Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing
文献类型：期刊论文
| 作者 | Ma, Baibing2,3; Xu, Ming2,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 15期号:20页码:5042 |
| 关键词 | MODIS LAI outliers temporal patterns inter-quartile range post-processing |
| DOI | 10.3390/rs15205042 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The moderate resolution imaging spectroradiometer (MODIS) calculates the leaf area index (LAI) for each pixel without incorporating the temporal correlation information, leading to a higher sensitivity for the LAI that produces uncertainties in observed reflectance. As a result, an increased noise level is observed in the timeseries, making the data discontinuous and inconsistent in space and time. Therefore, it is important to identify and handle the outliers during the post-processing of MODIS data. This study proposed a method to identify the MODIS LAI outliers based on the analyses of temporal patterns, including the interannual and seasonal changes in the LAI. The analysis was carried out utilizing the data from 278 global MODIS LAI sites and the results were verified against the measurement obtained from 52 ground stations. The results from the analyses detected 50 and 92 outliers based on 1.5 sigma and 1.0 sigma standard deviations, respectively, of the difference between the MODIS LAI and ground measurements; correspondingly, 46 and 65 outliers, respectively, were identified by incorporating temporal patterns during the post-processing of the data. The validation results exhibited improved values of the coefficient of determination (R2) after eliminating the MODIS LAI outliers identified through the interannual and seasonal patterns. Specifically, the R2 between the ground measurement LAI and MODIS LAI increased from 0.51 to 0.54, 0.88, and 0.90 after eliminating MODIS LAI outliers when considering the interannual patterns, seasonal patterns, and both the interannual and seasonal patterns, respectively. The results from the study provided valuable information and theoretical support to improve MODIS LAI post-processing. |
| WOS关键词 | LAND-SURFACE PHENOLOGY ; NDVI TIME-SERIES ; GROSS PRIMARY PRODUCTION ; VEGETATION PHENOLOGY ; CLIMATE-CHANGE ; GLOBAL PRODUCTS ; RESOLUTION LAI ; DATA SET ; FOREST ; VALIDATION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001093587800001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199472]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Hong Kong Univ Sci & Technol, Jiangmen Lab Carbon Sci & Technol, Jiangmen 529199, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Synth Res Ctr Chinese Ecosyst Res Network, Key Lab Ecosyst Network Observat & Modelling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Baibing,Xu, Ming. Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing[J]. REMOTE SENSING,2023,15(20):5042. |
| APA | Ma, Baibing,&Xu, Ming.(2023).Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing.REMOTE SENSING,15(20),5042. |
| MLA | Ma, Baibing,et al."Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing".REMOTE SENSING 15.20(2023):5042. |
入库方式： OAI收割
来源：地理科学与资源研究所
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