Recent climate trends evidence a rise of temperatures and an increase in the duration and intensity of droughts which is in turn leading to the occurrence of larger wildfires, which threaten the environment as well as human lives and beings. In this context, improved wildfires prediction tools are urgently needed. In this paper, the use of remotely sensed soil moisture data as a key variable in the climate-wildfires relationship is explored. The study is centered in the fires registered in the Iberian Peninsula during the period 2010-2014. Their prior-to-occurrence surface moisture-temperature conditions were analyzed using SMOS-derived soil moisture data and ERA-Interim land surface temperature reanalysis. Results showed that moisture and temperature conditions limited the extent of wildfires, and a potential maximum burned area per moisture-temperature paired values was obtained (R ² = 0.43). The model relating fire extent with moisture-temperature preconditions was improved by including information on land cover, regions, and the month of the fire outbreak (R ² = 0.68). Model predictions had an accuracy of 83.3% with a maximum error of 40.5 ha. Results were majorly coherent with wildfires behavior in the Iberian Peninsula and reflected the duality between Euro-Siberian and Mediterranean regions in terms of expected burned area. The proposed model has a promising potential for the enhancement of fire prevention services.

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.

Increased intensity and duration of droughts and high-temperature events have been associated with forest decline in many parts of the world, and these decline events are expected to become more common under climate change. There is, therefore, a need for monitoring and modeling of forest decline. We used a regional forest condition monitoring program (DEBOSCAT) to study the spatial distribution of decline events in 2012 in Catalonia (Northeastern Spain) and their relationship with climatic factors. In 2012, this dataset was collected after an extraordinarily dry summer, and allowed the study of decline events in eight dominant tree species. We fitted a logistic model to predict forest decline probability as a function of species, precipitation and temperature anomalies, solar radiation, and remotely sensed soil moisture data from the Soil Moisture and Ocean Salinity Mission (SMOS). Broadleaved species were more affected by decline events than conifers. The statistical model explained almost 40% of forest decline occurrence, wherein almost 50% of this variability was explained by species effect, with broadleaved trees being generally more sensitive to the studied factors than conifers. Climatically wetter areas and those more exposed to radiation were more likely to be affected, suggesting better adaptation of forests in dry areas. In general, more damaged forests were characterized by high-positive temperature anomalies, lower than average rainfall, and low soil moisture in summer 2012. The most vulnerable species was Fagus sylvatica , a Euro-Siberian species, contrasting with Pinus halepensis , a typically Mediterranean species, which showed low sensitivity to drought.

Vegetation optical depth (VOD) has contributed to monitor vegetation dynamics and carbon stocks at different microwave frequencies. Nevertheless, there is a need to determine which are the appropriate frequencies to monitor different vegetation types. Also, as only a few VOD-related studies use multifrequency approaches, it is needed to evaluate their applicability. Here, we analyze the sensitivity of VOD at three frequencies (L-, C-, and X-bands) to different vegetation covers by applying a global-scale unsupervised classification of VOD. A combination of these frequencies (LCX-VOD) is also studied. Two land cover datasets are used as benchmarks and, conceptually, serve as proxies of vegetation density. Results confirm that L-VOD is appropriate for monitoring the densest canopies but, in contrast, there is a higher sensitivity of X-, C-, and LCX-VOD to the vegetation cover in savannahs, shrublands, and grasslands. In particular, the multifrequency combination is the most suited to sense vegetation in savannahs. Also, our study shows a vegetation–frequency relationship that is consistent with theory: the same canopies (e.g., savannahs and some boreal forests) are classified as lighter ones at L-band due to its higher penetration (e.g., as shrublands), but labeled as denser ones at C- and X-bands due their saturation (e.g., boreal forests are labeled as tropical forests). This study complements quantitative approaches investigating the link between VOD and vegetation, extends them to different frequencies, and provides hints on which frequencies are suitable for vegetation monitoring depending on the land cover. Conclusions are informative for upcoming multifrequency missions, such as the Copernicus Multifrequency Image Radiometer.

In this study, drought conditions involving risk of fires are detected applying SMOS-derived soil moisture data and land surface temperature models. Moisture-temperature (SM-LST) patterns studied between 2010 and 2014 were linked to main fire regimes in the Iberian Peninsula. Most wildfires burned in warm and dry soils, but the analysis of pre-fire conditions differed among seasons. Absolute values of SM-LST were useful to detect prone- to-fire conditions during summer and early autumn. Complementarily, SM-LST anomalies were related to droughts and high fire activity in October 2011 and February-March 2012. These episodes were coincident with abnormally anticyclonic atmospheric conditions. Results show that combined trends of new soil moisture space-borne data and temperature models could enhance fire risk assessment capabilities. This contribution should be helpful to face the expected increase of wildfire activity derived from climate change.Peer ReviewedPostprint (published version

Fires are a concerning topic in Mediterranean areas. They are increasing in number and extension, probably due to the anomalous dry and hot conditions experienced in this region in the last decade. In this study, more than 2,000 fires that took place in the Iberian Peninsula (2010-2014) were analyzed. The new all-weather version of SMOS-derived soil moisture product at fine scale resolution, as well as ERA-Interim Skin Temperature datasets, were used. Soil moisture and temperature anomalies based in these datasets were computed and included in the database. These information allowed analyzing prior-to-fire conditions. Results reported that more than 70% of fires started under dry and hot conditions, and this percentage rose till 94% in the anomalous conditions prior to the biggest fires. A relation between soil moisture, temperature and burned area is found which could set the basis for a fire risk index based on SMOS data and temperature information.