基于QA权重NDVI时间序列重建效果评价研究————以长江流域为例

doi:10.13249/j.cnki.sgs.2022.11.016

Abstract

Abstract:

In this article, the quality of MODIS NDVI in the Yangtze River Basin is analyzed by calculating the mass frequency and noise ratio. Based on three commonly used reconstruction methods of S-G, A-G and D-L, three quality weight schemes are designed to reconstruct the time series MODIS NDVI data of the Yangtze River Basin from 2001 to 2020. Subsequently, the reconstruction effect is analyzed and evaluated by visual contrast, high-quality regional fidelity as well as simulation denoising. The results show that the annual noise ratio in the Yangtze River Basin is mainly 75%?125%, in which the noise in winter has a great inhibitory effect on NDVI, followed by spring and autumn, and the noise in summer has an increasing effect on NDVI. The S-G method on the basis of the third quality weight scheme exerts the optimal effect on the reconstruction of continuous missing original data. Additionally, in the high-quality region, the fidelity of A-G method is high, and the mean R² and RMSE of high-quality pixels are 0.9489 and 0.0245, respectively. Moreover, in the simulation denoising experiment, the S-G method has the least data loss pixels after reconstruction, the average value and standard deviation of the R² between the reconstructed data and the original data are 0.8616 and 0.1848 respectively the RMSE is between 0.0035 and 0.4411, and the standard deviation is 0.0383, indicating that the fidelity of the S-G method is higher in the low-quality area.

Key words: NDVI time series reconstruction, the Yangtze River Basin, QA weight, Savitzky-Golay

CLC Number:

TP79

Zhu Hui, Hu Yong, Sun Fen, Wang Qiang, Ma Xueying. Evaluation of Reconstruction Effect of NDVI Time Series Based on QA Weight: A Case Study of MODIS NDVI in the Yangtze River Basin[J].SCIENTIA GEOGRAPHICA SINICA, 2022, 42(11): 2019-2027.

Figures/Tables 10

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Bit	转换后DN值	含义
注：Bit代表转换前的数据质量。
00	0	VI产品具有良好的质量
01	1	VI产品需参考描述信息使用
10	2	VI产品受云污染
11	3	VI产品质量差

$ {NDVI}_{noise} $	1月	2月	3月	4月	5月	6月	7月	8月	9月	10月	11月	12月	平均值
注：加粗数值为该月分布最大占比。
<−200	0.63	0.63	1.36	2.28	0.93	0.35	0.23	0.16	0.22	0.39	1.33	0.65	0.76
−200~0	1.05	0.98	2.83	6.04	3.09	0.86	0.54	0.19	0.36	1.03	4.56	1.40	1.91
0~25	18.74	15.81	6.01	9.25	4.84	2.30	2.25	3.61	14.56	13.55	8.89	20.73	10.05
25~50	11.97	9.78	6.45	9.84	6.48	5.40	7.51	6.66	12.50	10.91	7.78	8.30	8.63
50~75	17.08	12.16	9.64	12.15	9.45	9.88	17.04	11.91	17.69	16.32	10.66	10.76	12.90
75~100	17.82	17.76	21.26	23.43	22.49	21.64	27.61	17.14	23.50	23.21	16.53	16.28	20.72
100~125	14.37	17.31	23.00	15.67	26.45	25.72	25.86	17.25	15.32	17.97	19.13	17.52	19.63
125~150	7.73	9.85	11.69	8.43	14.07	21.44	12.66	22.55	10.21	10.02	12.97	11.85	12.79
150~175	3.75	5.95	5.82	3.94	4.98	6.82	3.12	10.40	2.64	3.11	6.69	5.45	5.22
175~200	2.21	3.13	3.05	1.99	2.61	2.76	1.26	3.59	0.88	1.13	3.24	2.44	2.36
>200	4.66	6.64	8.88	6.97	4.62	2.83	1.92	6.54	2.13	2.34	8.21	4.61	5.03

重建方法	权重	R²					RMSE
重建方法	权重	最大值	最小值	平均值	中位数	标准差	最大值	最小值	平均值	中位数	标准差
注：加粗数值为最佳值。
A-G	方案一	0.9971	0.9150	0.9499	0.9670	0.0375	0.1162	0.0295	0.0243	0.0229	0.0090
A-G	方案二、三	0.9969	0.9143	0.9489	0.9664	0.1040	0.1177	0.0296	0.0245	0.0231	0.0112
D-L	方案一	0.9966	0.9133	0.9481	0.9658	0.0391	0.1285	0.0298	0.0247	0.0235	0.0090
D-L	方案二、三	0.9966	0.913	0.9471	0.9652	0.0402	0.1299	0.0298	0.0249	0.0238	0.0091
S-G	方案一	0.9850	0.8843	0.9059	0.9352	0.0567	0.1380	0.0344	0.0346	0.033	0.0123
S-G	方案二、三	0.9847	0.8817	0.9042	0.9336	0.0738	0.1391	0.0348	0.0349	0.0334	0.0131

重建方法	R²					RMSE
重建方法	最大值	最小值	平均值	中位数	标准差	最大值	最小值	平均值	中位数	标准差
注：加粗字体为最佳值。
A-G	0.9999	−0.8916	0.8520	0.9416	0.2389	0.9832	0.0016	0.0692	0.0547	0.0512
D-L	0.9999	−0.9741	0.8333	0.9278	0.2449	0.9168	0.0016	0.0760	0.0610	0.0534
S-G	0.9983	−0.8987	0.8616	0.9238	0.1848	0.4411	0.0035	0.0714	0.0628	0.0383

Evaluation of Reconstruction Effect of NDVI Time Series Based on QA Weight: A Case Study of MODIS NDVI in the Yangtze River Basin

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[2]	Jun Li, Hui Zhu. The Reconstruction of MODIS/NDVI Time Series Data in Chongqing [J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(3): 437-444.
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