Conference paper Open Access

Improving water bodies detection from Sentinel-1 in South Africa using drainage and terrain data

Cherif, Ines; Ovakoglou, Georgios; Alexandridis, Thomas K.; Kganyago, Mahlatse; Mashiyi, Nosiseko


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    "title": "Improving water bodies detection from Sentinel-1 in South Africa using drainage and terrain data", 
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        "title": "Enhancing Food Security in AFRIcan AgriCULTUral Systems with the Support of REmote Sensing", 
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    "keywords": [
      "Remote sensing", 
      "water bodies", 
      "SAR", 
      "SRTM", 
      "HAND", 
      "Otsu valley-emphasis", 
      "Radiometric terrain correction"
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    "publication_date": "2021-09-12", 
    "creators": [
      {
        "affiliation": "Lab of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece", 
        "name": "Cherif, Ines"
      }, 
      {
        "affiliation": "Lab of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece", 
        "name": "Ovakoglou, Georgios"
      }, 
      {
        "affiliation": "Lab of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece", 
        "name": "Alexandridis, Thomas K."
      }, 
      {
        "affiliation": "Earth Observation, South African National Space Agency, Pretoria, South Africa", 
        "name": "Kganyago, Mahlatse"
      }, 
      {
        "affiliation": "Earth Observation, South African National Space Agency, Pretoria, South Africa", 
        "name": "Mashiyi, Nosiseko"
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    "meeting": {
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      "dates": "13-17 September 2021", 
      "place": "Online event", 
      "title": "Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII"
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    "description": "<p>In areas with extensive, nomadic, or transhumant livestock farming, it is important to access regular information on the location of ephemeral surface water bodies. SAR satellite images are used for water bodies mapping at high-resolution. Unlike optical data their use is not restricted to cloud-free conditions. Nevertheless, surface roughness, hill shadows, and presence of vegetation are known to affect the SAR backscatter and lead to false positives while detecting water. In this study, surface water was automatically mapped in hilly landscapes using Sentinel-1 images and the Otsu Valley Emphasis method to detect a threshold for water in the histogram of backscatter. In order to reduce the false positive rate in the steep areas, five different water masks using terrain and drainage information with different thresholds are compared in the mountainous province of KwaZulu-Natal (KZN) in South-Africa. The quantitative assessment shows that the overall accuracy ranged between 0.865 and 0.958 with the highest value obtained with a threshold of Height Above the Nearest Drainage index of 10m, leading to the lowest specificity of 0.037. The visual inspection over two reservoirs (Midmar Dam and Wagendrift Dam) shows that there is high agreement between the produced map and the reference data despite differences in their spatial and temporal coverage. Besides, radiometrically terrain corrected SAR data, which could be advantageous in such landscapes were recently made available by the ASF vertex platform. Even though they are not available in NRT, the potential of using such data for water detection is investigated.</p>"
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