Accurate knowledge of soil moisture (SM) is crucial in hydrological, micrometeorological, and agricultural applications; however, the SM estimation is particularly challenging in agricultural regions due to high spatial variability and dynamic vegetation conditions. The need for information about SM conditions is even more evident in developing countries with limited monitoring infrastructure. Satellite SM products are a useful tool as a proxy for SM conditions on the ground, but they need to be evaluated for specific regions. In this study, we assess the quality of the soil moisture active passive (SMAP) SM retrievals at 36, 9, and 3 km in an agricultural region in Central Mexico using in situ measurements during the Terrestrial Hydrology Experiments in Mexico 2018 and 2019. In addition, we provide insights into soil and vegetation parameters in the retrieval algorithms compared to those observed in the region. It was found that the SM spatial variability at the SMAP pixel grids was well represented by upscaled in situ SM measurements (SM $_{\text{up}}$ ) from five monitoring stations using the soil-weighted averaging and the Voronoï diagrams. Overall, the SMAP SM retrievals are highly correlated with SM $_{\text{up}}$ at all scales, but they estimated wetter conditions and the average root-mean-square difference (RMSD) $>$ 0.045 m $^{3}$ /m $^{3}$ . The lowest RMSD was obtained for the SM product at 36 km, while the highest RMSD was found for the SM product at 3 km. In addition, the single-channel algorithm using H-polarization provided the lowest RMSD for the products at 36 and 9 km. The main sources of uncertainty in the region may arise from the higher clay fraction used in the SMAP retrieval algorithm, by 13% compared to that observed, and a nonrepresentative characterization of land cover heterogeneity for vegetation water content estimation. The incorporation of in situ values into an SM retrieval algorithm resulted in differences $< $ 0.04 m $^{3}$ /m $^{3}$ between SM estimates and in situ SM for the complete growing season. Particularly, the use of in situ information helped in improving SM estimation when optimizing V- and dual-polarization brightness temperature observations.

The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide estimates of soil moisture (SM) at similar spatial resolutions using L-band brightness temperatures ( $T_{\mathrm{B}}$). These missions meet the requirement of SM retrievals with an unbiased root-mean-square difference (ubRMSD) $<$ 0.04 (m ${ }^3$ m⁻³) compared to in-situ measurements over most of the ecosystems; however, their SM estimates over forests present an ubRMSD $>$ 0.10 (m ${ }^3$ m⁻³). In this paper, we compared the SM retrievals from the SMOS and SMAP SM products with in-situ SM over a tropical forest in Southern Mexico. The L-band passive SM retrievals were evaluated in terms of four statistical metrics: root-mean-square difference (RMSD), bias, ubRMSD, and correlation coefficient ( $r$). In-situ measurements of SM, soil and vegetation temperatures, precipitation, soil surface roughness, tree heights, diameters at the breast height of trunks, and forest cover fraction were collected during a field campaign from 6 January to 14 June 2015 in the Biosphere Reserve of Calakmul, Mexico, covering two areas of about 40 km $\times$ 40 km each. The comparison between SM retrievals from SMOS and SMAP and in-situ SM showed an RMSD ranging from 0.107 to 0.322 (m ${ }^3$ m⁻³) and an ubRMSD from 0.049 to 0.128 (m ${ }^3$ m⁻³). Overall, the SMAP SM estimates showed higher values of $r$ and were closer to in-situ SM. Because the SMAP and SMOS radiometers performed very similar, these differences are due to the values assigned to the vegetation optical depth ( $\tau$), the scattering albedo ( $\omega$), and the representation of the dynamics in vegetation and soil temperatures in the SMOS and SMAP retrieval algorithm. Based on an optimization process, we estimated simultaneously optimal $\omega$ and $\tau$ values for the tropical forest by using $T_{\mathrm{B}}$ observations from SMAP and SMOS radiometers. The optimal value of $\omega$ was 0.0655 for the tropical forest, and constant over the study period. In contrast, the optimal values of $\tau$ showed to be variant on time and ranging between 1.0 and 1.7, with an averaged value of 1.4 and a standard deviation of 0.24. When applying the optimal values of $\omega$ and $\tau$ and in-situ soil and vegetation temperatures, the SM retrievals showed an ubRMSD of 0.035–0.070 (m ${ }^3$ m ${ }^{-3}$), improving the SM retrievals about 45%. A sensitivity analysis was conducted to evaluate the effect of the uncertainties in $\tau$, $\omega$, and soil and vegetation temperatures on the estimates of $T_{\mathrm{B}}$. It was found that vegetation temperature ( $T_{\mathrm{v}\mathrm{e}\mathrm{g}}$) was the most sensitive parameter, with $r$ higher than 0.70 for both polarizations in $T_{\mathrm{B}}$. When comparing in-situ $T_{\mathrm{v}\mathrm{e}\mathrm{g}}$ and surface temperature values used in the SMAP and SMOS SM retrieval algorithms, differences up to 10 K were observed, affecting the SM estimates. The results presented in this paper could be useful in the preparation of the SMAP Calibration/Validation Experiment 2019, aiming at improving SM retrievals over forests.

A volcanic eruption can produce large ash clouds in the atmosphere around a volcano, affecting commercial aviation use of the airspace around the volcano. Encountering these ash clouds can cause severe damage to different parts of the aircraft, mainly the engines. This work seeks to contribute to the development of methods for observing the dispersion of volcanic ash and to complement computational methods that are currently used for the prediction of ash dispersion. The method presented here is based on the frequency of occurrence of the regions of airspace areas affected by ash emission during a volcanic eruption. Popocatepetl volcano, 60 km east of Mexico City is taken as a case study. A temporal wind analysis was carried out at different atmospheric levels, to identify the direction towards which the wind disperses ash at different times of the year. This information showed two different trends, related to seasons in the direction of dispersion: the first from November to May and the second from July to September. To identify the ash cloud and estimate its area, a set of 920 MODIS images that recorded Popocatepetl volcanic activity between 2000 and 2021 was used. These satellite images were subjected to a semi-automatic, digital pre-processing of binarization by thresholds, according to the level of the brightness temperature difference between band 31 (11 µm) and band 32 (12 µm), followed by manual evaluation of each binarized image. With the information obtained by the processing of the MODIS image, an information table was built with the geographical position of each pixel characterized by the presence of ash for each event. With these data, the areas around Popocatepetl volcano with the highest frequency of affectation by ash emissions were identified during the period analyzed. This study seeks to complement the results obtained by numerical models that make forecasts of ash dispersions and that are very important for the prevention of air navigation risks.

El monitorieo volcánico utilzado en imágenes de satélite constituye un método económico y rutinario. Las mediciones obtenidas con este método permiten obtener información periódica sobre las fluctuaciones de las emisiones de gases tales como el SO2, que es relacionado con los procesos internos del volcán. En este estudio se analiza un periódo de 36 días (10 de mayo al 15 de junio de 2005), donde se analizaron 113 imágenes MODIS para la detección de SO2. En este periódo también fue posible detectar y cuantificar 8 de 15 eventos explosivos reportados por la VAAC de Washington. Tomando a las imágenes de satélite como herramienta para el monitoreo de emisiones volcánicas, fue posible estudiar tres casos relacionados con las cenizas volcánicas: 1) seguimiento de ceniza volcánica y gases transportados por el viento, 2) cálculo del tiempo de resistencia de la nube de ceniza en la atmósfera, 3) efectos de los vientos cortantes en el ascenso de la columna de ceniza. En cuanto al monitoreo continuo de las emisiones pasivas de SO2, existe una relación entre los eventos explosivos y los picos de incremento de emisiones de SO2 que sugiere una diferencia de aproximadamente 2 días entre los dos acontecimientos que podría indicar el tipo de comportamiento del volcán.