GRACE and GRACE-FO Related Publications (no abstracts)

• Sorted by Date • Sorted by Last Name of First Author •

Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries

Li, Wenzhao, El-Askary, Hesham, Lakshmi, Venkat, Piechota, Thomas, and Struppa, Daniele, 2020. Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries. Remote Sensing, 12(9):1391, doi:10.3390/rs12091391.

Downloads

• from the NASA Astrophysics Data System  • by the DOI System  •

@ARTICLE{2020RemS...12.1391L,
       author = {{Li}, Wenzhao and {El-Askary}, Hesham and {Lakshmi}, Venkat and {Piechota}, Thomas and {Struppa}, Daniele},
        title = "{Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries}",
      journal = {Remote Sensing},
     keywords = {hydrology, impervious surface, Nile watershed, SDG, fully convolutional neural networks, Google Earth Engine, Google Cloud Platform, GRACE, CHIRPS, FLDAS, soil moisture, Landsat-8},
         year = 2020,
        month = apr,
       volume = {12},
       number = {9},
          eid = {1391},
        pages = {1391},
     abstract = "{In September 2015, the members of United Nations adopted the 2030 Agenda
        for Sustainable Development with universal applicability of 17
        Sustainable Development Goals (SDGs) and 169 targets. The SDGs
        are consequential for the development of the countries in the
        Nile watershed, which are affected by water scarcity and
        experiencing rapid urbanization associated with population
        growth. Earth Observation (EO) has become an important tool to
        monitor the progress and implementation of specific SDG targets
        through its wide accessibility and global coverage. In addition,
        the advancement of algorithms and tools deployed in cloud
        computing platforms provide an equal opportunity to use EO for
        developing countries with limited technological capacity. This
        study applies EO and cloud computing in support of the SDG 6
        ``clean water and sanitation'' and SDG 11 ``sustainable cities
        and communities'' in the seven Nile watershed countries through
        investigations of EO data related to indicators of water stress
        (Indicator 6.4.2) and urbanization and living conditions
        (Indicators 11.3.1 and 11.1.1), respectively. Multiple
        approaches including harmonic, time series and correlational
        analysis are used to assess and evaluate these indicators. In
        addition, a contemporary deep-learning classifier, fully
        convolution neural networks (FCNN), was trained to classify the
        percentage of impervious surface areas. The results show the
        spatial and temporal water recharge pattern among different
        regions in the Nile watershed, as well as the urbanization in
        selected cities of the region. It is noted that the classifier
        trained from the developed countries (i.e., the United States)
        is effective in identifying modern communities yet limited in
        monitoring rural and slum regions.}",
          doi = {10.3390/rs12091391},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020RemS...12.1391L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Generated by bib2html_grace.pl (written by Patrick Riley modified for this page by Volker Klemann) on Thu Apr 10, 2025 10:40:58

---

Thu Apr 10, F. Flechtner