---|---|---|---|---|---|---|
TOC (%) | OC (%) | TOC (%) | OC (%) | ||||
b0−n | SIλ2−λ1,1−n | b0−n | SIλ2−λ1,1−n | b0−n | SIλ2−λ1,1−n | b0−n | SIλ2-λ1,1−n |
−192 | −116 | −29 | −172 | ||||
1679 | SI1001–471 nm | 249 | SI1001–679 nm | −1693 | SI1023–844 nm | −2128 | SI1033–427 nm |
−3941 | SI1001–500 nm | −82 | SI1001–2150 nm | −24,127 | SI1023–1064 nm | −4832 | SI1033–457 nm |
3325 | SI1001–530 nm | 19,806 | SI1023–1094 nm | 5509 | SI1033–468 nm | ||
−2432 | SI1001–591 nm | −24,888 | SI1023–1104 nm | 6087 | SI1033–539 nm | ||
1991 | SI1001–620 nm | 24,104 | SI1023–1114 nm | −8698 | SI1033–569 nm | ||
−494 | SI1001–709 nm | −31,871 | SI1023–1185 nm | 4859 | SI1033–590 nm | ||
276 | SI1001–2102 nm | 26,902 | SI1023–1245 nm | −1368 | SI1033–925 nm | ||
−198 | SI1001–2150 nm | −10,102 | SI1023–1316 nm | −17,970 | SI1033–983 nm | ||
−12,028 | SI1023–1498 nm | 12772 | SI1033–993 nm | ||||
−14,115 | SI1023–1558 nm | 14470 | SI1033–1044 nm | ||||
18,239 | SI1023–1588 nm | 8856 | SI1033–1266 nm | ||||
13,523 | SI1023–1790 nm | −11,975 | SI1033–1276 nm | ||||
−9758 | SI1023–1982 nm | −6386 | SI1033–1639 nm | ||||
7336 | SI1023–1992 nm | 9555 | SI1033–1679 nm | ||||
8275 | SI1023–2052 nm | 4928 | SI1033–2083 nm | ||||
9878 | SI1023–2103 nm | −4845 | SI1033–2153 nm | ||||
−11,850 | SI1023–2163 nm | −6318 | SI1033–2163 nm | ||||
−3555 | SI1023–2355 nm | −5679 | SI1033–2194 nm | ||||
4452 | SI1033–2214 nm | ||||||
5356 | SI1033–2244 nm |
Sensor a | Spectral Range (nm) b, Number of Bands, and Spatial Resolution (m) | Study Area (Country) c | Soil Property d | Modeling Technique e | ncal|nval f | RMSE g | R2 g | RPD g | Authors |
---|---|---|---|---|---|---|---|---|---|
AHS * | 400–1600 (VNS), 21, 2.6 | Agric. fields (Belgium) | C org. | PLSR (2) | 110 xval | 1.7 g/kg | 0.54 | 1.47 | Stevens et al. [30] |
AHS * | 430–1600 (VNS), 21, 2.6 | Agric. fields (Luxembourg) | C org. | PLSR | 267|134 | 4.3 g/kg | 0.72 | 1.89 | Stevens et al. [8] |
AHS * | 430–1600 (VNS), 21, 2.6 | Agric. fields (Luxembourg) | C org. | PLSR | 400 xval | 3.9 g/kg | 0.79 | 2.33 | Stevens et al. [27] |
AHS * | 430–2540 (VNS), 63, 2.6 | Agric. fields (Luxembourg) | C org. | PLSR | 91 xval | 3.7 g/kg | 0.96 | 3.13 | Denis et al. [29] |
AHS * | 450–2120 (VNS), 30, 2.6 | Partially vegetated agric. fields (Belgium) | C org. | PLSR (8) | 52|16 | 1.7 g/kg | 0.56 | 1.50 | Bartholomeus et al. [37] |
AHS * | 442–1019 (VN), 20, 2.6 | Agric. fields (Luxembourg) | C org. | PLSR | 46|31 | 2.2 g/kg | 0.74 | 1.9 | Steinberg et al. [28] |
AHS * | 430–2335 (VNS), 38, 5 | Partially vegetated burned areas (Spain) | C total C oxid. | PLSR (7) PLSR (7) | 89|10 89|10 | 7.8% 5.1% | 0.73 0.72 | 1.92 1.89 | Fernández et al. [38] |
AHS * | 430–2335 (VNS), 38, 5 | Partially vegetated burned areas (Spain) | C total C oxid. | SMLR (8) SMLR (2) | 39 xval 39 xval | 9.1% 6.4% | 0.62 0.60 | 1.62 1.60 | Peón et al. [this paper] |
HyMap * | 420–2480 (VNS), 127, 6 | Agric. fields (Germany) | C org. C org. | PLSR (7) MLR (4) | 60 xval 60 xval | 0.29% 0.22% | 0.90 0.86 | Selige et al. [22] | |
HyMap * | 450–2500 (VNS), 128, 4 | Agric. fields (Germany) | C org. | PLSR | 9 xval | 1.6 g/kg | 0.74 | Patzold et al. [26] | |
HyMap * | 420–2480 (VNS), 110, 6 | Bare soils (Spain) | C total | PLSR | 61|61 | 0.13% | 0.77 | 1.92 | Schwanghart and Jarmer [23] |
HyMap * | 450–2480 (VNS), 128, 4 | Agric. fields (Germany) | C org. | PLSR (2) | 38|29 | 2.1 g/kg | 0.71 | 1.80 | Gerighausen et al. [24] |
HyMap * | 400–2500 (VNS), 124, 5 | Agric. fields (France) | C org. | PLSR | 95 xval | 2.6 g/kg | 0.02 | 0.99 | Gomez et al. [25] |
HyMap * | 539–2477 (VNS), 126, 8 | Agric. fields (Germany) | C org. | PLSR (7) | 204 xval | 1.1 g/kg | 0.83 | 2.45 | Hbirkou et al. [21] |
DAIS * | 400–2500 (VNS), 69, 8 | Agric. fields (Israel) | OM | MLR (4) | 62|5 | 0.83 | Ben-Dor et al. [34] | ||
HSTIR * | 400–2450 (VNS), 178, 2.5 | Agric. fields (USA) | C org. | PLSR (8) | 269 xval | 0.18% | 0.64 | 1.39 | Hively et al. [35] |
P-AISA * | 400–2500 (VNS), 282, 1 | Weathered soils (Brazil) | OM | PLSR (8) | 60 xval | 2.8 g/kg | 0.60 | 1.60 | Franceschini et al. [36] |
AVNIR * | 429–1010 (VN), 60, 1.2 | Agric. fields (California) | C total OM | MLR (4) MLR (4) | 321|- 321|- | 0.08% 0.08% | 0.27 0.49 | DeTar et al. [33] | |
CASI * | 409–947 (VN), 71, 2 | Agric. fields (Canada) | OM | PCA-SMLR | 47 xval | 0.5% | 0.75 | 1.57 | Uno et al. [32] |
CASI * | 405–950 (VN), 96, 6 | Agric. fields (Belgium) | C org. | PLSR (2) | 170|57 | 5.1 g/kg | 0.85 | 1.86 | Stevens et al. [31] |
HYP ** | 427–2355 (VNS), 152, 30 | Agric. fields and pastures (Australia) | C org. | PLSR (3) | 72 xval | 0.73% | 0.51 | 1.43 | Gomez et al. [39] |
HYP ** | 468–1770 (VNS), 98, 30 | Forests, pastures and agric. fields (USA) | C org. | SMLR-ANN | 227|76 | 11.3 t/ha | 0.68 | Jaber et al. [43] | |
HYP ** | 400–2500 (VNS), 158, 30 | Agric. fields (China) | C org. | PLSR (3) | 47 xval | 1.6 g/kg | 0.63 | 1.65 | Lu et al. [40] |
HYP ** | 400–2500 (VNS), 150, 30 | Agric. fields (USA) | C total OM | PLSR (3) PLSR (4) | 20|8 20|8 | 0.33% 0.66% | 0.48 0.74 | 1.48 1.91 | Zhang et al. [41] |
HYP ** | 436–2345 (VNS), 171, 30 | Maize crops (Italy) | OM | OLS MNF | 72 xval | 0.15% | 2.93 | Castaldi et al. [42] | |
HYP ** | 427–2335 (VNS), 155, 30 | Partially vegetated burned areas (Spain) | C total C oxid. | SMLR (18) SMLR (20) | 200 xval 200 xval | 7.6% 3.7% | 0.49 0.61 | 1.39 1.58 | Peón et al. [this paper] |
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Peón, J.; Recondo, C.; Fernández, S.; F. Calleja, J.; De Miguel, E.; Carretero, L. Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery. Remote Sens. 2017, 9, 1211. https://doi.org/10.3390/rs9121211
Peón J, Recondo C, Fernández S, F. Calleja J, De Miguel E, Carretero L. Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery. Remote Sensing. 2017; 9(12):1211. https://doi.org/10.3390/rs9121211
Chicago/Turabian StylePeón, Juanjo, Carmen Recondo, Susana Fernández, Javier F. Calleja, Eduardo De Miguel, and Laura Carretero. 2017. "Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery" Remote Sensing 9, no. 12: 1211. https://doi.org/10.3390/rs9121211