Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A New Method for Calculating Water Quality Parameters by Integrating Space-Ground Hyperspectral Data and Spectral-In situ Assay Data

Version 1 : Received: 27 May 2022 / Approved: 30 May 2022 / Online: 30 May 2022 (05:59:36 CEST)

How to cite: Zhang, D.; Zhang, L.; Sun, X.; Gao, Y.; Lan, Z.; Wang, Y.; Zhai, H.; Li, J.; Wang, W.; Chen, M.; Li, X.; Hou, L.; Li, H. A New Method for Calculating Water Quality Parameters by Integrating Space-Ground Hyperspectral Data and Spectral-In situ Assay Data. Preprints 2022, 2022050387 (doi: 10.20944/preprints202205.0387.v1). Zhang, D.; Zhang, L.; Sun, X.; Gao, Y.; Lan, Z.; Wang, Y.; Zhai, H.; Li, J.; Wang, W.; Chen, M.; Li, X.; Hou, L.; Li, H. A New Method for Calculating Water Quality Parameters by Integrating Space-Ground Hyperspectral Data and Spectral-In situ Assay Data. Preprints 2022, 2022050387 (doi: 10.20944/preprints202205.0387.v1).

Abstract

The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity and chlorophyll during data collection. The cross correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to reduce the UAV data noise significantly. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of multiple linear regression method (MLR), support vector machine method (SVM) and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

Keywords

hyperspectral imager; UAV remote sensing; water quality monitoring; space-ground data; buoy spectrometer; water eutrophication; absorption characteristics

Subject

EARTH SCIENCES, Environmental Sciences

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