Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation

Snow parameters modeling using remote sensing techniques and HEC-HMS hydrological modeling-case study: Kan Basin

Floods occur yearly, with great property and human life damage worldwide due to severe climate changes. Snow cover dominates the mountainous areas in winter. Therefore, the river discharge increases significantly in spring, when the snow melts gradually and is accompanied by rain this season. This study aims to evaluate the snow parameters such as snow cover, monthly average snow cover, and snowmelt from early winter to late summer 2020 by using the Terra satellite, MODIS sensor, and the FLDAS model to estimate the amount of water equivalent to snowmelt in the Google Earth Engine system for Kan basin in Tehran province. The hydrological model HEC-HMS was used for assessing the effect of snow parameters on the amount of Kan River discharge in this study. The land use map was extracted by using the image of the Sentinel-2 satellite to acquire more accuracy in this study. Finally, Sentinel-1 radar images were used to evaluate the effect of flood on the area and monitor the changes.

Tracing Real-Time Transnational Hydrologic Sensitivity and Crop Irrigation in the Upper Rhine Area over the Exceptional Drought Episode 2018–2020 Using Open Source Sentinel-2 Data

Climate and regional land-use and landcover change (LUCC) impact the ecosystem of the Upper Rhine Area (URA) and transform large parts of the landscape into strongly irrigated agricultural cropland. The increase of long-term drought periods and the trend towards low summer precipitation totals trigger an increase in groundwater scarcity and amplify the negative effects of extensive irrigation purposes and freshwater consumption in a hydrologically sensitive region in Central Europe. This article presents qualitative transnational open source remote sensing temporal series of vegetation indices (NDVI) and groundwater level development to tracing near real-time vegetation change and socio-ecological feedbacks during periods of climate extremes in the Upper Rhine Area (2018–2020). Increased freshwater consumption caused a dramatic drop in groundwater availability, which eventually led to a strong degradation of the vegetation canopy and caused governmental regulations in July 2020. Assessing vegetation growth behavior and linking groundwater reactions in the URA through open source satellite data contributes to a rapidly accessible understanding of the ecosystem’s feedbacks on the local to the transnational scale and further enables risk management and eco-political regulations in current and future decision-making processes.