Name
Title | ||
|---|---|---|
Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance |
Abstract | ||
|---|---|---|
Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method ( , ) was comparable with that of the GEOV1 FVC product ( , ), which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TGRS.2015.2409563 | IEEE Trans. Geoscience and Remote Sensing |
Keywords | DocType | Volume |
estimation,moderateresolution imaging spectroradiometer (modis),fractional vegetation cover (fvc),general regression neural networks (grnns),global | Journal | PP |
Issue | ISSN | Citations |
99 | 0196-2892 | 13 |
PageRank | References | Authors |
0.98 | 8 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| kun jia | 1 | 30 | 11.31 |
| Shunlin Liang | 2 | 611 | 116.22 |
| Su-Hong Liu | 3 | 79 | 20.10 |
| Yuwei Li | 4 | 16 | 4.08 |
| Zhiqiang Xiao | 5 | 164 | 32.08 |
| Yunjun Yao | 6 | 105 | 30.36 |
| bo jiang | 7 | 42 | 7.73 |
| Xiang Zhao | 8 | 41 | 9.19 |
| Xiaoxia Wang | 9 | 16 | 1.41 |
| Shuai Xu | 10 | 13 | 0.98 |
| Jiao Cui | 11 | 13 | 0.98 |