Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Data Collection
3. Methods
3.1. Creation of Daily Continuous NDVI Time Series
3.2. Derivation of Rice Phenological Characteristics
3.3. Acquisition of WRT Based on Assimilation Algorithm
4. Results
4.1. Agreement Assessment of NDVI Time Series
4.2. Extraction of Rice Phenology and WRT
4.3. Sensitivity Analysis of Different Phenological Indicators during Heavy Metal Stress
4.4. Differentiation of Heavy Metal Stress Levels Based on Feature Space
5. Discussion and Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Study Area | Geographic Location | Content of Cd in Soil (mg/kg) | Quality Standard | Pollution Level |
---|---|---|---|---|
A | 113°06′E 27°47′ N | 1.38 | 0.3–1.0 | Mild |
B | 113°10′E 27°40′ N | 2.31 | Moderate | |
C | 113°02′E 27°50′ N | 3.28 | Severe |
Characteristic | CCD | ETM+ | OLI |
---|---|---|---|
Spatial Resolution (m) | 30 | 30 | 30 |
Swath Width (km) | 360 | 185 | 185 |
Revisit Cycle (days) | 2 | 16 | 16 |
Scan Technology | push-broom scan | push-broom scan | push-broom scan |
Sensor Height (km) | 649 | 705 | 705 |
Spectral Resolution (μm) | band1: 0.43–0.52 | band1: 0.45–0.52 | band1: 0.45–0.51 |
band2: 0.52–0.60 | band2: 0.52–0.60 | band2: 0.53–0.59 | |
band3: 0.63–0.69 | band3: 0.63–0.69 | band3: 0.64–0.67 | |
band4: 0.76–0.90 | band4: 0.76–0.90 | band4: 0.85–0.88 |
Variable | Definition | Reported Unit |
---|---|---|
Start of Season | Starting point of the growing season | Day of year |
End of Season | Ending point of the growing season | Day of year |
Length of Season | Interval elapsed from the start to the end of the season | Number of days |
Base Level | Average of the left and right minimum values | NDVI unit |
Largest Value | Highest value of a year | NDVI unit |
Seasonal Amplitude | Difference between the maximum value and the base level | NDVI unit |
Seasonal Integral | Integral of the function describing the season from season start to season end | (NDVI unit)·(time unit) |
Rate of Increase or Decrease | Ratio of the difference between the left or right 20% and 80% levels and the corresponding time difference | (NDVI unit)/(time unit) |
Original | Double Logistic Fitting | Savitzky-Golay Filter | Threshold Wavelet | Forced Wavelet | |
---|---|---|---|---|---|
average value | 0.5820 | 0.5946 | 0.5925 | 0.5921 | 0.5919 |
standard deviation | 0.1351 | 0.1178 | 0.1171 | 0.1205 | 0.1204 |
root mean square error | / | 0.0624 | 0.0577 | 0.0620 | 0.0649 |
correlation coefficient | / | 0.8752 | 0.8957 | 0.8775 | 0.8636 |
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Liu, T.; Liu, X.; Liu, M.; Wu, L. Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology. Sensors 2018, 18, 860. https://doi.org/10.3390/s18030860
Liu T, Liu X, Liu M, Wu L. Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology. Sensors. 2018; 18(3):860. https://doi.org/10.3390/s18030860
Chicago/Turabian StyleLiu, Tianjiao, Xiangnan Liu, Meiling Liu, and Ling Wu. 2018. "Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology" Sensors 18, no. 3: 860. https://doi.org/10.3390/s18030860
APA StyleLiu, T., Liu, X., Liu, M., & Wu, L. (2018). Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology. Sensors, 18(3), 860. https://doi.org/10.3390/s18030860