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Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System

文献类型：期刊论文

作者Ma, Yuanxu5,6; Sun, Dongqi4; Liu, Weihua3,4; You, Yongfa2,5; Wang, Siyuan1; Sun, Zhongchang5,6; Wang, Shaohua5,6
刊名REMOTE SENSING
出版日期2022-12-01
卷号14期号:23页码:18
关键词remote sensing chl-a concentration spectral index piecewise algorithm model evaluation sensitivity analysis highland river
DOI10.3390/rs14236119
通讯作者Liu, Weihua(liuwh.20b@igsnrr.ac.cn)
英文摘要Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m(3). Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m(3) corresponded to the D3B value of -0.051; therefore, we used -0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8-10 mg/m(3), which was higher than that in rivers with a range of 2-4 mg/m(3). The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems.
WOS关键词TURBID PRODUCTIVE WATERS ; BIOOPTICAL PARAMETER VARIABILITY ; RESOLUTION IMAGING SPECTROMETER ; MERIS ; LAKE ; PHYTOPLANKTON ; COASTAL ; MODEL ; INLAND ; FLUORESCENCE
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030104] ; Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals[CBAS2022IRP04] ; Third Xinjiang Scientific Expedition Program[2021xjkk1201] ; Special Exchange Program of Chinese Academy of Sciences[2022000383] ; National Natural Science Foundation of China[42071008]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000897319800001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals ; Third Xinjiang Scientific Expedition Program ; Special Exchange Program of Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/188098]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Weihua
作者单位1.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
2.Auburn Univ, Coll Forestry Wildlife & Environm, Int Ctr Climate & Global Change Res, Auburn, AL 36849 USA
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Resource Res, Beijing 100101, Peoples R China
5.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
6.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Yuanxu,Sun, Dongqi,Liu, Weihua,et al. Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System[J]. REMOTE SENSING,2022,14(23):18.
APA Ma, Yuanxu.,Sun, Dongqi.,Liu, Weihua.,You, Yongfa.,Wang, Siyuan.,...&Wang, Shaohua.(2022).Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System.REMOTE SENSING,14(23),18.
MLA Ma, Yuanxu,et al."Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System".REMOTE SENSING 14.23(2022):18.

入库方式： OAI收割

来源：地理科学与资源研究所

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