Satellite-derived shallow water depths estimation using remote sensing and artificial intelligence models, a case study: Darbandikhan Lake Upper, Kurdistan Region, Iraq

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-01-01 DOI:10.1016/j.rsase.2024.101432

Arsalan Ahmed Othman , Salahalddin S. Ali , Ahmed K. Obaid , Sarkawt G. Salar , Omeed Al-Kakey , Younus I. Al-Saady , Sarmad Dashti Latif , Veraldo Liesenberg , Silvio Luís Rafaeli Neto , Fabio Marcelo Breunig , Syed E. Hasan

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引用次数: 0

Abstract

Bathymetric mapping provides valuable information for the estimation of the depth and volume of enclosed inland water bodies that are useful in the planning and management of water resources. The use of conventional methods for the detection of shallow water depth, specifically in flooded areas, has been challenging. However, advances in remote sensing technology combined with artificial intelligence (AI) offer a reliable method. This study presents a reliable method to estimate water depth, using the Darbandikhan Lake Upper (DLU) as a test site. The novelty of this work lies in using a combination of Quantile Regression Forests (QRF), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) approaches together with the reflectance of Sentinel-2 and the ICESat-2 LiDAR data to estimate the depth of the water in the DLU during the 2019 spring flood. Our results gave the coefficient of determination (R²) and root mean square error (RMSE) between the actual depth obtained from the ICESat-2 and the estimated depth from the applied artificial intelligence models of 0.984, 0.983, 0.868, and 0.809; and 0.545, 0.569, 1.618, and 2.143 for the QRF, RF, SVM, and ANN models, respectively. This study, which applied the QRF model for the first time to determine the satellite-derived water depths, produced the most accurate result, with the maximum and mean estimated depth of DLU being 19.93 and 6.29 m, respectively. This study shows that the most sensitive bands to estimate the bathymetry are Band 9 (940 nm), Band 3 (560 nm), and Band 5 (705 nm) of the Sentinel-2, while the less sensitive bands are Band 2 (490 nm) and Band 11 (1610 nm). We argue that this technique can be applied to estimate the depth of shallow water bodies using passive satellite imageries in other regions of the world regardless of the full coverage availability of ICESat-2.

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems