Soil moisture (SM) in the profile is the main reservoir of water available for vegetation. Therefore, monitoring SM during dry-down periods is crucial for understanding vegetation water status, among other applications. Datasets derived from Land Surface Models, such as the ERA5-Land dataset, provide SM estimates at different depths. The aim of this study was to evaluate the ability of ERA5-Land SM data to detect dry-down periods and to test whether its drying time scale aligns with field measurements at three sites in the Argentine Pampas. The analysis was carried out across the three standard soil layers used by ERA5-Land: layer 1 (L1, 0-7 cm), layer 2 (L2, 7-28 cm), and layer 3 (L3, 28-100 cm). First, the evaluation of the ERA5-Land SM data showed a moderate agreement with field data, although it exhibited a high overestimation (bias > |0.09| m3/m3) in the SM estimates. On the other hand, dry-down periods analysis indicated that ERA5-Land SM data was able to detect a similar number of dry-down periods and drying time scales as observed in the field for L1 and L2. In contrast, at L3, both the number of detected periods and the estimated drying time scale were lower. ERA5-Land SM data showed a consistent and expected pattern of faster drying in the shallower layers, demonstrating its potential for monitoring SM dynamics within the profile.

Soil moisture (SM) is a key parameter in micrometeorology, hydrology, and agriculture to estimate energy and moisture fluxes at the land surface and the water within the soil available for agricultural purposes. Observations at L-band (1.2–1.4 GHz) are desirable to estimate SM due to their larger penetration depth. Currently, missions such as the NASA SMAP and the ESA SMOS provide periodically global coverage of brightness temperature (TB) information at L-band. Current satellite SM retrievals from TB observations are supplied at different spatial resolutions and their validation using ground measurements is challenging due to differences in the representativeness between satellite scales and site SM values. Because of the capability of satellites to cover extent areas, their SM products have been a valuable tool to monitor atypical drought conditions due to climate change. Satellite SM retrievals have been challenging in agricultural regions due to high variability of SM and phenological changes in vegetation. Particularly, agricultural areas in arid and semi-arid do not have reliable databases to calibrate SM retrieval algorithms. In this work, we conducted a field experiment and created an SM database that can be used. The database includes data from agricultural areas located in the Central Valleys of Oaxaca, Southern Mexico, during 2021 to 2024. In this study, we aim to compare different existing L-band passive satellite SM products with in-situ measurements from 2021 to 2024 covering different growing seasons in the semi-arid agricultural region. The results show that the products from the SMAP mission provide closer SM estimates to in-situ measurements at 9 and 36 km compared to SM products from other missions.

In this work, we present a time-constrained approach for the estimation of soil dielectric permittivity ($\varepsilon$) in agricultural fields in central Argentina. This method combines a two-layer scattering model (based on the Fresnel approximation, which accounts for vegetation attenuation and surface roughness), in the context of a Bayesian inference scheme. To constrain the inference process, we introduce a semi-empirical prior distribution for $\varepsilon$ on day $t+1$, based on its value on day $t$, derived from in situ $\varepsilon$ data. This distribution automatically accounts for the asymmetrical dynamics of soil permittivity. Validation against measured soil $\varepsilon$ in the study areas demonstrates the potential of the proposed method.

Understanding of debris-covered glaciers is crucial for predicting melt dynamics in high-mountain regions. Juncal Norte Glacier, located on the northwestern slope of Nevado Juncal in central Chile, has experienced a marked retreat—losing 22% of its surface area between 1955 and 2022. As it recedes, its lower tongue has become increasingly covered by supraglacial debris, a process that appears to have accelerated in recent years. This study proposes a spectral index (SI) derived from UAV-based multispectral imagery to classify debris thickness and its spatial distribution at Juncal Norte Glacier tongue. Data was collected during three UAV flights in February 2025 using high-resolution sensors. The classification relies on the green, red, red edge, and near-infrared (NIR) bands, enabling detailed surface mapping of the glacier’s ablation zone, with a spatial resolution of 11 cm/pixel. Preliminary results indicate that the method captures fine-scale debris heterogeneity and identifies zones of bare ice and varying debris thickness. Future work will focus on refining the spectral approach and integrating the results into geodetic mass balance estimates to improve glacier change assessments.