摘要
基于云南省MOD13Q1时序数据,对比分析了不同质量设置(UI5、UI5-CSS、UI3、UI3-CSS)和不同时序重构方法(简单线性插值、Savitzky-Golay滤波、非对称高斯函数拟合法和双逻辑函数拟合法)组合下NDVI时序重构效果。结果表明:NDVI时序中无效像元数和最大间隙长度在时间和地域上的分布差异受气候干、雨季影响显著。非对称高斯函数拟合法和双逻辑函数拟合法的稳健性和拟合效果较优。NDVI时序中无效像元最大间隙长度是衡量数据质量优劣和时序重构可行性的重要指标,雨季降水和多云天气过于集中是影响云南省境内部分地区时序重构质量提升的关键。基于重构NDVI时序,云南省全境NDVI时空分布呈现雨季大于干季、西部大于东部、南部高于北部、河谷大于山地的特征。
Satellite-derived NDVI time series are often contaminated by negative atmospheric conditions and sunsen- sor-surface viewing geometries. The reconstruction of high quality NDVI time-series is crucial to the detection of long-term vegetation cover changes and the remote sensing of vegetation phenology. In this paper, MOD13Q1 time- series data covered in Yunnan province were employed to address the performance effectiveness of time-series data reconstruction methods （e. g. linear interpolation, Savitzky-Golay filtering, asymmetric Gaussian and double logistic function-fitting） through integrating with different quality setting （e. g. UIS,UIS-CSS,UI3,UI3-CSS）. The results show that seasonal and regional variations in the number and the maximum gap length of invalid pixels of time-se- ries data were mainly controlled by local climate. A comparison of four selected methods revealed that the superiori- ty of the robustness and fitting capability of asymmetric Gaussian and double logistic function-fitting methods over the other fitting techniques. The maximum gap length of invalid pixels in time-series data is an important data qual- ity indicator reflecting the feasibility for meaningful reconstruction. Concentrated clouds and precipitation in the rainy season is a crucial factor of influencing the fitting accuracy of the reconstructed time-series data in some parts of the study area. The reconstructed NDVI time-series data show that the NDVI values are higher in the rainy sea- son than those in the dry season,higher in the western than those in the eastern,higher in the southern than those in the northern, and hi^her in the river vallev than those in the uplands in the study area.
出处
《遥感技术与应用》
CSCD
北大核心
2013年第1期90-96,共7页
Remote Sensing Technology and Application
基金
国家自然科学基金项目(41061010)
云南省应用基础研究面上项目(2010ZC002)
"十二五"支撑计划(2011BAC09B07)资助
