A quantitative inverse remote sensing model for measuring suspended sediment concentration

2010 ◽  
Author(s):  
Fei Tao ◽  
Ming Sheng ◽  
Yun-song Ji ◽  
Jun-lin Wei
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Remote Sensing Model ◽  
Sensing Model

scholarly journals A Semi-Analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

2015 ◽  
Vol 7 (5) ◽  
pp. 5373-5397 ◽  
Author(s):  
Jin-Ling Kong ◽  
Xiao-Ming Sun ◽  
David Wong ◽  
Yan Chen ◽  
Jing Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Analytical Model ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration

scholarly journals Automatic calibration of a large-scale sediment model using suspended sediment concentration, water quality, and remote sensing data

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Hugo de Oliveira Fagundes ◽  
Fernando Mainardi Fan ◽  
Rodrigo Cauduro Dias de Paiva
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Suspended Sediment ◽  
Large Scale ◽  
Suspended Sediment Concentration ◽  
Remote Sensing Data ◽  
Sediment Concentration ◽  
Spatial Discretization ◽  
Calibration And Validation ◽  
Sediment Model

ABSTRACT Calibration and validation are two important steps in the application of sediment models requiring observed data. This study aims to investigate the potential use of suspended sediment concentration (SSC), water quality and remote sensing data to calibrate and validate a large-scale sediment model. Observed data from across 108 stations located in the Doce River basin was used for the period between 1997-2010. Ten calibration and validation experiments using the MOCOM-UA optimization algorithm coupled with the MGB-SED model were carried out, which, over the same period of time, resulted in 37 calibration and 111 validation tests. The experiments were performed by modifying metrics, spatial discretization, observed data and parameters of the MOCOM-UA algorithm. Results generally demonstrated that the values of correlation presented slight variations and were superior in the calibration step. Additionally, increasing spatial discretization or establishing a background concentration for the model allowed for improved results. In a station with high quantity of SSC data, calibration improved the ENS coefficient from -0.44 to 0.44. The experiments showed that the spectral surface reflectance, total suspended solids and turbidity data have the potential to enhance the performance of sediment models.

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