Ensemble modeling methods for remote sensing retrieval of water quality parameters in inland wate
CAO Yin1,2, YE Yun-tao2, ZHAO Hong-li2, JIANG Yun-zhong2, WANG Hao1,2, WANG Jun-feng1
1. State Environmental Protection Engineering Center for Pollution Control in Textile Industry, College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China;
2. State Key Laboratory of Smimulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Based on the measured hyperspectral data and concentration of chlorophyll a, total suspended matter (TSM) and turbidity obtained during June 11 to 13, 2015 in Weishan Lake, empirical models and PSO-SVM model were established to retrieve the three water quality parameters. Meanwhile, the performance of those models was evaluated to determine the models applied to ensemble modeling. The ensemble models containing EW-CM, SPA-CM and BMA were established to retrieve the three water quality parameters by using deterministic ensemble method and probabilistic ensemble method. The deterministic and probabilistic ensemble method was based on the entropy weight method along with pair analysis method and Bayesian averaging method, respectively. Bayesian averaging method was employed to obtain the retrieval uncertainty range of the three water quality parameters by using the single model and the BMA ensemble model, and the retrieval uncertainty range of these models was compared. These results demonstrated that (1) the accuracy of SPA-CM model was better than that of EW-CM model in deterministic ensemble models; (2) the modeling accuracy of BMA probabilistic ensemble model was better than that of SPA-CM and EW-CM model; the verification accuracy of BMA probabilistic ensemble model was similar with that of EW-CM model but slightly lower than that of the SPA-CM model; (3) Probabilistic ensemble modeling could obtain the retrieval uncertainty range of water quality parameters by using the ensemble model and the single model; (4) The deterministic and probabilistic ensemble model associated with the single model information showed a higher modeling and verification accuracy, which could be used to reduce the uncertainty of water quality parameters retrieval compared with single model and promote the retrieval accuracy of water quality parameters in a manner.
曹引, 冶运涛, 赵红莉, 蒋云钟, 王浩, 王俊锋. 内陆水体水质参数遥感反演集合建模方法[J]. 中国环境科学, 2017, 37(10): 3940-3951.
CAO Yin, YE Yun-tao, ZHAO Hong-li, JIANG Yun-zhong, WANG Hao, WANG Jun-feng. Ensemble modeling methods for remote sensing retrieval of water quality parameters in inland wate. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(10): 3940-3951.
Tian L Q, Chen X L, Li W B, et al. Retrieval of total suspended matter concentration from Gaofen-1Wide Field Imager (WFI) multispectral imagery with the assistance of Terra MODIS in turbid water-case in Deep Bay[J]. International Journal of Remote Sensing, 2016,37(14):3400-3413.
Tian H, Cao C, Xu M, et al. Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation[J]. International Journal of Remote Sensing, 2014,35(16):5984-6003.
Sun D Y, Li Y M, Wang Q. A unified model for remotely estimating chlorophyll a in Lake Taihu, China, based on SVM and in situ, hyperspectral data[J]. Geoscience & Remote Sensing IEEE Transactions on, 2009,47(8):2957-2965.
Raftery A E, Gneiting T, Balabdaoui F, et al. Using Bayesian model averaging to calibrate forecast ensembles[J]. Monthly Weather Review, 2005,113(5):1155-1174.
Mueller, J.L., Fargion, G.S., Mcclain, C.R., et al. Ocean optics protocols for satellite ocean color sensor validation, Revision 4, Volume Ⅲ:radiometric measurements and data analysis protocols[R]. Greenbet, Maryland:NASA Goddard Space Flight Center, 2003.
Giorgio D, Gitelson A A, Rundquist D C. Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters[J]. Geophysical Research Letters, 2003,30(18):159-171.
Le C F, Li Y M, Yong Z, et al. A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes:the case of Taihu Lake, China[J]. Remote Sensing of Environment, 2009, 113(6):1175-1182.