Comparative Analysis of GF-1 and HJ-1 Data to Derive the Optimal Scale for Monitoring Heavy Metal Stress in Rice
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Remote Sensing Data
2.3. Measured Data
2.4. Meteorological Data
3. Methods
3.1. Computing LAI from GF-1 and HJ-1 Data
3.2. Extraction of WRT and WSO
3.3. Determination of the Heavy Metal Stress Condition Indicators
4. Results
4.1. WRT Analysis
4.2. WSO Analysis
4.3. SORMR Analysis
5. Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Study Area | Type | Cd | Hg | Pb | As | Pollution Level |
---|---|---|---|---|---|---|
A | Soil | 0.84 | 0.25 | 78.32 | 10.22 | Low |
Rice Tissue | 0.82 | 0.04 | 10.60 | 5.39 | ||
Pollution index | 0.59 | 1.25 | 0.95 | 0.53 | ||
B | Soil | 3.28 | 0.51 | 120.75 | 18.15 | High |
Rice Tissue | 5.90 | 0.06 | 36.73 | 7.04 | ||
Pollution index | 2.29 | 2.55 | 1.46 | 0.95 | ||
Background value | 1.43 | 0.2 | 82.78 | 19.11 |
Sensor | Spatial Resolution (m) | Revisitation Period (day) | Breadth (km) | Band 1 (nm) | Band 2 (nm) | Band 3 (nm) | Band 4 (nm) |
---|---|---|---|---|---|---|---|
GF-1 | 2 | 4 | 60 | 0.45~0.90 | |||
8 | 4 | 60 | 0.45~0.52 | 0.52~0.59 | 0.63~0.69 | 0.77~0.89 | |
16 | 2 | 800 | 0.45~0.52 | 0.52~0.59 | 0.63~0.69 | 0.77~0.89 | |
HJ-1 | 30 | 4 | 360 (1CCD) 700 (2CCD) | 0.43~0.52 | 0.52~0.60 | 0.63~0.69 | 0.76~0.90 |
Statistical Analysis | A_30m | B_30m | A_16m | B_16m | A_8m | B_8m | A_2m | B_2m |
---|---|---|---|---|---|---|---|---|
Mean | 0.819 | 0.781 | 0.8447 | 0.7484 | 0.775 | 0.691 | 0.7518 | 0.704 |
Minimum | 0.775 | 0.6 | 0.705 | 0.6 | 0.664 | 0.6 | 0.6 | 0.6 |
Maximum | 0.9171 | 0.95 | 0.95 | 0.8161 | 0.9462 | 0.783 | 0.8851 | 0.775 |
Statistical Analysis | A_30m | B_30m | A_16m | B_16m | A_8m | B_8m | A_2m | B_2m |
---|---|---|---|---|---|---|---|---|
Minimum | 0.00030 | 0.00029 | 0.00031 | 0.00028 | 0.00029 | 0.00027 | 0.00029 | 0.00027 |
Mean | 0.73376 | 0.75426 | 1.72085 | 0.7826 | 0.76162 | 0.81404 | 0.77959 | 0.81175 |
Maximum | 4.55944 | 4.6869 | 4.47896 | 4.86346 | 4.73293 | 5.05791 | 4.84456 | 5.04432 |
Standard Deviation | 1.23371 | 1.26862 | 1.21164 | 1.31682 | 1.28115 | 1.37043 | 1.31164 | 1.3665 |
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Wang, D.; Liu, X. Comparative Analysis of GF-1 and HJ-1 Data to Derive the Optimal Scale for Monitoring Heavy Metal Stress in Rice. Int. J. Environ. Res. Public Health 2018, 15, 461. https://doi.org/10.3390/ijerph15030461
Wang D, Liu X. Comparative Analysis of GF-1 and HJ-1 Data to Derive the Optimal Scale for Monitoring Heavy Metal Stress in Rice. International Journal of Environmental Research and Public Health. 2018; 15(3):461. https://doi.org/10.3390/ijerph15030461
Chicago/Turabian StyleWang, Dongmin, and Xiangnan Liu. 2018. "Comparative Analysis of GF-1 and HJ-1 Data to Derive the Optimal Scale for Monitoring Heavy Metal Stress in Rice" International Journal of Environmental Research and Public Health 15, no. 3: 461. https://doi.org/10.3390/ijerph15030461