Estimating Total Suspended Matter in Low to Extremely High Level Turbid River Surface Waters using a WISP-3 Hyperspectral Radiometer and Sentinel-2 Optical Imagery

Estimating Total Suspended Matter in Low to Extremely High Level Turbid River Surface Waters using a WISP-3 Hyperspectral Radiometer and Sentinel-2 Optical Imagery: A case study conducted on the Brantas River Basin, East-Java, Indonesia

Wiggins, J.C. (TU Delft Civil Engineering and Geosciences)

Ertsen, M.W. (mentor)
Citrosiswoyo, W. (graduation committee)
Hariyanto, T. (graduation committee)
Hoes, O.A.C. (graduation committee)
Laanen, M. (graduation committee)
Lhermitte, S.L.M. (graduation committee)

Delft University of Technology

Fostering inclusive growth, health and equity by mainstreaming water quality in River Basin Management in the Brantas River Basin, Indonesia

2020-08-17

This research focuses on using Sentinel-2 optical imagery to provide a means of high-resolution monitoring and evaluation of changes in Total Suspended Matter (TSM) concentration in the Brantas river basin. In situ spectral measurements as well as laboratory results show an extremely turbid nature of the Brantas River surface water. Current monitoring of the river water quality, is done by point measurements representing point estimations of the water quality in time and pace. Interactions within the system are mostly unknown. Having accurate knowledge of near real time water quality information will greatly enhance the effectiveness of the monitoring organizations, especially if this comes in a high
spatial and temporal resolution. The Sentinel- 2 remote sensing platform delivers information which can be used to derive such data with a 10m resolution and revisit time of 5 days. To estimate TSM concentrations a multi-conditional algorithm is developed. It uses linear regression for low to medium
TSM concentrations based on the green and red band reflectance values and polynomial regression for high to extremely high TSM concentrations based on the red edge NIR band. Testing the multi-conditional algorithm on the WISP-3 in situ spectral data shows the model’s performance is good with r2 = 0.79, RMSE
= 66.5 mg/L and NRMSE = 9.7%. Performance of the multi-conditional algorithm is found to be poor when based on Sentinel-2 (S2) bottom of atmosphere data from bands green, red and red edge NIR. However, when recalibrating the polynomial model on Sentinel-2 atmospherically uncorrected top of atmosphere
data, results are more promising: r2 = 0.75, RMSE = 64.2 mg/L and NRMSE = 11.3% . Also, TSM estimates from remote sensing reflectances atmospherically corrected by different processors are compared, from which ACOLITE (RMSE = 5.0 mg/L, NRMSE = 25.3%) performs significantly better than C2RCC (RMSE =
11.3 mg/L, NRMSE = 57.5%) and Sen2Cor (RMSE = 42.8 mg/L, NRMSE = 217%). This study shows that 1) high-resolution spatial and temporal variation of TSM concentration estimation can be made visible within the Brantas river basin, 2) an overview of TSM concentration estimation of the entire basin at one
moment in time can be achieved and visualised, 3) an extensive historical record of TSM concentration estimations can be accessed, and 4) information is provided to prioritize sampling locations and field surveying times.

Total Suspended Matter
Atmospheric Correction
Remote Sensing
WISP-3
Sentinel-2
multi-conditional algorithm,
Brantas river

http://resolver.tudelft.nl/uuid:770fd557-7054-450e-8edb-c61afa246e46

-7.300380, 112.740183

Student theses

master thesis

© 2020 J.C. Wiggins