气候变化领域集成服务门户(CCPortal): Quantification of lake clarity in China using Landsat OLI imagery data

CCPortal

DOI	10.1016/j.rse.2020.111800
	Quantification of lake clarity in China using Landsat OLI imagery data
	Song K.; Liu G.; Wang Q.; Wen Z.; Lyu L.; Du Y.; Sha L.; Fang C.
发表日期	2020
ISSN	00344257
卷号	243
英文摘要	The eutrophication of lake and reservoir (hereafter referred to as lakes) has attracted concerns from the public and government in China. Water clarity is a reliable indicator for quantifying eutrophic status because of its strong association with chlorophyll-a, total suspended matter, and nutrients. Traditionally, water clarity is measured using Secchi disk depth (SD). By linking the spectral signal from water surface with in situ measured SD, remote sensing provides a useful tool for SD estimation at a large scale in a repetitive manner. Remote sensing derived water clarity has been reported in many regions with specific models established for different satellite overpasses concurrent with in situ measured SD, but national water clarity remained unknown in China. In this study, 2152 samples were collected from 34 field campaigns in 2013–2018, of which 1792 samples were gathered within ± 7 days of Landsat OLI overpasses. We used Landsat 8 OLI bands 1–4 to develop regression models (n = 1016), and 782 samples to validate model performances. We further collected three additional in situ SD datasets to validate the best performance model, and eventually used it to map SD at a national scale with OLI images mainly acquired in 2016. Our results indicated that the entire dataset of SD has a strong association with Landsat reflectance, yielding low root mean square error between measured and estimated SD (RMSE = 63 cm) for lakes in China. The national water clarity was averaged to 176 cm in 2016 with large spatial variability (S.D: 216 cm) due to the marked variation between turbid waters in the east plain area and clean water across the Tibet Plateau. Lakes in the northeastern (75 cm) and eastern (84 cm) China had low clarity due to shallow water depth combined with high suspended matter and algal abundance. Lakes in the Yungui Plateau (91 cm), Inner Mongolia and Xinjiang autonomous regions (114 cm) exhibited intermediate clarity; while lakes in the Tibet Plateau (294 cm) displayed the highest clarity. This investigation demonstrated that Landsat imagery with Rayleigh scattering correction reflectance combined with in situ observation can provide quantitative information about the lake clarity with surface area >8 ha. Moreover, this method has the potential to retrieve SD with archived Landsat imagery to determine the temporal variation of SD at national or continental scale which can be used to support inland water management and decision-maker for improving water quality. © 2020 Elsevier Inc.
英文关键词	Eutrophic; Landsat OLI; Remote estimation; SD
语种	英语
scopus关键词	Decision making; Eutrophication; Image enhancement; Lakes; Mean square error; Overpasses; Reflection; Regression analysis; Remote sensing; Water management; Water quality; In-situ observations; Quantitative information; Root mean square errors; Scattering corrections; Shallow water depths; Spatial variability; Temporal variation; Total suspended matter; Reservoirs (water); Landsat; physicochemical property; quantitative analysis; satellite data; water quality; China; algae
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179336
作者单位	Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, China; University of Chinese Academy of Sciences, Beijing, 100049, China; School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China
推荐引用方式 GB/T 7714	Song K.,Liu G.,Wang Q.,et al. Quantification of lake clarity in China using Landsat OLI imagery data[J],2020,243.
APA	Song K..,Liu G..,Wang Q..,Wen Z..,Lyu L..,...&Fang C..(2020).Quantification of lake clarity in China using Landsat OLI imagery data.Remote Sensing of Environment,243.
MLA	Song K.,et al."Quantification of lake clarity in China using Landsat OLI imagery data".Remote Sensing of Environment 243(2020).