A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

Zhubin Zheng; Chao Huang; Yunmei Li; Heng Lyu; Changchun Huang; Na Chen; Ge Liu; Yulong Guo; Shaohua Lei; Runfei Zhang; Jianzhong Li

doi:10.1016/j.scitotenv.2023.166785

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

Sci Total Environ. 2023 Dec 15:904:166785. doi: 10.1016/j.scitotenv.2023.166785. Epub 2023 Sep 4.

Authors

Zhubin Zheng¹, Chao Huang², Yunmei Li³, Heng Lyu³, Changchun Huang³, Na Chen⁴, Ge Liu⁵, Yulong Guo⁶, Shaohua Lei⁷, Runfei Zhang³, Jianzhong Li³

Affiliations

¹ School of Geography and Environmental Engineering, Jiangxi Provincial Key Laboratory of Low-Carbon Solid Waste Recycling, Gannan Normal University, Ganzhou 341000, China. Electronic address: zhengzhubin@gnnu.edu.cn.
² School of Geography and Environmental Engineering, Jiangxi Provincial Key Laboratory of Low-Carbon Solid Waste Recycling, Gannan Normal University, Ganzhou 341000, China.
³ School of Geographic Science, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, China.
⁴ Department of Environmental Sciences, Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands.
⁵ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
⁶ College of the Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China.
⁷ State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China.

PMID: 37666339
DOI: 10.1016/j.scitotenv.2023.166785

Abstract

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA₇₁₆) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NE_Chl-a). The main findings are as follows: (1) QAA₇₁₆ achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA₇₁₆, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NE_Chl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NE_Chl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

Keywords: Chlorophyll-a; Noise-equivalent Chlorophyll-a; Orbita Hyperspectral image; Semi-analytical mode; Spatial-temporal patterns.