Remote sensing reflectance model of optically active components of turbid waters

1994 ◽  
Author(s):  
Tiit Kutser ◽  
Helgi Arst
Keyword(s):  
Remote Sensing ◽  
Optically Active ◽  
Active Components ◽  
Turbid Waters ◽  
Remote Sensing Reflectance ◽  
Reflectance Model

An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters

2018 ◽  
Vol 215 ◽  
pp. 18-32 ◽  
Author(s):  
Jianwei Wei ◽  
Zhongping Lee ◽  
Rodrigo Garcia ◽  
Laura Zoffoli ◽  
Roy A. Armstrong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coral Reefs ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Turbid Waters ◽  
Remote Sensing Reflectance

scholarly journals A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2125 ◽  
Author(s):  
Lucas Silveira Kupssinskü ◽  
Tainá Thomassim Guimarães ◽  
Eniuce Menezes de Souza ◽  
Daniel C. Zanotta ◽  
Mauricio Roberto Veronez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Chlorophyll A ◽  
Suspended Solids ◽  
Analysis Data ◽  
Optically Active ◽  
Laboratory Analysis ◽  
Critical Parameters ◽  
Active Components ◽  
Sentinel 2

Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.

scholarly journals Remote Sensing Retrieval of Total Phosphorus in the Pearl River Channels Based on the GF-1 Remote Sensing Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1420
Author(s):  
Shijun Lu ◽  
Ruru Deng ◽  
Yeheng Liang ◽  
Longhai Xiong ◽  
Xianjun Ai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Total Phosphorus ◽  
Remote Sensing Data ◽  
Optically Active ◽  
Pearl River ◽  
Percentage Error ◽  
River Channels ◽  
Active Components ◽  
Sensing Data ◽  
The Pearl River

Total phosphorus (TP) concentration is one of the indicators for surface water quality evaluation. In this study, an indirect algorithm was proposed to retrieve TP concentration. This algorithm retrieves the TP concentration in urban waters based on Gaofen-1 (GF-1) remote sensing data. The algorithm uses the correlation between remote-sensing reflectance, optically significant constituents of water (chlorophyll, suspended sediment, and organic matter (excluding algae)), and TP to establish a retrieval model. First, the concentrations of optically active components are retrieved using a semi-analytical model. Second, the correlation between TP and optically active components is used to retrieve the TP concentration in waters. The GF-1 remote sensing data for 7 August 2015 were used to perform remote sensing retrieval of TP concentration in the Pearl River channels in Guangzhou, China. The results show that the TP concentration in most areas of the Front Channel, Western Channel, Guangzhou Channel, and the western part of the Back Channel was higher than 0.2 mg/L, while the TP concentration in the middle and eastern parts of the Back Channel was generally lower than 0.2 mg/L. The mean absolute percentage error of the retrieval is 24.18%. The experimental results show that the model is suitable for remote sensing retrieval of TP in urban waters in Guangzhou.

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