David Chaparro

Low soil moisture and high temperatures as indicators for forest fire occurrence and extent across the Iberian Peninsula

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.

SMOS and climate data applicability for analyzing forest decline and forest fires

Forests partially reduce climate change impact but, at the same time, this climate forcing threatens forests health. In recent decades, droughts are becoming more frequent and intense implying an increase of forest decline episodes and forest fires. In this context, global and frequent soil moisture observations from the ESAs SMOS mission could be useful in controlling forest exposure to decline and fires. In this paper, SMOS observations and several climate variables are analyzed together with decline and fire inventories, to study the effect of soil moisture on forest decline during an important drought on summer 2012, and on forest fires in the period 2010-2013. Results show that SMOS-derived soil moisture is a complementary variable in forest decline models. Some of the studied tree species exhibit high probability of decline occurrence under dry conditions. First results showed burned areas to be drier than unburned ones previous to the fire occurrences.

The Role of Climatic Anomalies and Soil Moisture in the Decline of Drought-Prone Forests

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.

Surface moisture and temperature trends anticipate drought conditions linked to wildfire activity in the Iberian Peninsula

Abstract 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.

SMAP Multi-Temporal vegetation optical depth retrieval as an indicator of crop yield trends and crop composition

Vegetation Optical Depth (VOD) is related to Vegetation Water Content (VWC). This provides new and highly valuable information for ecological and agricultural studies. In this work, VOD from the Soil Moisture Active-Passive (SMAP) satellite has been retrieved with the new Multi-Temporal Dual-Channel Algorithm (MT-DCA). Then, it has been applied to the study of crop yield trends and crop composition. The increase on VOD (ΔVOD) during crop development has been compared to yield data in two selected regions located in the United States. The first region presents a heterogeneous crop composition and weak ΔVOD-yield relationship (r2=0.21). The second region presents a highly homogenous cover and a strong exponential relationship (r2=0.65) between ΔVOD and yield. A saturation of yield is observed at a certain ΔVOD value. This pattern is probably due to an increasing plant density, which limits the crop yield due to plant physiological stress.

A spatially consistent downscaling approach for SMOS using an adaptive moving window

The ESAs Soil Moisture and Ocean Salinity (SMOS, 2009–2017) is the first mission using L-band radiometry to monitor the Earths global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improving the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm is proposed for retrieving high resolution (1 km) SM. This model is an extension of the “universal triangle” technique, and also introduces the concept of adaptive moving window. Its inputs are the low resolution SMOS BEC L3 SM and the brightness temperatures at vertical and horizontal polarizations (SMOS L1C), and the high resolution NDVI and LST from optically-based sensors. The proposed method allows obtaining high resolution SM maps worldwide, with no limitation in extension.

Remote sensing of vegetation dynamics in agro-ecosystems using smap vegetation optical depth and optical vegetation indices

The ESAs SMOS and the NASAs SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive to the water content in soils and vegetation. Focusing on the vegetation signal at L-band, we have implemented an inversion approach for SMAP that allows deriving vegetation optical depth (VOD, a microwave parameter related to biomass and plant water content) alongside soil moisture, without reliance on ancillary optical information on vegetation. This work aims at using this new observational data to monitor the phenology of crops in major global agro-ecosystems and enhance present agricultural monitoring and prediction capabilities. Core agricultural regions have been selected worldwide covering major crops (corn, soybean, wheat, rice). The complementarity and synergies between the microwave vegetation signal, sensitive to biomass water-uptake dynamics, and optical indices, sensitive to canopy greenness, are explored. Results reveal the value of L-band VOD as an independent ecological indicator for global terrestrial biosphere studies. 1

Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and Temperature Trends

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 (R2 = 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.