Conference Agenda

This study proposes a multi-source methodology for monitoring bathymetry in continental water bodies in central Mexico by integrating in-situ and satellite-based techniques. A 3D-printed Unmanned Surface Vehicle (USV), equipped with echo sounders and GPS, was used to collect high-resolution depth data from five dams: Cointzio and Querendaro (Morelia, Michoacán) and Mata, Soledad, and Esperanza (Guanajuato). This data was correlated with Sentinel-2 imagery accessed through the Microsoft Planetary Computer, which provided multispectral, spatio-temporal data across different seasons. To assess water body delineation accuracy, several water indices were compared, including the Normalized Difference Water Index (NDWI), Automated Water Extraction Index with shadows (AWEI_sh), without shadows (AWEI_nsh), Modified NDWI (MNDWI), Sentinel Multi-Band Water Index (SMBWI), and Sentinel-2 Water Index (SWI), along with the Scene Classification Layer (SCL). SWI consistently yielded the most reliable contours. Although the SCL layer struggled in areas with dense aquatic vegetation, misclassifying water surfaces, it proved useful when combined with SWI. This integration produced the most accurate results for most dams—except in Querendaro, where the combined method overestimated water extent. A strong correlation between USV data and satellite-derived contours confirms that combining in-situ and remote sensing sources offers a robust and precise framework for bathymetric mapping in inland waters of Mexico.

Soil moisture is a key variable in the global water cycle, carbon balance and energy conversion, and is crucial for hydrological control, meteorological forecasting and crop growth. Pengyang County in Ningxia is a typical region with fragile ecology. In this paper, we utilize Sentinel 1 SAR data and Landsat 8 optical imagery to synergistically invert soil moisture in Pengyang County by combining the advantages of optical and microwave remote sensing. The study calculates the vegetation water content through the VWC model, and uses the water cloud model to eliminate the influence of vegetation on the radar signal to obtain the soil backscattering coefficient with the removal of the influence of vegetation. Finally, the BP neural network model was utilized to invert the soil moisture in Pengyang County. The results show that the VH-polarized SAR data are more sensitive to the vegetation structure and moisture content, which is more suitable for soil moisture inversion in this region, and the NDMI has the highest sensitivity to the vegetation moisture content, which contributes more to the soil moisture estimation. The inversion results of the BP neural network model have a high correlation with the measured values, which indicates that the method can effectively invert the soil moisture in Pangyang County. The results of the study can provide a reference for soil moisture monitoring in the region, and provide a basis for decision-making in ecological protection, water conservation and comprehensive regional management.

Quickflow (QF) is the fraction of rainfall that rapidly runs off to channels, a key element for understanding floods and designing control measures. It reflects rapid runoff pathways that drive peak flows, sediment pulses, water-quality downstream impacts, and hazards. QF is influenced by the precipitation regime (seasonality and event concentration) and by surface properties represented by land use/land cover (LULC) and soil types, which modulate infiltration and storage. We applied the InVEST–Seasonal Water Yield (SWY) model at 30 m resolution in the San Fernando–Soto la Marina basin. We used monthly climate, 2020 LULC, and soil type data, and analyzed (i) the basin-wide distribution of QF, (ii) the spatial influence of LULC- and soil-based on QF, and (iii) the precipitation–QF relationship. Results show a multimodal distribution of QF ratios; urban areas, bare soils, and low-cover croplands yield higher QF than woody covers; and the precipitation–QF relationship is positive but dispersed, modulated by the sequence and intensity of events and by surface conditions. We conclude that integrated land and water management should (a) focus on targeted interventions in high-QF zones that maintain or improve vegetation cover and promote infiltration to reduce flooding and erosion and promote increased dry season flows and (b) integrate improved temporal and spatial representation of precipitation events with realistic LULC and soil parameterizations for more accurate model outputs and reliable planning.