A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature
文献类型：SCI/SSCI论文
| 作者 | Jing W. L.; Yang, Y. P.; Yue, X. F.; Zhao, X. D. |
| 发表日期 | 2016 |
| 关键词 | precipitation spatial downscaling land surface temperature random forests SVM cover classification random forests machine china rain variability vegetation networks scales |
| 英文摘要 | Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. In this study, we propose a spatial downscaling approach based on precipitation-land surface characteristics. Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to improve the spatial downscaling algorithm. Two machine learning algorithms, Random Forests (RF) and support vector machine (SVM), were implemented to downscale the yearly Tropical Rainfall Measuring Mission 3B43 V7 (TRMM 3B43 V7) precipitation data from 25 km to 1 km over the Tibetan Plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. According to the validation results, the RF and SVM-based models produced higher accuracy than the exponential regression (ER) model and multiple linear regression (MLR) model. The downscaled results also had higher accuracy than the original TRMM 3B43 V7 dataset. Moreover, models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation-land surface temperature when downscaling TRMM 3B43 V7 precipitation data. The RF model with only NDVI and DEM produced much worse accuracy than the SVM model with the same variables. This indicates that the Random Forests algorithm is more sensitive to LSTs than the SVM when downscaling yearly TRMM 3B43 V7 precipitation data over Tibetan Plateau. Moreover, the precipitation-LSTs relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale. |
| 出处 | Remote Sensing |
| 卷 | 8 |
| 期 | 8 |
| 语种 | 英语 |
| ISSN号 | 2072-4292 |
| DOI标识 | 10.3390/rs8080655 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/43015]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Jing W. L.,Yang, Y. P.,Yue, X. F.,et al. A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature. 2016. |
入库方式： OAI收割
来源：地理科学与资源研究所
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