%0 Journal Article %1 journals/sensors/LuoSLKM19 %A Luo, Shuangxiao %A Song, Chunqiao %A Liu, Kai %A Ke, Linghong %A Ma, Ronghua %D 2019 %J Sensors %K dblp %N 19 %P 4247 %T An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes. %U http://dblp.uni-trier.de/db/journals/sensors/sensors19.html#LuoSLKM19 %V 19

@article{journals/sensors/LuoSLKM19, added-at = {2019-11-26T00:00:00.000+0100}, author = {Luo, Shuangxiao and Song, Chunqiao and Liu, Kai and Ke, Linghong and Ma, Ronghua}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2275d7479c0b4c850f7e356bd4b4f9dfe/dblp}, ee = {https://doi.org/10.3390/s19194247}, interhash = {ea0b7f781596cae3e76c2ee3cb7ef178}, intrahash = {275d7479c0b4c850f7e356bd4b4f9dfe}, journal = {Sensors}, keywords = {dblp}, number = 19, pages = 4247, timestamp = {2019-12-11T12:57:55.000+0100}, title = {An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes.}, url = {http://dblp.uni-trier.de/db/journals/sensors/sensors19.html#LuoSLKM19}, volume = 19, year = 2019 }