Name
Title | ||
|---|---|---|
Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework |
Abstract | ||
|---|---|---|
To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000x2013;2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) x003D; 0.062; mean $R = 0.851$ ) among the three modes and more accurate than ESTARFM (mean RMSE x003D; 0.065; mean $R = 0.776$ ) and LMGM (mean RMSE x003D; 0.074; mean $R = 0.734$ ). For the 12 years (2000x2013;2011), PK-STEM also achieves high accuracies with mean RMSE x003D; 0.066 and $R = 0.938$ . PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2021.3067913 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Remote sensing, Spatiotemporal phenomena, Earth, Market research, Artificial satellites, Spatial resolution, MODIS, Fraction of absorbed photosynthetically active radiation (FAPAR, FPAR), spatiotemporal data fusion, Landsat, MODIS, PK-STEM, remote sensing trend surface (RSTS), vegetation dynamic | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiting Wang | 1 | 4 | 5.55 |
| Yan, G. | 2 | 9 | 10.04 |
| Donghui Xie | 3 | 22 | 17.02 |
| Ronghai Hu | 4 | 10 | 7.88 |
| Hu Zhang | 5 | 1 | 1.71 |