气候变化领域集成服务门户(CCPortal): A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes

CCPortal

DOI	10.1016/j.rse.2020.111974
	A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes
	Cao Z.; Ma R.; Duan H.; Pahlevan N.; Melack J.; Shen M.; Xue K.
发表日期	2020
ISSN	00344257
卷号	248
英文摘要	Landsat-8 Operational Land Imager (OLI) provides an opportunity to map chlorophyll-a (Chla) in lake waters at spatial scales not feasible with ocean color missions. Although state-of-the-art algorithms to estimate Chla in lakes from satellite-borne sensors have improved, there are no robust and reliable algorithms to generate Chla time series from OLI imageries in turbid lakes due to the absence of a red-edge band and issues with atmospheric correction. Here, a machine learning approach termed the extreme gradient boosting tree (BST) was employed to develop an algorithm for Chla estimation from OLI in turbid lakes. This model was developed and validated by linking Rayleigh-corrected reflectance to near-synchronous in situ Chla data available from eight lakes in eastern China (N = 225) and three coastal and inland waters in SeaWiFS Bio-optical Archive and Storage System (N = 97). The BST model performed well on a subset of data (N = 102, R2 = 0.79, root mean squared difference = 7.1 μg L−1, mean absolute percentage error = 24%, mean absolute error = 1.4, Bias = 0.9), and had better Chla retrievals than several band-ratio algorithms and a random forest approach. The performance of BST model was judged as appropriate only for the range of conditions in the training data. Given these limitations, spatial and temporal variations of Chla in hundreds of lakes larger than 1 km2 in eastern China for the period of 2013–2018 were mapped using the BST model. OLI-derived Chla indicated that small lakes (<50 km2) had greater Chla than the larger lakes. This research suggests that machine-learning models provide practical approaches to estimate Chla in turbid lakes via broadband instruments like OLI and that extending to other regions requires training with a representative dataset. © 2020 Elsevier Inc.
英文关键词	Eutrophication; Lakes; Landsat; Machine learning
语种	英语
scopus关键词	Adaptive boosting; Chlorophyll; Decision trees; Digital storage; Image enhancement; Lakes; Optical data storage; Atmospheric corrections; Broadband instruments; Machine learning approaches; Machine learning models; Mean absolute percentage error; Operational land imager; Spatial and temporal variation; State-of-the-art algorithms; Machine learning; algorithm; bootstrapping; chlorophyll a; error analysis; estimation method; lake water; Landsat; machine learning; ocean color; performance assessment; satellite data; satellite imagery; China
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/179198
作者单位	Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Science Systems and Applications Inc., 10210 Greenbelt Rd. Suite 600, Lanham, MD 20706, United States; Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, United States
推荐引用方式 GB/T 7714	Cao Z.,Ma R.,Duan H.,et al. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes[J],2020,248.
APA	Cao Z..,Ma R..,Duan H..,Pahlevan N..,Melack J..,...&Xue K..(2020).A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes.Remote Sensing of Environment,248.
MLA	Cao Z.,et al."A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes".Remote Sensing of Environment 248(2020).