Wetlands play a critical role in water retention and supply in drainage connected regions.The Usangu wetland ecosystem contributes to recharging the Ruaha River,which is hydrologically connected to the wetland,support...Wetlands play a critical role in water retention and supply in drainage connected regions.The Usangu wetland ecosystem contributes to recharging the Ruaha River,which is hydrologically connected to the wetland,supporting both ecolog-ical balance and agricultural activities in the region.This study analyzes Land Surface Temperature(LST)data from Landsat 8 and 9,employing machine learning techniques to explore temporal relationships with multiple variables,in-cluding the NDVI and the SPI.The SPI dataset,derived from NOAA PER-SIANN-CDR satellite images,was analysed from 2000 to 2024 using Google Earth Engine(GEE).The precipitation datasets clustered using the K-Means al-gorithm to identify SPI drought years.Timeseries charts and Seasonal Trend De-composition by LOESS(STL)statistical tests,conducted using CHIRPS data with 0.05˚resolution.Historical CMIP6 model precipitation datasets were bias cor-rected against the CHIRPS reference dataset using linear scaling,which revealed that the raw CMIP6 outputs consistently overestimated precipitation.The cor rected data shows severe dry spells in the wetland region with values frequently below 200 mm/month.The SPI analysis identifies drought years in the water shed,which align with periods of below average precipitation.Linear regression of LST data shows a strong positive correlation between the baseline temperature and predicted data,with a correlation coefficient(r)of 0.79.However,the corre-lation between LST and the Shuttle Radar Topography Mission Digital Elevation Model(SRTDEM)dataset reveals a negative relationship.This suggests that lower elevations in the wetland experience higher temperatures.LST influences various spectral indices in the wetland.The Water in Wetland(WIW)method detects water pixel through two spectral threshold approaches applied to NIR and SWIR2 bands.NDVI trends from 2019 to 2023,show higher greenness NDVI up to 0.5 in the wetland compared to the surrounding area.These varia-tions are influenced by seasonal harvesting,drought years,and the warming trend.This study is crucial for water management in the Usangu wetland,which serves as a vital source and watershed for the Ruaha River,supporting both eco logical and agricultural sustainability in the region.展开更多
文摘Wetlands play a critical role in water retention and supply in drainage connected regions.The Usangu wetland ecosystem contributes to recharging the Ruaha River,which is hydrologically connected to the wetland,supporting both ecolog-ical balance and agricultural activities in the region.This study analyzes Land Surface Temperature(LST)data from Landsat 8 and 9,employing machine learning techniques to explore temporal relationships with multiple variables,in-cluding the NDVI and the SPI.The SPI dataset,derived from NOAA PER-SIANN-CDR satellite images,was analysed from 2000 to 2024 using Google Earth Engine(GEE).The precipitation datasets clustered using the K-Means al-gorithm to identify SPI drought years.Timeseries charts and Seasonal Trend De-composition by LOESS(STL)statistical tests,conducted using CHIRPS data with 0.05˚resolution.Historical CMIP6 model precipitation datasets were bias cor-rected against the CHIRPS reference dataset using linear scaling,which revealed that the raw CMIP6 outputs consistently overestimated precipitation.The cor rected data shows severe dry spells in the wetland region with values frequently below 200 mm/month.The SPI analysis identifies drought years in the water shed,which align with periods of below average precipitation.Linear regression of LST data shows a strong positive correlation between the baseline temperature and predicted data,with a correlation coefficient(r)of 0.79.However,the corre-lation between LST and the Shuttle Radar Topography Mission Digital Elevation Model(SRTDEM)dataset reveals a negative relationship.This suggests that lower elevations in the wetland experience higher temperatures.LST influences various spectral indices in the wetland.The Water in Wetland(WIW)method detects water pixel through two spectral threshold approaches applied to NIR and SWIR2 bands.NDVI trends from 2019 to 2023,show higher greenness NDVI up to 0.5 in the wetland compared to the surrounding area.These varia-tions are influenced by seasonal harvesting,drought years,and the warming trend.This study is crucial for water management in the Usangu wetland,which serves as a vital source and watershed for the Ruaha River,supporting both eco logical and agricultural sustainability in the region.
