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<p>This study provides a long-term assessment of remote sensing-based water indices (NDWI, MNDWI, ANDWI, AWEIsh, and WI2015) for delineating surface water bodies in the semi-arid region of Northeast Brazil under both wet and dry conditions, using 38 Landsat satellite images from 2006 to 2022. Validation compared the indices with manually vectorised maps, using statistical metrics (R², RMSE, and MAE) and spatial metrics (commission and omission). The influence of turbidity and chlorophyll a was examined using the Normalised Difference Turbidity Index (NDTI) and the ratio of NIR/Red bands (β) as proxies, respectively. Results showed that AWEIsh and MNDWI exhibited the highest agreement, particularly in larger reservoirs during the wet season. Chlorophyll a was found to reduce index accuracy, notably in smaller reservoirs during dry periods, whereas turbidity’s effect was seasonally dependent. Overall, the findings underscore the importance of seasonally adaptive methods and integrating water quality indicators to enhance surface water mapping in semi-arid areas. Finally, this research offers a comparative overview of the performance and utility of various water indices for semi-arid reservoir mapping, highlighting their strengths and limitations.</p> Graphical Abstract <p></p>

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A Ride in Semi-arid: Evaluation of Remote Sensing-based Indices for the Detection and Monitoring of Surface Water Bodies Under Dry and Wet Periods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Eduardo Gonçalves Patriota,
  • Guillaume Francis Bertrand,
  • Alexandro Medeiros Silva,
  • Natalia Maria Mendes da Silva,
  • Khalil Grisi Velôso Mendes,
  • José Welton Gonçalo de Sousa,
  • Richarde Marques da Silva,
  • Cristiano das Neves Almeida,
  • Cinthia Maria de Abreu Claudino,
  • Carolyne Wanessa Lins de Andrade Farias,
  • Jorge Flávio C. B. C. Silva,
  • Victor Hugo Rabelo Coelho

摘要

This study provides a long-term assessment of remote sensing-based water indices (NDWI, MNDWI, ANDWI, AWEIsh, and WI2015) for delineating surface water bodies in the semi-arid region of Northeast Brazil under both wet and dry conditions, using 38 Landsat satellite images from 2006 to 2022. Validation compared the indices with manually vectorised maps, using statistical metrics (R², RMSE, and MAE) and spatial metrics (commission and omission). The influence of turbidity and chlorophyll a was examined using the Normalised Difference Turbidity Index (NDTI) and the ratio of NIR/Red bands (β) as proxies, respectively. Results showed that AWEIsh and MNDWI exhibited the highest agreement, particularly in larger reservoirs during the wet season. Chlorophyll a was found to reduce index accuracy, notably in smaller reservoirs during dry periods, whereas turbidity’s effect was seasonally dependent. Overall, the findings underscore the importance of seasonally adaptive methods and integrating water quality indicators to enhance surface water mapping in semi-arid areas. Finally, this research offers a comparative overview of the performance and utility of various water indices for semi-arid reservoir mapping, highlighting their strengths and limitations.

Graphical Abstract