Gerard Portal

Microwave satellite vegetation parameters are widely used to monitor ecosystem spatiotemporal dynamics. Among these, vegetation optical depth (VOD) stands out as a critical microwave vegetation indicator, widely used for applications such as monitoring crop yield, estimating carbon stocks, and assessing risks threatening forests and their resilience to them. However, current VOD products are available at a coarse resolution, often around tens of kilometers, limiting their usefulness to only large-scale applications or those that do not need high spatial precision. Nevertheless, although extensive research has been conducted to improve the spatial resolution of several geophysical indicators, such as soil moisture, advances have been scarce for VOD. Here, we review the advances conducted to estimate VOD at medium-high spatial resolutions, overviewing the state of the art of different methods and their potentials and limitations. Basedon the available literature, we propose a taxonomy that classifies existing VOD downscaling approaches into proxy-based methods, which exploit the relationship between VOD and auxiliary variables, and data-fusion strategies that combine complementary microwave observations across sensors and frequencies. Also, we synthesize the suitability of different proxy variables according to the VOD frequency band, showing that while optical vegetation indices perform well for high-frequency VOD, the downscaling of low-frequency VOD benefits from the integration of complementary radar-derived proxies. Additionally, we examine the effectiveness and affordability of current validation methods and review the potential of emerging ones, such as GNSS technology and land surface models, to guarantee the reliable quality of future VOD downscaled products. Finally, we highlight the capabilities of future missions, particularly the upcoming CIMR multiresolution capability across frequency bands, which could considerably aid in obtaining VOD at better spatial scales.

Soil moisture is an essential climate variable exhibiting strong spatio-temporal dynamics, especially in the topsoil. Therefore, it is assessed multiple times by sensors within in situ networks, satellites, and by modeling of the Earth system. The resulting soil moisture fields from all methods are individual and non-congruent due to the imperfection of the methods and retrievals. But their spatial patterns have valuable similarities that call for investigation to foster intercomparison or even fusion of soil moisture products. In this research study, the similarity of spatial soil moisture patterns between passive microwave remote sensing products and Earth system modeling is investigated. We configure and apply spatial similarity metrics to enable a spatial comparison of the operational SMAP Dual Channel Algorithm (DCA) radiometer soil moisture product with the soil moisture output from IFS model runs of the ECMWF. The pattern assessment spans over the whole of Europe and aims to find the drivers behind the spatial soil moisture distributions at scales ranging from single grid cells (minimum) to continental (maximum) spatial scales, and between growing periods of wet (2021) and dry (2022) years. The two specifically configured metrics, total disagreement and mean category distance, showcase the opportunities and challenges when assessing spatial similarity in soil moisture fields across different scales. In addition, the potential drivers of the spatial moisture patterns were screened. Here, soil texture is the most influential single driver of spatial patterns in the IFS soil moisture runs, when analyzed in absolute terms [m³ m⁻³]. In relative terms of soil moisture [-] (soil wetness index), precipitation and soil temperature explain most of the variability of the IFS soil moisture for Europe. The SMAP retrievals are predominantly driven by the brightness temperatures, mostly influenced by surface temperature, vegetation water content, and soil roughness. These differences in drivers, as well as in methodology, culminate in an inherent discrepancy between the two soil moisture products. However, the assessment of their spatial patterns reveals the underlying similarity from the local to the continental scale.

This study evaluates temporal variability and algorithm differences in soil moisture estimates over Europe using the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis and the passive Soil Moisture Active Passive (SMAP) soil moisture product. While models and satellite retrievals have improved in capturing the timing of soil moisture dynamics, absolute accuracy and temporal variability magnitudes still diverge. This study compares the representation of short-term and seasonal variability of soil moisture in absolute and normalized terms over two different hydrometeorological growing periods (2021 and 2022). Both datasets exhibit intermediate to high temporal correlations with in situ measurements at selected stations (median Pearson correlation coefficients of all stations range between 0.65 and 0.79), confirming previous studies. However, they overestimate the magnitude of absolute soil moisture variability at most stations (median interquartile range of all stations at 0.085 (0.10) m³m⁻³ for ECMWF and 0.072 (0.079) m³m⁻³ for SMAP opposed to 0.063 (0.072) m³m⁻³ for in situ in 2021 (2022)) due to an overestimation of short-term fluctuations, especially at dry stations in southern France and Eastern Europe. The soil wetness index is underestimated, particularly within SMAP estimates. The performance of both is sensitive to hydrometeorological conditions, with the 2022 European drought causing strong seasonal and weak short-term fluctuations. This is easier to capture than conditions with pronounced short-term and weaker seasonal fluctuations, as in 2021. Overall, SMAP and ECMWF time series show considerable coincident timing, whereas the magnitude of temporal variability and accuracy depend on site-specific characteristics and the pre-processing of the data.

Understanding complex causal interactions between local, continental, and global drivers remains a significant challenge in wildfire prediction systems. This study implements a causal inference framework combining the Peter-Clark momentary conditional independence (PCMCI) algorithm with do-calculus interventions to analyse land-atmosphere feedback mechanisms influencing wildfire dynamics across South Asia (India, Pakistan, Myanmar, and adjacent regions). Time-series causal graphs derived from satellite and reanalysis data identified 500-hPa geopotential height anomalies (ΔZ₅₀₀) as the primary driver of surface aridity and wildfire incidence. Extreme scenario simulations via do-operator perturbations revealed that artificially enhancing ΔZ₅₀₀ to the 100th percentile, representing intensified upper-tropospheric ridging, produced the most severe mean burned area outcomes. Under these conditions, mean extreme burned area reached ~4.2 log ha (~15,000 ha), exceeding impacts from other perturbed variables. The integration of PCMCI-derived causal networks with counterfactual analysis provides a novel methodology for disentangling multiscale wildfire drivers, offering critical insights into future climate-driven fire risks through explicit representation of teleconnection mechanisms.

Understanding Soil Moisture (SM) at high spatio-temporal resolution provides crucial insights across various societal disciplines due to its direct impact on environmental and natural disasters monitoring, weather forecasting, agricultural productivity, and water resource management. In recent decades, a variety of algorithms have been developed to improve the spatial resolution of SM maps from passive sensors (∼40 km); however, the resulting maps, often with resolutions around 1 km or even hundreds of meters, still lack the necessary resolution for detailed local analysis. This study addresses this gap by presenting a machine learning methodology aimed at estimating SM at 60-m spatial resolution. A Feedforward Neural Network (FNN) is employed to capture the relationships among 14 different predictors, including several spectral bands and indices from Sentinel-2, land surface temperature from Moderate-Resolution Spectroradiometer (MODIS), elevation and slope from Shuttle Radar Topography Mission (SRTM), precipitation from the fifth generation of the ECMWF Re-Analysis for Land (ERA5-Land), and the sand fraction from SoilGrids250m, with European Space Agency (ESA) Climate Change Initiative (CCI) SM serving as the target variable. The model is trained and applied over the central part of the Iberian Peninsula (38.9°N–42.5°N and 3.5°W–7.2°W) from 2019 to 2021. At 60-m resolution, the SM maps effectively capture the spatial heterogeneity of the terrain. The temporal analysis demonstrates that high-resolution SM maps preserve virtually the same sensitivity as those from the ESA CCI, with a correlation of 0.66, a bias of 0.095 m3/m3, and an unbiased root mean square error of 0.044 m3/m3 on average.

L-band radiometry has been considered the preferred technique for global soil moisture (SM) remote sensing, as the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions proved. Owing the limited spatial resolution of current L-band radiometers, several downscaling algorithms have been developed to enhance the SMOS and SMAP SM resolutions. In this regard, the apparent thermal inertia (ATI) may be used for indirectly estimating SM, not only for disaggregation. In this study, l-km ATI-derived SM was obtained from March 31, 2015 to December 31, 2016, using Moderate Resolution Imaging Spectroradiometer (MODIS) as an alternative to L-band radiometry. The ATI-derived SM was validated over the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS). Comparisons with in situ SM showed slightly lower correlation (~0.62), but similar error (~0.043 m ³ ·m ^-3 ) and lower bias (~0.027 m ³ ·m ^-3 ) than the existing SMOS and SMAP products, whilst improving the resolution.

The European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) is the first spaceborne mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improve the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm for SMOS is proposed to obtain high-resolution (HR) SM maps at 1 km (L4), from the ∼40 km native resolution of the instrument. This algorithm introduces the concept of a shape adaptive moving window as an improvement of the current semi-empirical downscaling approach at SMOS Barcelona Expert Center, based on the “universal triangle”. Its inputs are as follows: the SMOS SM (L3 at ∼40 km spatial resolution), the vertical and the horizontal SMOS brightness temperatures (L1C at ∼40 km), and the HR normalized difference vegetation index and land surface temperature from optical-based sensors. The proposed method has the following advantages: HR SM maps are obtained while maintaining the dynamic range from the original L3 product; energy is conserved, because differences between aggregated L4 and L3 SM maps are negligible; and it can be applied to continental areas, even when they integrate different climates. A comparison of SMOS L3 and L4 products with in situ data for networks allocated in Spain and Australia shows good agreement in terms of correlation and root mean square error. The proposed method is shown to capture 1-km SM spatial variability while preserving the quality of SMOS at its native resolution.