Developing water quality retrieval models with in situ hyperspectral data in Poyang Lake, China

Abstract

Applying remote sensing techniques to develop the retrieval models and further to obtain the spatiotemporal information of water quality parameters is necessary for understanding, managing, and protecting lake ecosystems. This study aimed to calibrate and validate the retrieval models for estimating the concentrations of chlorophyll a (C CHL), suspended particulate matter (C SPM), and dissolved organic carbon (C DOC) with the in situ hyperspectral measurements in Poyang Lake, China in 2010 and 2011. The model calibration and validation results indicated that: (1) for C CHL retrieval, significantly strong and moderate correlations existed between the measured and estimated values (with the correlation coefficient r = 0.92 and r = 0.76) using the exponential model and the three-band model, respectively, with biased estimation observed for the exponential model; (2) for retrieving C SPM , there was a strong correlation between the measured and estimated values (r = 0.95) using the exponential model; and (3) no significant correlation between measured and estimated C DOC values was found with our developed models. More work is needed to allow the water quality of Poyang Lake to be accurately and steadily estimated, especially for C CHL and C DOC .

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

“…In addition, there is no concern about the calculation of water quality parameters at different spatial resolution scales. Chlorophyll a [5,11,17,25,[38][39][40], suspended particulate matter [10,11,41,42], dissolved organic matter [11,43], transparency [44], total phosphorus [7], total nitrogen [45], ammonia nitrogen [16], biochemical oxygen demand [46], water color, colored dissolved organic matter (CDOM), dissolved organic carbon [12], transparency [20], pH [13], turbidity [47], water depth [48], and other indicators are the research objectives. Regression models [48], artificial neural networks (ANN) [49], wavelet neural networks (WNN) [50], the multi-algorithm index and look-up table technology [51], and other algorithms [52] have been well studied.…”

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

“…Analysis of existing water quality parameters, mainly focusing on chlorophyll a [4,14,22,28,[45][46][47][48]; Suspended particulate matter [9,22,47,49]; dissolved organic matter [22,49]; transparency [50], total phosphorus [6] total nitrogen [51], ammonia nitrogen [13], biochemical oxygen demand [52], water color, colored dissolved organic matter (CDOM), dissolved organic carbon [24], transparency [17], pH [10], turbidity [53], water depth [42]. In addition, some regional parameters also have preliminary conclusions, including salinity, temperature and dissolved oxygen [38], water temperature, pressure, salinity, chemistry, ocean current [20] and heavy metals [13].…”

MFCD-Net: Cross Attention Based Multimodal Fusion Network for DPC Imagery Cloud Detection

“…Directional Polarimetric Cameras (DPC) have attracted much attention as an emerging Earth observation technology [8]. Compared with traditional optical remote sensing means, clouds and aerosols are more sensitive to polarization information, which makes satellite polarimetric remote sensing more advantageous in atmosphere detection [9]. The main task of DPC is to obtain multi-band and multi-angle polarized radiation and reflection information.…”