A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sites and Datasets
2.1.1. Site Descriptions
2.1.2. LAI Datasets
2.2. LAI Curve Fitted Model
- (1)
- Model 1: Asymmetric Gaussian functionThe left half of the asymmetric Gaussian function [33] after the equivalent-transformation is described below:where m1(t)=((a1−t) / a2 )a3 and t < a1, y is the LAI-fitted value and t is the time in days. The parameters p and q determine the amplitude and the base level, respectively; a1 determines the position of the maximum or minimum with respect to the independent time variable t. a2 and a3 determine the width and flatness (kurtosis), respectively, of the left half of the function (in most cases, a3 = 2).
- (2)
- Model 2: Logistic functionThe logistic function [13] uses the following function form:where m2(t) = b1t + b2, y is the LAI-fitted value and t is the time in days. The parameters p and q determine the amplitude and the base level, respectively; b1 and b2 are both fitted parameters.
- (3)
- Model 3: S-curve functionFrom Equations (1) and (2), the uniform function is as follows:where m(t) = at2 + bt + c; y is the LAI-fitted value; t is the time in days; q is the minimum LAI value; p + q is the maximum LAI value; and a, b and c are parameters of the curve and control the position of the inflection points as well as the slope of the curve.
2.3. Vegetation Phenology Extraction Methodology
2.3.1. Vegetation Germination and Dormancy
2.3.2. Vegetation Green-Up, Maturation, Senescence and Defoliation
2.4. Extraction Process for Vegetation Phenology
2.5. Validation Process for Vegetation Phenology
2.6. Quantitative Evaluation of Vegetation Phenology Retrieval Methodology
3. Results
4. Discussion
4.1. Complexity of Validation of Retrieval Results with Inversion Algorithms
4.2. Shortage of Validation Data
4.3. Uncertainties of the Retrieval Results
5. Conclusions
- (1)
- The SC algorithm has better performance for deriving the vegetation phenophases, especially for the germination and dormancy. The averaged absolute biases of germination are 2, 28 and 39 days for the SC, LC and AG algorithms, respectively; while the averaged absolute biases of dormancy are 5, 13 and 16 days for the SC, LC and AG algorithms, respectively. The retrieval results of the SC algorithm begin later than the other two algorithms during germination and green-up but start earlier during defoliation and dormancy. The discrepancy among the retrieval results is reflected in the fitted LAI curve. The SC function fits the LAI curve better than the other two functions. The averaged RMSE values of fitting LAI are 0.116, 0.149 and 0.244 m2·m−2 for the SC, LC and AG functions, respectively. The averaged indices of agreement (IA) of fitting LAI are 0.983, 0.973, 0.892 for the SC, LC and AG functions, respectively. The phenology recognition rate (PRR) of the SC algorithm is obviously higher than the other two algorithms. The averaged PRRs are 1.00, 0.62, 0.91 for the SC, LC and AG algorithms, respectively.
- (2)
- Regardless of the LAI data used, the retrieved results using theSC algorithm are reliable and better than the other two algorithms. The averaged absolute biases of green-up using the SC algorithm are 9 and 12 days for the AVHRR LAI and improved MODIS LAI datasets, respectively. The green-up dates derived by the LC algorithm are earlier than the SC algorithm, but later than the AG algorithms. The SC and LC algorithms have the highest and lowest accuracy in the PRR (0.99 and 0.75), respectively. The SC algorithm has the advantage of deriving the vegetation phenology across time and space.
- (3)
- Besides the algorithms, the temporal–spatial resolution and quality of the LAI data used also determine the accuracy of the retrieved vegetation phenophases: low resolution/quality, higher uncertainty. For all three inversion algorithms, the retrieved results based on the AVHRR LAI data are later than those based on the improved MODIS LAI. The bias statistics analysis, i.e., the averaged absolute biases of green-up are 18 and 26 days for the AVHRR LAI and improved MODIS LAI data, respectively, shows that the retrieved results based on the AVHRR LAI data are more reasonable than those based on the improved MODIS LAI data. In addition, the mixing pixels of the LAI datasets and changes in the agricultural cultivation patterns influence the uncertainty as well.
Acknowledgments
Conflicts of Interest
- Author ContributionsAll authors contributed extensively to the work presented in this paper. Baozhang Chen and Huifang Zhang proposed the research idea. Mingliang Che and Baozhang Chen designed the algorithm, interpreted the results and wrote the paper. Huifang Zhang and Shifeng Fang analyzed the results and edited the paper. Guang Xu prepared the leaf area index (LAI) datasets. Xiaofeng Lin and Yuchen Wang prepared the phenology observed data.
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ID | Site Name | Lat. (°N) | Lon. (°E) | Elev. (m) | Site Year | a Percip. (mm·yr−1) | b Temp. (°C) |
---|---|---|---|---|---|---|---|
1 | Heze | 35.22 | 115.47 | 54 | 2000–2009 | 698.4 | 14.7 |
2 | Liaocheng | 36.45 | 115.98 | 36 | 1982–1988, 2000–2009 | 578.4 | 13.1 |
3 | Jining | 35.40 | 116.58 | 42 | 2000–2009 | 708.5 | 13.7 |
4 | Taian | 36.18 | 117.08 | 171 | 1982–1986, 2000–2009 | 675.3 | 13.0 |
5 | Huimin | 37.48 | 117.50 | 13 | 2000–2009 | 589.4 | 12.2 |
6 | Zibo | 36.80 | 118.05 | 40 | 2000–2009 | 622.1 | 13.3 |
7 | Linyi | 35.10 | 118.33 | 68 | 2000–2009 | 793.9 | 13.3 |
8 | Weifang | 36.70 | 119.15 | 28 | 1985–1988, 2000–2009 | 615.3 | 12.6 |
9 | Laiyang | 36.97 | 120.70 | 41 | 2000–2009 | 795.4 | 11.2 |
10 | Wendeng | 37.18 | 122.05 | 50 | 2000–2009 | 762.2 | 11.5 |
Site Name | Algorithm | Germination | Greenup | Maturation | Senescence | Defoliation | Dormancy |
---|---|---|---|---|---|---|---|
Liaocheng | SC | −3 | 14 | 39 | −32 | −15 | −1 |
LC | −31 | −10 | 47 | −37 | −5 | 15 | |
AG | −44 | −47 | 21 | −24 | 18 | 17 | |
Taian | SC | 1 | 18 | 44 | −18 | 9 | 11 |
LC | −37 | 9 | 49 | −22 | 14 | 15 | |
AG | −43 | −27 | 21 | −15 | 42 | 18 | |
Weifang | SC | 2 | 11 | 27 | −28 | −31 | −3 |
LC | −15 | 5 | 32 | −27 | −22 | 8 | |
AG | −32 | −24 | 7 | −26 | 2 | 11 |
Site Name | Algorithm | PRR | RMSE | IA | |||
---|---|---|---|---|---|---|---|
Spring | Autumn | Spring | Autumn | Spring | Autumn | ||
Liaocheng | SC | 1.00 | 1.00 | 0.059 | 0.235 | 0.973 | 0.982 |
LC | 0.43 | 1.00 | 0.072 | 0.315 | 0.962 | 0.971 | |
AG | 0.86 | 1.00 | 0.111 | 0.443 | 0.848 | 0.910 | |
Taian | SC | 1.00 | 1.00 | 0.084 | 0.158 | 0.989 | 0.979 |
LC | 0.40 | 0.40 | 0.094 | 0.180 | 0.988 | 0.973 | |
AG | 0.80 | 0.80 | 0.225 | 0.277 | 0.886 | 0.897 | |
Weifang | SC | 1.00 | 1.00 | 0.089 | 0.070 | 0.978 | 0.995 |
LC | 0.50 | 1.00 | 0.142 | 0.094 | 0.954 | 0.992 | |
AG | 1.00 | 1.00 | 0.158 | 0.250 | 0.901 | 0.911 |
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Che, M.; Chen, B.; Zhang, H.; Fang, S.; Xu, G.; Lin, X.; Wang, Y. A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data. Remote Sens. 2014, 6, 5650-5670. https://doi.org/10.3390/rs6065650
Che M, Chen B, Zhang H, Fang S, Xu G, Lin X, Wang Y. A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data. Remote Sensing. 2014; 6(6):5650-5670. https://doi.org/10.3390/rs6065650
Chicago/Turabian StyleChe, Mingliang, Baozhang Chen, Huifang Zhang, Shifeng Fang, Guang Xu, Xiaofeng Lin, and Yuchen Wang. 2014. "A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data" Remote Sensing 6, no. 6: 5650-5670. https://doi.org/10.3390/rs6065650