Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

doi:10.3390/rs13101988

	Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery
	Hu, Minqi 1,2; Ma, Ronghua 2; Cao, Zhigang 1,2; Xiong, Junfeng 2; Xue, Kun 2
刊名	REMOTE SENSING
	2021-05-01
卷号	13 期号:10 页码:22
关键词	algal biomass index inland waters Landsat-8 trophic state
DOI	10.3390/rs13101988
通讯作者	Ma, Ronghua(rhma@niglas.ac.cn)
英文摘要	Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R-2 = 0.62, N = 282) and Type II water (R-2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km(2)) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.
资助项目	National Natural Science Foundation of China[42071341]
WOS关键词	YANGTZE-RIVER BASIN ; CHLOROPHYLL-A CONCENTRATION ; DISSOLVED ORGANIC-MATTER ; LONG-TERM CHANGES ; LAKE CHAOHU ; SENSING REFLECTANCE ; LIGHT-ABSORPTION ; SENTINEL-3 OLCI ; RISK-ASSESSMENT ; SATELLITE DATA
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000662645600001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/164106]
专题	中国科学院地理科学与资源研究所
通讯作者	Ma, Ronghua
作者单位	1.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Peoples R China
推荐引用方式 GB/T 7714	Hu, Minqi,Ma, Ronghua,Cao, Zhigang,et al. Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery[J]. REMOTE SENSING,2021,13(10):22.
APA	Hu, Minqi,Ma, Ronghua,Cao, Zhigang,Xiong, Junfeng,&Xue, Kun.(2021).Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery.REMOTE SENSING,13(10),22.
MLA	Hu, Minqi,et al."Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery".REMOTE SENSING 13.10(2021):22.