An Ensemble Kalman Filter (EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using a Soil-Vegetation-Atmosphere Transfer (SVAT) model during a complete growing season of corn in Central Mexico. Synthetic and field soil moisture (SM) observations and NASA SMAP SM retrievals were used to understand the effect of vertically spatial updates and uncertainties in meteorological forcings on RZSM estimates. Assimilation of RZSM every 3 days using SM observations at 4 depths lowered the averaged standard deviation (ASD) and the root mean square error (RMSE) by 60 % and 50 %, respectively, compared to the open-loop ASD. The assimilation of synthetic SM at the top 0-5 cm obtained RZSM closer to observations compared to THEXMEX-18 SM measurements and SMAP SM retrievals. Differences between EnKF estimates and SM observations and SMAP SM retrievals are mainly due to misrepresentation of vegetation conditions. The results improved SM estimates up to 10-cm depth using SMAP SM retrievals; however, additional studies are needed to improve SM at deeper layers. The implemented methodology can estimate SM at the top 10 cm of the soil every 3 days to mitigate the impact of the climate change on agricultural production over rainfed areas, particularly in developing countries.

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.

The agricultural productivity and the optimized use of water resources rely on the soil moisture (SM) retrieval to achieve some of the sustainable development goals such as ensuring food security and monitoring climate change. One of the main aspects to provide accurate SM retrieval results is the selection of the most effective models. This study is carried out to exhibit the impact of three different soil formulations (i.e., Linear, Oh, and I2EM) within the Water Cloud Model (WCM). The experiments are conducted based on the combined use of Sentinel-1 (S-1) and Sentinel-2 (S-2) images. The in-situ measurements used in this work are collected from five different fields in Huamantla, Central Mexico. The experiments focus on the complete growing season of corn taking into consideration the soil and the vegetation contribution. The best Bias and ubRMSD values obtained by the Oh-WCM are equal to -0.437dB and 0.295dB, respectively at VV in PX1. The I2EM-WCM achieved Bias and ubRMSD values equal to -0.760 dB and 0.379 dB at VV, respectively. The Linear-WCM also obtained low Bias and ubRMSD values equal to -0.297dB and 0.322dB, respectively. Therefore, the combination of the Oh model within the WCM is considered as the appropriate combination for the SM retrieval due to its high achieved accuracy. The sensitivity analysis of changes in $\sigma ^{0}_{pq,tot}$ due to changes in SM found that it is possible to capture changes higher than 0.06 m $^{3}$ /m $^{3}$ in SM over the complete growing season of corn using C-band backscatter observations.

This work presents a comparative analysis of six airfoil profiles for small-scale vertical axis wind turbines (VAWTs) operating under low wind speeds (2–8 m/s) typical of urban environments. Aerodynamic performance during startup and nominal operation is investigated using two widely adopted modeling approaches, the Double Multiple Streamtube (DMST) and the Lifting Line Free Vortex Wake (LLFVW) methods, implemented in the open-source QBlade framework. The objective of the study is to evaluate relative airfoil performance and the consistency of observed trends across aerodynamic models commonly used in early-stage VAWT design. The results demonstrate a fundamental trade-off between self-starting capability at low tip-speed ratios ($\lambda <2$) and power efficiency at nominal operating conditions ($2\le \lambda \le 4$). Low-Reynolds-number and VAWT-oriented airfoils (S1210, E387, and DU 06-W-200) show enhanced startup torque under weak inflow conditions, whereas symmetric NACA airfoils (NACA 0015 and NACA 0018) deliver higher power coefficients once operational tip-speed ratios are achieved. Comparison with experimental benchmark data indicates that the transient LLFVW model yields improved agreement relative to the stationary DMST approach, which tends to overestimate performance at moderate and high tip-speed ratios. Overall, the study provides practical guidance for airfoil selection in micro-scale VAWTs intended for urban applications, where reliable self-starting and efficient operation must be carefully balanced.

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Vertical axis wind turbines (VAWTs) require efficient aerodynamic models that can predict their performance at a low computational cost, especially in the preliminary stages of design and parametric analysis. This paper presents a comparative evaluation of two widely used simplified aerodynamic models: the Double Multiple Stream Tube (DMST) model and the Lift Line with Free Vortex Trail (LLFVW) model. Both approaches are applied to a small-scale Darrieus-H turbine and validated using experimental data reported in the literature. Two modeling configurations with different levels of physical complexity are analyzed, considering the influence of induction losses, wake expansion, and unsteady aerodynamic effects. The results show that the DMST model tends to overestimate the maximum power coefficient and shift the optimum operating point toward higher tip speed ratio (TSR) values, even when corrections for tip losses and variable induction factors are incorporated. In contrast, the LLFVW model more accurately reproduces wake dynamics and non-stationary effects, achieving better agreement with experimental data across the entire operating range. In particular, the LLFVW more accurately captures both the magnitude and position of peak performance, identifying itself as a more reliable model for the aerodynamic prediction of VAWTs.