Author:
Lu Xiaoman,Situ Chunyan,Wang Jiajia,Zhou Liguo,Ma Weichun,Li Linna
Funder
National Key R&D program of China
Natural Science Foundation of Shanghai
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Reference44 articles.
1. Allan, M. G., Hamilton, D. P., Hicks, B., & Brabyn, L. (2015). Empirical and semi-analytical chlorophyll a algorithms for multi-temporal monitoring of New Zealand lakes using Landsat. Environmental Monitoring & Assessment, 187, 1–24.
2. Arabi, B., Salama, M., Wernand, M., & Verhoef, W. (2016). MOD2SEA: A coupled atmosphere-hydro-optical model for the retrieval of chlorophyll-a from remote sensing observations in complex turbid waters. Remote Sensing, 8, 722.
3. Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 88, 677–684.
4. Chao Rodriguez, Y., Anjoumi, A., Gómez, J., Pérez, D., & Rico, E. (2014). Using Landsat image time series to study a small water body in Northern Spain. Environmental Monitoring and Assessment, 186, 3511–3522.
5. Chen, J., Quan, W., Wen, Z., & Cui, T. (2013). An improved three-band semi-analytical algorithm for estimating chlorophyll- a concentration in highly turbid coastal waters: A case study of the Yellow River estuary, China. Environmental Earth Sciences, 69, 2709–2719.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献