EGU24-18504, updated on 11 Mar 2024
https://doi.org/10.5194/egusphere-egu24-18504
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Google’s cloud computing platform performance assessment of spatio-temporal and estimation for global water quality analysis and surface water mapping using integrated satellite data of MODIS (TERRA), Landsat- (8, 9), Sentinel‑2A/B and Planetscope

Abhinav Galodha1, Sanya Anees2, Brejesh Lall3, and Shaikh Ziauddin Ahammad4
Abhinav Galodha et al.
  • 1School of Interdisciplinary Research (SIRe), Indian Institute of Technology IIT Delhi, India (srz208737@sire.iitd.ac.in)
  • 2Department of Electronics and Communication Engineering, Netaji Subhas University of Technology (NSUT), Delhi, India (sanya.anees@nsut.ac.in)
  • 3Department of Electrical Engineering, Indian Institute of Technology Delhi, IIT Delhi, India (brejesh@ee.iitd.ac.in)
  • 4Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, IIT Delhi, India (zia@iitd.ac.in)

India is ranked 120th among 122 countries globally in WaterAid’s water quality index. Regular water quality monitoring is essential to determine which inland water bodies are experiencing depreciating water quality. Long-term trends have been obtained using satellite remote sensing, necessitating multiple image analysis. The computational burden of processing numerous satellite images can be reduced using Google Earth Engine’s (GEE) cloud computing capabilities. Thorough research was conducted to determine the global spatio-temporal and biochemical factors impacting surface water quality (WQ). The public availability of geospatial datasets and free access to cloud-based geo-computing platforms such as GEE are widely used for spatio-temporal mapping, global surface water monitoring, water quality parametric variation, and real-time forecasting. Many researchers have recently focused on improving data mining and machine learning algorithms to accurately deal with image classification and predictive problems for crop identification and monitoring. In the present study, we propose to define band spectral ratios, spectral band equations, and empirical models for water quality parameters. A few neural network models will be used to (i) query and pre-process satellite earth observations that coincide with the study area, (ii) extract the spectra, and (iii) use spectral band wavelength charts, time-series charts, spatial-distribution maps, and the development of an online dashboard application to visualize the results graphically upon using integrated Landsat (8,9), Sentinel-2A/B and PlanetScope satellite data for the pre-monsoon and post-monsoon seasons in 2023 in the pan-India region. MODIS-TERRA provides LST spatial-temporal monitoring. In this, we have assessed and compared the performance of CART, SVM, and RF ML algorithms. We found that RF outperforms CART and SVM algorithms in the GEE platform with PlanetScope data (80.71% overall accuracy (OA) with Kappa 0.89) and also with the integration of PlanetScope and Sentinel-2A/B data (OA = 85.53%, Kappa 0.91). But CART outperforms RF and SVM algorithms with Sentiel-2A/B data (OA = 81.59%, Kappa 0.85). The SAM technique, spectral feature fitting, continuum band removal, and other band-spectral ratio techniques are employed for quantitative hyperspectral data analysis. Specifically, the performance metrics of XGBoost and SGD for both Chl-a (R^2 = 0.818) and Turbidity (R^2 = 0.815) models exhibited robust accuracy. We have also developed a Google API-based JavaScript code that can be tested under complex coastal shores, challenging inundation and variable climatic conditions. Our method provides the end-to-end cloud computing workflow shown in this research, considering cost and computational efficiency for timely information delivery.

Keywords: Classification and Regression Trees (CART), Chlorophyll-a (conc), Color dissolved organic matter (CDOM), Composite water management index (CWMI), Environmental Mapping and Analysis Program (EnMAP), Land Surface Temperature (LST), Machine Learning (ML), Random Forest (RF), Spectral Angle Mapper (SAM), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Total Suspended Solids (TSS), Water Quality (WQ), XGBoost (Extreme Gradient Boosting).

How to cite: Galodha, A., Anees, S., Lall, B., and Ahammad, S. Z.: Google’s cloud computing platform performance assessment of spatio-temporal and estimation for global water quality analysis and surface water mapping using integrated satellite data of MODIS (TERRA), Landsat- (8, 9), Sentinel‑2A/B and Planets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18504, https://doi.org/10.5194/egusphere-egu24-18504, 2024.

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