TY - JOUR
T1 - Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning
AU - Steinbach, S.
AU - Bartels, Anna
AU - Rienow, Andreas
AU - Thiong'o Kuria, Bartholomew
AU - Zwart, Sander
AU - Nelson, A.
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/2
Y1 - 2025/2
N2 - Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). Our results suggest that short- and longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management.
AB - Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). Our results suggest that short- and longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management.
KW - Agricultural water management
KW - Sentinel-2
KW - Turbidity
KW - Water quality
KW - UT-Gold-D
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-GOLD
U2 - 10.1016/j.jag.2025.104390
DO - 10.1016/j.jag.2025.104390
M3 - Article
AN - SCOPUS:85216945660
SN - 1569-8432
VL - 136
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
M1 - 104390
ER -