Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery

ZHENG Zhubin; ZHANG Runfei; LI Jianzhong; LIN Lin; YANG Hong

doi:10.11834/jrs.20211264

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Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery
Vol. 26, Issue 11, Pages: 2162-2173(2022)
Published： 07 November 2022 ，
DOI： 10.11834/jrs.20211264

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郑著彬，张润飞，李建忠，林琳，杨虹.2022.基于欧比特高光谱影像的滇池叶绿素a浓度遥感反演研究.遥感学报，26（11）： 2162-2173

Zheng Z B，Zhang R F，Li J Z，Lin L and Yang H. 2022. Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery. National Remote Sensing Bulletin， 26（11）：2162-2173
郑著彬，张润飞，李建忠，林琳，杨虹.2022.基于欧比特高光谱影像的滇池叶绿素a浓度遥感反演研究.遥感学报，26（11）： 2162-2173 DOI： 10.11834/jrs.20211264.

Zheng Z B，Zhang R F，Li J Z，Lin L and Yang H. 2022. Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery. National Remote Sensing Bulletin， 26（11）：2162-2173 DOI： 10.11834/jrs.20211264.

摘要

水体叶绿素a ，即Chla（chlorophyll-a）浓度是表征水体富营养化程度的关键性指标，对于水环境评估和水质遥感监测具有重要意义。欧比特高光谱卫星是中国于2018年发射的新一代高光谱卫星，然而其在内陆水体水质遥感监测的适用性仍有待验证。本研究以高原富营养化湖泊滇池为研究区，以叶绿素a浓度为反演指标，利用滇池两次野外现场实测数据和欧比特高光谱OHS（Orbita Hyperspectral）影像，通过分析滇池水体的光学特性，构建了适用于欧比特高光谱影像的滇池水体叶绿素a浓度遥感反演模型，并通过星地同步数据验证了反演模型的有效性与可行性，获得了滇池叶绿素a浓度的空间格局。结果表明：（1）波段比值模型（B17/B9）适合于基于欧比特高光谱影像的滇池水体叶绿素a浓度的遥感反演，模型反演精度较高，决定系数（

）为0.804，均方根误差（RMSE）和平均绝对误差百分比（MAPE）分别为6.99 μg/L和6.32%；（2）2019年4月2日滇池水体叶绿素a浓度呈现出由湖岸向湖泊中心逐渐降低的趋势，东北部与东南部呈幂函数型递减，西北部呈线性递减；（3）滇池欧比特高光谱影像的近岸4个水体像元易受到陆地邻近效应的影响。本研究提出的基于欧比特高光谱影像的波段比值模型能够实现滇池叶绿素a浓度的遥感反演，为内陆富营养化水体叶绿素a浓度的遥感监测提供了一种新的思路与方法。

Abstract

The chlorophyll-a (Chla) concentration that refers to the content of Chla contained in per unit volume water

is a key indicator describing the eutrophication degree of lake waters. Accurate quantification of Chla concentration is of great significance for water environment assessment and water quality monitoring and has become a focus on the study of watercolor remote sensing. Orbita hyperspectral (OHS) satellite is a new generation of hyperspectral satellites launched by Zhuhai Orbita Aerospace Technology Co.

Ltd. in 2018

which covers spectral range of 400~1000 nm and 32 spectral channels with both high spectral and high spatial resolution (2.5 nm and 10 m

respectively)

showing great potential for inland water quality monitoring application. However

this satellite has a short operating period from launch

and the applicability of the generated images needs to be further investigated and validated.

Dianchi Lake

a typical eutrophic plateau lake

was used as the study area for Chla concentration retrieval based on the OHS hyperspectral imagery. We collected in-situ spectra and Chla concentration from two cruise surveys in Dianchi Lake and acquired the satellite-ground synchronization data within one day of the OHS satellite overpass. Data from two field campaigns including 72 sampling sites were used for model calibration and validation

and ground data matched with satellite overpass including 10 sampling sites was used to further validate the retrieval results after the calibrated model was applied to the OHS imagery. We first utilized all 72 in-situ spectra to explore the relationship between all possible combinations of band ratio and Chla concentrations to seek the optimal band ratio model. Immediately after we used the spectral response function of the OHS imagery to resample the in-situ spectra to the band configuration of the OHS imagery

the OHS-based band ratio model was calibrated using 48 field-measured data according to the optimal band ratio combination of in-situ spectra

and the remaining 24 data were used to evaluate model accuracy. We further validated the retrieval results using the Chla concentrations at 10 sampling points synchronized with the OHS image after the OHS-based band ratio model was applied to the OHS image

and the spatial pattern of Chla concentration in Dianchi Lake was revealed.

The band ratio

(716)/

(595) had the highest correlation with Chla concentration in terms of the in-situ spectra with

=0.819

and the corresponding OHS-based band ratio model (B17/B9) was suitable for remote sensing retrieval of Chla concentration in Dianchi Lake with

of 0.804

the root-mean-square error (RMSE) of 6.99 μg/L and the mean absolute percentage error (MAPE) of 6.32%. The retrieval results of the OHS-based band ratio model applied to the OHS image and the spatial pattern of Chla concentration were reasonable with acceptable errors (RMSE=12.47 μg/L

MAPE=22.53%). The spatial pattern of Chla concentration in Dianchi Lake showed a decreasing trend from the lakeshore to the center of the lake on April 2

2019

the northeast and southeast decrease fitted a power function

whereas the northwest decrease described a linear function. The pixel reflectance of the nearshore waters may be higher than that of the normal waters due to the land adjacency effect

which may lead to a high concentration of retrieved Chla along the coast. In the OHS imagery of Dianchi Lake

four nearshore water pixels could be easily influenced by the land adjacency effect

so these four pixels needed to be masked to eliminate the influence. In addition

compared with the existing Chla concentration retrieval algorithms

the band ratio model (B17/B9) proposed in this study improved the retrieval accuracy of Chla concentration.

In conclusion

the OHS-based band ratio model works efficiently and reliably for retrieving Chla concentration in Dianchi Lake. OHS hyperspectral data show great potential in terms of accurate retrieval of Chla concentration for inland waters

providing a new means for remote sensing monitoring of Chla concentration. However

whether the OHS-based band ratio model developed in this study applies to other water bodies with different optical properties still needs to be further investigated and tested. In future studies

the performance of the model will be further examined by collecting more field data in different lakes.

关键词

欧比特高光谱影像叶绿素a滇池陆地邻近效应

Keywords

Orbita hyperspectral imagerychlorophyll-aDianchi Lakeland adjacency effect

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