substratum types (SAMBUCA). 2. Water quality. ⢠Water column optically active components. Fig 5: Modeling approach using time-series RS scenes. Reseach ...
Water detection and water quality tracking for regions of Vietnam: Applications of VNSC Data Cube Nguyen Hong Quang, Pham Thi Thanh Nga, Le Thi Thu Hang and Loc Thi Thuy Linh Vietnam National Space Center (VNSC), Vietnam Academy of Science and Technology (VAST)
Motivation
Methodology
Water challenges have become increasingly apparent due to an increasing demand in fresh water and the shortage of the supply in general. Under the human-made intervention (flood control etc.) and climate change, the water contributions are altered from region to region. Vietnam is one of the nations affected most by climate change impacts such as the rise of the sea level, drought and salinization. Hence, this study focuses on the spatial time-series presentation of inland water and its quality.
1. Water detection
Fig. 1: Water challenges in Vietnam
Reseach objectives • To monitor the presentation of inland water over Vietnam • To examine the timely quality of the inland water using remote sensing datasets • To compare for other future applications of the VNSC Data Cube.
Fig 4: Remote sensing in complex water 1. Atmospheric correction and air-water interface effects removal (RT physics-based and increasingly relying on ANN for fast processing) 2. For optically deep waters: adaptive linear matrix inversion method (aLMI) using variable sets of SIOPS (Specific Inherent Optical Properties) to allow for varying water types within one image 3. For optically shallow waters: enhancement of enhanced implementation of the inversion/optimization approach by Lee et al. (1999, 2001) by including multiple substratum types (SAMBUCA) 2. Water quality • Relevant bio-optical data is required to parameterize and validate the products Algal bloom detecting in inland waters e.g.
• Driven by color information in spectral reflectance
Study area Fig 2: Initial selected area of VNSC Data Cube
• Water column optically active components Our solution: physics-based modelling; SAMBUCA
Fig 5: Modeling approach using time-series RS scenes
Results The VNSC Data Cube
1. Water detection
2. Water quality Landsat 8 - SAMBUCA outputs Fig 3: VNSC Data Cube- System diagram, System photo, Data, working scheme and interface
Enhanced True color (10/3/2016)
CDOM: Coloured Disolved organic particulates NAP: Non Algal particulates Chl: Chlorophyll SDI: Silt Density Index
Further information & contact:
KD: rate of decay Substrate Fraction – Sand – subb1_frac_norm
Summary - VNSC Data cube is promising for remote sensing-based applications providing end-user products. - Water detection and quality examination have been successfully pilot for some areas in Vietnam but we still need more calibrations and validations.
