An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes
文献类型：期刊论文
| 作者 | Luo, Shuangxiao; Song, Chunqiao; Liu, Kai; Ke, Linghong; Ma, Ronghua |
| 刊名 | SENSORS
 |
| 出版日期 | 2019 |
| 卷号 | 19期号:19 |
| 源URL | [http://159.226.73.51/handle/332005/19134]  |
| 专题 | 中国科学院南京地理与湖泊研究所 |
| 作者单位 | 中科院南京地理与湖泊研究所 |
| 推荐引用方式 GB/T 7714 | Luo, Shuangxiao,Song, Chunqiao,Liu, Kai,et al. An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes[J]. SENSORS,2019,19(19). |
| APA | Luo, Shuangxiao,Song, Chunqiao,Liu, Kai,Ke, Linghong,&Ma, Ronghua.(2019).An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes.SENSORS,19(19). |
| MLA | Luo, Shuangxiao,et al."An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes".SENSORS 19.19(2019). |
入库方式： OAI收割
来源：南京地理与湖泊研究所
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