基于加权光滑样条的多云雨地区NDVI重构——以广东省为例

doi:10.13284/j.cnki.rddl.003286

摘要/Abstract

摘要：

为探讨加权光滑样条算法（sspw）在多云雨地区重构高质量NDVI的效果，以广东省为研究区，对其范围内2005—2015年MOD13Q1 NDVI数据进行重构。为证实sspw在重构多云雨地区NDVI的优势，选取多云雨样区的重构影像进行对比，并将未加权滤波［普通光滑样条（ssp）、Savitzky-Golay（SG）滤波］与加权滤波（sspw、加权SG滤波）重构的NDVI时序曲线进行对比。结果发现：1）sspw能有效重构多云雨地区NDVI。2）与未加权滤波相比，在质量权重的辅助下，加权滤波重构的NDVI数据时间序列曲线更接近原始NDVI上包络线，加权滤波重构的NDVI与原始NDVI线性相关性更强，误差更小；而加权滤波中，sspw比加权SG（Savitzky-Golay）滤波更能抑制图像中的噪音。总体来说，sspw重构的数据误差更小，与原始高质量数据相关性更强，重构效果更好，更适合多云雨地区。

关键词: 多云雨地区, 加权光滑样条对比, NDVI重构, 滤波, 广东省

Abstract:

Compared with other regions, there are more low-quality pixels in the Normalized Difference Vegetation Index (NDVI) of cloudy and rainy areas in terms of temporal and spatial distribution. This would increase the difficulty of reconstructing a high-quality NDVI in cloudy and rainy areas. Guangdong Province—located in the subtropical monsoon region of southern China—is a typical cloudy and rainy province with a high intensity of human activities. Therefore, it is particularly significant to study vegetation change and the response of vegetation to human activities in Guangdong and reconstruct a high-quality NDVI within it. To verify that a weighted smooth spline (sspw) can reconstruct a high-quality NDVI in cloudy and rainy areas and provide reliable data for follow-up studies, this paper takes Guangdong Province as an example and uses MOD13Q1 NDVI from 2000 to 2005 to reconstruct its NDVI. Further, to verify the reliability of the NDVI reconstructed by sspw, the results are compared with those of smooth spline (ssp), Savitzky-Golay (SG), and weighted SG filter (SGw). The following conclusions were drawn. First, compared with SGw, sspw, and SG, sspw-reconstructed NDVI had the largest number of strong correlations and low error pixels. In the correlation coefficient results, 41% of the NDVI pixels reconstructed by sspw had a correlation coefficient of more than 0.7 with the original; in the root mean square error, 64% of the NDVI pixels reconstructed by sspw had an error of less than 0.1 with the original. In terms of Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC), the NDVI reconstructed by sspw had 41% of all pixels' AIC < 197 (lower AIC) and 52% of all pixels' BIC < 235 (lower BIC), respectively. This indicates that sspw can reconstruct a high-quality NDVI in cloudy and rainy areas. Second, while the NDVI curve characteristics of typical sample points can be used to determine the reasonable range of the spar coefficient, spar = 0.25 is more suitable for NDVI reconstruction in Guangdong Province. Third, compared with the unweighted ssp and SG, the NDVI time series curve reconstructed by sspw and SGw with quality weight is closer to the original value. Finally, compared with the NDVI reconstructed by sspw, in cloudy and rainy areas, the NDVI reconstructed by SG and SGw will retain some noise and abnormal values in the low- and high-value parts. Therefore, sspw is more suitable for NDVI reconstruction in cloudy and rainy areas.

Key words: cloudy and rainy areas, weighted smooth spline comparison, NDVI reconstruction, filter, Guangdong Province

中图分类号:

TP751

阮柱, 匡耀求. 基于加权光滑样条的多云雨地区NDVI重构——以广东省为例[J]. 热带地理, 2020, 40(6): 1051-1062.

Zhu Ruan, Yaoqiu Kuang. Normalized Difference Vegetation Index Reconstruction of Cloudy and Rainy Areas Based on Weighted Smooth Spline: A Case Study of Guangdong Province[J]. Tropical Geography, 2020, 40(6): 1051-1062.

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像元值	数据说明	质量说明	权重赋值
0	理想可用数据	好	1
1	临界的数据	较好	0.7
2	雪或者冰覆盖	差	0.5
3	云覆盖	极差	0.1

样点号	地类	位置	地形	NDVI质量	NDVI值
1	林地	广东省东北部	低山	总体较好	高
2	林地	广东省西南部	低山	总体较好	高
3	耕地	广东省西部	平原	总体好	较高
4	耕地	广东省东部	平原	总体好	较高
5	建设用地	广东省中部偏北	平原	总体较差	较低
6	建设用地	广东省中部偏南	平原	总体较差	较低
7	耕地	广东省西部偏南	平原	总体好	较高

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