Tropical lakes such as Lake Sentarum in Kalimantan,Indonesia,represent ecologically rich ecosystems with high biodiversity and constitute the largest lake on the island of Kalimantan.This lake serves as a sensitive in...Tropical lakes such as Lake Sentarum in Kalimantan,Indonesia,represent ecologically rich ecosystems with high biodiversity and constitute the largest lake on the island of Kalimantan.This lake serves as a sensitive indicator of climate change;however,its monitoring is often hindered by persistent cloud cover.This study evaluates the effectiveness of a Gradient Tree Boosting machine learning model integrated with multisource satellite data,including optical imagery,Sentinel-1 SAR,Sentinel-2,and high resolution NICFI data,in accurately mapping surface water dynamics.The Gradient Tree Boosting model was trained and validated using water and non water samples collected from annual imagery spanning 2019 to 2024,achieving validation accuracies ranging from 80 percent to 97 percent.Results demonstrate that Gradient Tree Boosting successfully integrates the strengths of each sensor,producing consistent annual water maps despite extreme hydrological fluctuations caused by El Nino and La Nina events.These findings highlight the model’s potential application in water resource man-agement,particularly in providing accurate baseline data to support adaptation planning for droughts and floods in climate vulnerable regions.展开更多
文摘Tropical lakes such as Lake Sentarum in Kalimantan,Indonesia,represent ecologically rich ecosystems with high biodiversity and constitute the largest lake on the island of Kalimantan.This lake serves as a sensitive indicator of climate change;however,its monitoring is often hindered by persistent cloud cover.This study evaluates the effectiveness of a Gradient Tree Boosting machine learning model integrated with multisource satellite data,including optical imagery,Sentinel-1 SAR,Sentinel-2,and high resolution NICFI data,in accurately mapping surface water dynamics.The Gradient Tree Boosting model was trained and validated using water and non water samples collected from annual imagery spanning 2019 to 2024,achieving validation accuracies ranging from 80 percent to 97 percent.Results demonstrate that Gradient Tree Boosting successfully integrates the strengths of each sensor,producing consistent annual water maps despite extreme hydrological fluctuations caused by El Nino and La Nina events.These findings highlight the model’s potential application in water resource man-agement,particularly in providing accurate baseline data to support adaptation planning for droughts and floods in climate vulnerable regions.
