Spatiotemporal Assessment of Surface Temperature Effects Chlorophyll-a as the Water Quality Indicators in Tallo River
Bambang Bakri
Suharman Hamzah
Abstract
Monitoring water quality is essential for sustaining ecological balance and public health, particularly in urban rivers facing increasing anthropogenic pressures. This study examines the relationship between surface temperature and chlorophyll-a concentrations in the Tallo River, Makassar, Indonesia, using multi-temporal Sentinel-2A imagery from 2019 to 2024. The Normalized Difference Water Index (NDWI) and Normalized Difference Chlorophyll Index (NDCI) were applied to assess seasonal and spatial dynamics. Results show clear seasonal variation: higher chlorophyll-a concentrations occurred during the rainy season due to algal proliferation, while levels decreased in the dry season as elevated temperatures limited growth. Spatially, chlorophyll-a ranged from 0.01–0.09 µg/L in the central river to 0.12–4.7 µg/L along the banks. Surface temperature trends indicated gradual warming, with significant increases observed in 2022 and 2024. Correlation analysis revealed stronger negative associations between rising temperatures and chlorophyll-a in recent years, suggesting thermal stress and nutrient limitations. The Extreme Gradient Boosting (XGBoost) algorithm produced stable and reliable predictions of chlorophyll-a, closely aligning with satellite observations. These findings highlight the effectiveness of combining remote sensing and machine learning for long-term water quality monitoring in urban river systems.
References
[1] Jeppesen, E., et al., Ecological impacts of global warming and water abstraction on lakes and reservoirs due to changes in water level and related changes in salinity. Hydrobiologia, 2015. 750(1): p. 201-227. https://doi.org/10.1007/s10750-014-2169-x
[2] Nieves, V., J.K. Willis, and W.C. Patzert, Recent hiatus caused by decadal shift in Indo-Pacific heating. Science, 2015. 349(6247): p. 532-535. https://doi.org/10.1126/science.aaa4521
[3] Yang, K., et al., Spatial and temporal variations in the relationship between lake water surface temperatures and water quality-A case study of Dianchi Lake. Science of the total environment, 2018. 624: p. 859-871. https://doi.org/10.1016/j.scitotenv.2017.12.119
[4] Dodds, W.K., et al., Eutrophication of US freshwaters: analysis of potential economic damages. 2009, ACS Publications. https://doi.org/10.1021/es801217q
[5] Kim, H.G., et al., Spatial patterning of chlorophyll a and water-quality measurements for determining environmental thresholds for local eutrophication in the Nakdong River basin. Environmental Pollution, 2021. 268: p. 115701. https://doi.org/10.1016/j.envpol.2020.115701
[6] Wu, H., et al., Plateau lake ecological response to environmental change during the last 60 years: A case study from freshwater Lake Yangzong, SW China. Journal of Soils and Sediments, 2021. 21(3): p. 1550-1562. https://doi.org/10.1007/s11368-021-02894-2
[7] Farid, H.U., et al., Assessing seasonal and long-term changes in groundwater quality due to over-abstraction using geostatistical techniques. Environmental Earth Sciences, 2019. 78(13): p. 386. https://doi.org/10.1007/s12665-019-8373-2
[8] ĐUROVIĆ, B., et al., Monitoring eutrofikacije korištenjem satelitske metode Landsat 8 u Bokokotorskom zaljevu. Acta Adriatica, 2018. 59(1): p. 17-34. https://doi.org/10.32582/aa.59.1.2
[9] Yang, H., et al., Sedimentation rates, nitrogen and phosphorus retentions in the largest urban Lake Donghu, China. Journal of radioanalytical and nuclear chemistry, 2005. 267(1): p. 205-208. https://doi.org/10.1007/s10967-006-0028-6
[10] Yang, X., et al., Temporal and spatial variations of Chlorophyll a concentration and eutrophication assessment (1987–2018) of Donghu Lake in Wuhan using Landsat images. Water, 2020. 12(8): p. 2192. https://doi.org/10.3390/w12082192
[11] Chen, J., et al., Using the multidimensional synthesis methods with non-parameter test, multiple time scales analysis to assess water quality trend and its characteristics over the past 25 years in the Fuxian Lake, China. Science of the Total Environment, 2019. 655: p. 242-254. https://doi.org/https://doi.org/10.1016/j.scitotenv.2018.11.144
[12] Chen, K., et al., Spatiotemporal changes in water quality parameters and the eutrophication in Lake Erhai of Southwest China. Water, 2022. 14(21): p. 3398. https://doi.org/10.3390/w14213398
[13] Liu, M., et al., Thermal stratification dynamics in a large and deep subtropical reservoir revealed by high-frequency buoy data. Science of the Total Environment, 2019. 651: p. 614-624. https://doi.org/10.1016/j.scitotenv.2018.09.215
[14] Syafjanuar, T., et al., Water Quality Accuracy Test from Sentinel-2 Imagery in The Pasee-Peusangan Watershed Area. 2023. https://doi.org/10.1016/j.scitotenv.2018.09.215
[15] Achmad, A. and M. Muftiadi. The relationship between land surface temperature and water index in the urban area of a tropical city. in IOP Conference Series: Earth and Environmental Science. 2019. IOP Publishing. https://doi.org/10.1088/1755-1315/365/1/012013
[16] Joshi, V., D. Haldar, and S. Dey, Integration of Sentinel-1 and Sentinel-2 for temporal identification of aquacultural ponds. Results in Earth Sciences, 2025: p. 100114. https://doi.org/10.1016/j.rines.2025.100114
[17] Liu, X., et al., Seasonal water storage of large reservoirs exacerbates eutrophication risk in the fluctuating backwater zone: A case study of Three Gorges Reservoir, China. Ecological Indicators, 2025. 171: p. 113100. https://doi.org/10.1016/j.ecolind.2025.113100
[18] Abdiyeva-Aliyeva, G., J. Aliyev, and U. Sadigov, Application of classification algorithms of Machine learning in cybersecurity. Procedia Computer Science, 2022. 215: p. 909-919. https://doi.org/10.1016/j.procs.2022.12.093
[19] Song, X., et al., XGBoost machine learning algorithm for predicting unplanned readmission in elderly patients with coronary heart disease. Geriatric Nursing, 2025. 66: p. 103609. https://doi.org/10.1016/j.gerinurse.2025.103609
[20] Wijaya, Y.F. and S.Y.J. Prasetyo, Model Penilaian Tata Guna Lahan Dengan Citra Landsat 8 OLI Menggunakan Algoritma XGBoost Diwilayah Beresiko Tsunami (Studi Kasus: Kota Palu Sulawesi Tengah). Indonesian Journal of Computing and Modeling, 2021. 4(1): p. 23-28. https://doi.org/10.24246/icm.v4i1.4981