Estimation of Soil Heavy Metal Content Using Hyperspectral Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Soil Samples
2.3. Spectral Measurements and Preprocessing
2.4. Methods
2.4.1. Selecting the Spectral Characteristic Indices of Dry Soil
2.4.2. Selecting the Optimal Spectral Variables from the Spectral Indexes of Dry Soil
2.4.3. Model Development and Validation for Estimating Dry Soil Heavy Metal Contents
2.4.4. The Spectral Relationship Model Between Dry Soil and Moist Soil
3. Results
3.1. Selecting Spectral Indexes of Dry Soil
3.2. Selection of Optimal Relevant Spectral Variables
3.3. Estimation Models of Soil Heavy Metal Contents and Accuracy Assessment
3.4. The Spectral Ratio Model of Dry Soil to Moisture Soil
3.5. Regional-Scale Soil Heavy Metal Contents Retrieved from HJ-1A hyperspectral Data
4. Discussions
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Metal | Minimum (mg/kg) | Maximum (mg/kg) | Mean (mg/kg) | Standard Deviation (mg/kg) | CV (%) | Background Value [35] (mg/kg) | Health Standard (mg/kg) |
---|---|---|---|---|---|---|---|
Cd | 0.003 | 1.937 | 0.109 | 0.103 | 94.50 | 0.034 | 0.3 |
Hg | 0.018 | 1.326 | 0.128 | 0.111 | 86.72 | 0.078 | 0.3 |
As | 1.230 | 34.797 | 7.912 | 6.951 | 87.85 | 10.50 | 30 |
Soil Heavy Metals | The First Derivative Spectral Variables |
---|---|
As | FD 470 nm FD 987 nm FD 1056 nm |
Cd | FD 1059 nm FD 2178 nm FD 2379 nm |
Hg | FD 453 nm FD 907 nm |
Heavy Metal | As | Cd | Hg | |||
---|---|---|---|---|---|---|
Field Data | Estimates | Field Data | Estimates | Field Data | Estimates | |
Mean | 7.12 | 7.11 | 1.33 | 1.35 | 0.15 | 0.15 |
Stdev | 4.26 | 3.30 | 0.74 | 0.70 | 0.13 | 0.11 |
RRMSE (%) | 45.92 | 11.51 | 40.29 |
Model Coefficients | The Central Wavelength of Selected Spectral Bands (nm) from HJ-1A Image | ||||||||
---|---|---|---|---|---|---|---|---|---|
470 | 472 | 477 | 479 | 481 | 484 | 486 | 491 | 493 | |
a | 35.79 | 36.44 | 36.47 | 37.48 | 36.87 | 39.76 | 40.18 | 40.95 | 38.29 |
b | 1.11 | 1.10 | 1.12 | 1.09 | 1.12 | 1.07 | 1.06 | 1.03 | 1.11 |
Plot # | As Content (mg/kg) | Cd Content (mg/kg) | Hg Content (mg/kg) | ||||||
---|---|---|---|---|---|---|---|---|---|
Observation | Estimate | Error | Observation | Estimate | Error | Observation | Estimate | Error | |
1 | 13.72 | 16.76 | 3.04 | 0.19 | 0.18 | −0.01 | 0.15 | 0.12 | −0.03 |
2 | 5.48 | 7.52 | 2.04 | 0.11 | 0.13 | 0.02 | 0.17 | 0.11 | −0.06 |
3 | 5.58 | 5.15 | −0.43 | 0.09 | 0.08 | −0.01 | 0.09 | 0.04 | −0.05 |
4 | 4.15 | 6.17 | 2.02 | 0.18 | 0.14 | −0.04 | 0.18 | 0.14 | −0.04 |
5 | 24.56 | 17.89 | −6.67 | 0.22 | 0.19 | −0.03 | 0.44 | 0.27 | −0.17 |
6 | 5.14 | 5.2 | 0.06 | 0.1 | 0.16 | 0.06 | 0.11 | 0.14 | 0.03 |
7 | 5.33 | 6.06 | 0.73 | 0.11 | 0.09 | −0.02 | 0.11 | 0.18 | 0.07 |
8 | 4.09 | 6.56 | 2.47 | 0.17 | 0.15 | −0.02 | 0.18 | 0.19 | 0.01 |
9 | 5.7 | 9.85 | 4.15 | 0.13 | 0.16 | 0.03 | 0.12 | 0.1 | −0.02 |
10 | 4.03 | 7.06 | 3.03 | 0.12 | 0.13 | 0.01 | 0.14 | 0.21 | 0.07 |
11 | 5.65 | 8.85 | 3.2 | 0.21 | 0.18 | −0.03 | 0.2 | 0.18 | −0.02 |
12 | 11.93 | 17.83 | 5.9 | 0.21 | 0.17 | −0.04 | 0.18 | 0.27 | 0.09 |
13 | 6.4 | 7.98 | 1.58 | 0.07 | 0.08 | 0.01 | 0.24 | 0.24 | 0 |
14 | 4.03 | 6.44 | 2.41 | 0.14 | 0.16 | 0.02 | 0.1 | 0.15 | 0.05 |
15 | 10.38 | 14.94 | 4.56 | 0.3 | 0.3 | 0 | 0.12 | 0.13 | 0.01 |
16 | 4.3 | 6.26 | 1.96 | 0.09 | 0.1 | 0.01 | 0.21 | 0.24 | 0.03 |
17 | 5.61 | 7.96 | 2.35 | 0.15 | 0.13 | −0.02 | 0.2 | 0.18 | −0.02 |
18 | 8.06 | 7.56 | −0.5 | 0.22 | 0.19 | −0.03 | 0.23 | 0.22 | −0.01 |
19 | 5.22 | 8.08 | 2.86 | 0.17 | 0.18 | 0.01 | 0.17 | 0.16 | −0.01 |
20 | 4.3 | 7.42 | 3.12 | 0.19 | 0.17 | −0.02 | 0.19 | 0.14 | −0.05 |
21 | 8.28 | 7.39 | −0.89 | 0.13 | 0.16 | 0.03 | 0.13 | 0.15 | 0.02 |
22 | 18.92 | 18.77 | −0.15 | 0.13 | 0.18 | 0.05 | 0.13 | 0.2 | 0.07 |
23 | 3.98 | 5.46 | 1.48 | 0.23 | 0.21 | −0.02 | 0.09 | 0.13 | 0.04 |
24 | 3.22 | 7.23 | 4.01 | 0.12 | 0.16 | 0.04 | 0.07 | 0.17 | 0.1 |
25 | 8.11 | 5.12 | −2.99 | 0.12 | 0.12 | 0 | 0.18 | 0.14 | −0.04 |
26 | 15.22 | 10.35 | −4.87 | 0.19 | 0.2 | 0.01 | 0.12 | 0.17 | 0.05 |
27 | 18.98 | 24.12 | 5.14 | 0.23 | 0.2 | −0.03 | 0.2 | 0.24 | 0.04 |
28 | 25.59 | 27.87 | 2.28 | 0.2 | 0.21 | 0.01 | 0.18 | 0.16 | −0.02 |
29 | 57.22 | 32.51 | −24.71 | 0.1 | 0.14 | 0.04 | 0.09 | 0.19 | 0.1 |
30 | 6.66 | 7.49 | 0.83 | 0.25 | 0.21 | −0.04 | 0.17 | 0.19 | 0.02 |
31 | 3.51 | 7.6 | 4.09 | 0.04 | 0.05 | 0.01 | 0.13 | 0.14 | 0.01 |
32 | 21.06 | 26.83 | 5.77 | 0.24 | 0.28 | 0.04 | 0.39 | 0.23 | −0.16 |
33 | 7.56 | 7.35 | −0.21 | 0.14 | 0.14 | 0 | 0.14 | 0.12 | −0.02 |
Average (mg/kg) | 10.36 | 11.20 | 0.844 | 0.16 | 0.16 | 0.001 | 0.17 | 0.17 | 0.003 |
STDEV (mg/kg) | 10.52 | 7.43 | 0.06 | 0.05 | 0.08 | 0.05 | |||
CV (%) | 101.54 | 37.22 | 45.90 | ||||||
RMSE (mg/kg) | 5.34 | 0.03 | 0.06 | ||||||
RRMSE (%) | 51.55 | 17.1 | 36.34 |
Spectral variables | FD367 | FD400 | FD647 | FD675 | FD693 | FD796 | FD879 | FD902 |
VIF | 2.99 | 6.26 | 10.82 | 26.57 | 19.87 | 16.90 | 51.63 | 31.87 |
Spectral variables | FD907 | FD1008 | FD1059 | FD1943 | FD2178 | FD2379 | ||
VIF | 9.96 | 19.87 | 16.90 | 51.63 | 31.87 | 9.96 |
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Liu, Z.; Lu, Y.; Peng, Y.; Zhao, L.; Wang, G.; Hu, Y. Estimation of Soil Heavy Metal Content Using Hyperspectral Data. Remote Sens. 2019, 11, 1464. https://doi.org/10.3390/rs11121464
Liu Z, Lu Y, Peng Y, Zhao L, Wang G, Hu Y. Estimation of Soil Heavy Metal Content Using Hyperspectral Data. Remote Sensing. 2019; 11(12):1464. https://doi.org/10.3390/rs11121464
Chicago/Turabian StyleLiu, Zhenhua, Ying Lu, Yiping Peng, Li Zhao, Guangxing Wang, and Yueming Hu. 2019. "Estimation of Soil Heavy Metal Content Using Hyperspectral Data" Remote Sensing 11, no. 12: 1464. https://doi.org/10.3390/rs11121464
APA StyleLiu, Z., Lu, Y., Peng, Y., Zhao, L., Wang, G., & Hu, Y. (2019). Estimation of Soil Heavy Metal Content Using Hyperspectral Data. Remote Sensing, 11(12), 1464. https://doi.org/10.3390/rs11121464