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An Effective High Spatiotemporal Resolution NDVI Fusion Model Based on Histogram Clustering
| Content Provider | MDPI |
|---|---|
| Author | Xing, Xuegang Yan, Changzhen Jia, Yanyan Jia, Haowei Lu, Junfeng Luo, Guangjie |
| Copyright Year | 2020 |
| Description | The normalized difference vegetation index (NDVI) is a powerful tool for understanding past vegetation, monitoring the current state, and predicting its future. Due to technological and budget limitations, the existing global NDVI time-series data cannot simultaneously meet the needs of high spatial and temporal resolution. This study proposes a high spatiotemporal resolution NDVI fusion model based on histogram clustering (NDVI_FMHC), which uses a new spatiotemporal fusion framework to predict phenological and shape changes. Meanwhile, this model also uses four strategies to reduce error, including the construction of an overdetermined linear mixed model, multiscale prediction, residual distribution, and Gaussian filtering. Five groups of real MODIS_NDVI and Landsat_NDVI datasets were used to verify the predictive performance of the NDVI_FMHC. The results indicate that NDVI_FMHC has higher accuracy and robustness in forest areas (r = 0.9488 and ADD = 0.0229) and cultivated land areas (r = 0.9493 and ADD = 0.0605), while the prediction effect is relatively weak in areas subject to shape changes, such as flooded areas (r = 0.8450 and ADD = 0.0968), urban areas (r = 0.8855 and ADD = 0.0756), and fire areas (r = 0.8417 and ADD = 0.0749). Compared with ESTARFM, NDVI_LMGM, and FSDAF, NDVI_FMHC has the highest prediction accuracy, the best spatial detail retention, and the strongest ability to capture shape changes. Therefore, the NDVI_FMHC can obtain NDVI time-series data with high spatiotemporal resolution, which can be used to realize long-term land surface dynamic process research in a complex environment. |
| Starting Page | 3774 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12223774 |
| Journal | Remote Sensing |
| Issue Number | 22 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-17 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Normalized Difference Vegetation Index (ndvi) Spatiotemporal Fusion Framework Histogram Clustering Modis Landsat |
| Content Type | Text |
| Resource Type | Article |
