Fires were obtained from the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product (MOD14A1_M). The product is a quantification of the number of fires observed within a 1000 km2 area over a month. A fire must cover at least ∼ 1000 m2 to be detected and must not be covered by clouds, heavy smoke, or tree canopy (Giglio et al., 2020). The active fire product is based on the 1 km fire channels at 3.9 and 11 µm of the MODIS Terra and Aqua satellites (Justice et al., 2006). It is distributed at 0.1∘ resolution and at a monthly timescale by the NASA Earth Observations (NEO) portal.

Precipitation (P) data come from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). The precipitation estimate uses the PERSIANN algorithm on GridSat-B1 infrared satellite data and training of the artificial neural network on the National Centers for Environmental Prediction (NCEP) hourly precipitation data (Ashouri et al., 2015). The dataset is distributed by the National Oceanic and Atmospheric Administration (NOAA) at a daily timescale and at 0.25∘ resolution in the latitude band 60∘ S–60∘ N.

The soil moisture (SM) dataset comes from the SMOS satellite, launched by the European Space Agency (ESA) in 2009 (Kerr et al., 2001). It performs passive measurements of the thermal emission of Earth at the L band (1.4 GHz, 21 cm). L-band VOD and SM are derived from SMOS brightness temperatures using the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model (Wigneron et al., 2007; Kerr et al., 2012). L-band SM is the volume of water per volume of soil (m3 m-3) in the top surface soil layer (∼ 5 cm). The footprint size is ∼ 43 km on average (Kerr et al., 2010). We considered the ESA Level 2 SM dataset in version 7.2 (L2 v720) resampled to the global cylindrical Equal-Area Scalable Earth (EASE) Grid version 2.0 (Brodzik et al., 2012) at 625 km2 spatial sampling (25 km × 25 km at 30∘ of latitude). Ascending (06:00) and descending (18:00) overpasses were averaged, from June 2010 to December 2020.

Terrestrial water storage (TWS) anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellite were also considered. We used the monthly GRACE/GRACE-FO (Follow-On) Level 3 product provided through the Gravity Information Service (GravIS) web portal of the German Research Centre for Geosciences (GFZ) at 1∘ latitude–longitude grids (Boergens et al., 2019). TWS anomalies (cm) represent the water mass anomalies from snow, surface water, soil moisture, and deep groundwater. They are derived from measurements of temporal changes in Earth's gravity field. Data were lacking for 35 months in the 10-year dataset. One-time gaps were filled by linear interpolation; consecutive missing months were not considered (September–November 2016, July 2017–May 2018, and August–October 2018; 17 months in total).

Temperature (T) data come from the land surface temperature (LST) dataset from the MODIS Terra satellite (NASA). Daytime and nighttime measurements were averaged (MOD11C3 Version 6 product in the Climate Modeling Grid (CMG), LST_Day_CMG, and LST_Night_CMG; Wan et al., 2015). These datasets are obtained using MODIS thermal infrared bands from 3 to 15 µm and distributed by the NASA Land Processes Distributed Active Archive Center (LP DAAC) at a monthly timescale and at 0.05∘ resolution.

Vegetation optical depth (VOD) is a remotely sensed indicator related to AGB and to VWC (Kerr and Njoku, 1990; Jackson and Schmugge, 1991; Jones et al., 2011; Rahmoune et al., 2014; Vittucci et al., 2016; Rodriguez-Fernandez et al., 2018; Mialon et al., 2020). No clear approach exists for disentangling the contributions of AGB and VWC in the VOD because of the co-sensitivity of microwave observables to both quantities (Konings et al., 2019). The lower frequencies have better capabilities of penetrating deeper within the canopy (Ulaby et al., 1981). At the L band, VOD is sensitive to coarse woody elements, such as trunks, stems, and branches. At the C and X bands, VOD is more sensitive to thin stems and leaves (Guglielmetti et al., 2007). L-VOD is then more sensitive than higher-frequency VODs to high AGB values and is a good proxy for dense vegetation (Rodriguez-Fernandez et al., 2018). In this paper, L-VOD comes from the SMOS Level 2 dataset in version 7.2 (L2 v720) measured at 1.4 GHz (λ=21 cm), resampled to EASE-Grid 2.0 at 625 km2 resolution (25 km × 25 km at 30∘ of latitude). In the SMOS retrieval algorithm, the vegetation attenuation is taken into account by the τ parameter of the τ-ω model (Mo et al., 1982), which corresponds to the L-VOD. Data from June 2010 to December 2020 were considered, and ascending (06:00) and descending (18:00) overpasses were averaged. C- and X-VOD from the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission (GCOM) Advanced Microwave Scanning Radiometer 2 (AMSR2) dataset were also considered (Imaoka et al., 2010). C- and X-VOD are measured at 6.9 GHz (λ=4.3 cm) and 10.7 GHz (λ=2.8 cm) respectively. The C2 band (7.3 GHz, λ=4.1 cm) was not discussed in this paper, as the data were mostly redundant with the C1 band (6.9 GHz). We used the daily L3 V001 VOD products, from July 2012 to December 2020, processed with the land parameter retrieval model (LPRM) algorithm (Owe et al., 2008) and distributed by NASA on a regular grid at 25 km × 25 km resolution. Ascending (13:30) and descending (01:30) overpasses (LPRM_AMSR2_A_SOILM3 and LPRM_AMSR2_D_SOILM3) were averaged.

VOD values were compared with the visible–near-infrared-based enhanced vegetation index (EVI) from MODIS (NASA) MOD13C2 and MYD13C2 Version 6 for the Aqua and Terra Satellites respectively, distributed at 5600 m resolution (Didan, 2015). EVI represents canopy greenness, with an improved sensitivity over high-AGB regions compared to NDVI. It is obtained by combining measurements at red (λ=0.6–0.7 µm, f ∼ 460 THz) and near-infrared wavelengths (λ=0.7–1.1 µm, f ∼ 330 THz).

Data from June 2010 to December 2020 were considered (10.5 years), except for C- and X-VOD from AMSR2 which were only available from July 2012. Monthly averages of all datasets were computed and resampled to SMOS EASE-Grid 2.0 (∼ 25 km resolution) with a weighted average interpolation, using GDAL (Geospatial Data Abstraction Library) (GDAL/OGR contributors, 2020). SMOS data (SM and L-VOD) were filtered from RFI (radio frequency interference) impacts by using a 20 % maximum threshold on the RFI probability, provided by the SMOS Level 2 product. Only the centre part of the swath was considered (less than 450 km away from the sub-satellite track) so as to only use optimal retrievals. Microwave measurements were also proven to be disturbed by strong topography (Mialon et al., 2008), snow (Schwank et al., 2014), and standing water (Ye et al., 2015; Jones et al., 2011; Bousquet et al., 2021). Hence, for all datasets, we removed strong-topography areas based on the SMOS topography mask, snow-covered months based on the IMS database (20 % maximum snow coverage), water-contaminated areas based on the land cover map (50 % maximum water fraction), and flooded ones based on GIEMS-2 climatology (20 % maximum water fraction).

Fires were then studied at the ecosystem scale to assess the general factors and impacts of fire according to the specific features of each biome (Fig. 3). Five land cover classes were studied: grasslands and croplands (IGBP labels 10, 12, and 14), savannas and shrublands (IGBP labels 6, 7, 8, and 9), needleleaf forests (IGBP labels 1 and 3), sparse broadleaf forests (IGBP labels 2 and 4; AGB ≤ 150 Mg ha-1), and dense broadleaf forests (IGBP labels 2 and 4; AGB > 150 Mg ha-1). Only the latitude band 60∘ S–60∘ N was retained in order to be consistent with the precipitation dataset extent. Only the range July 2012–December 2020 was conserved here for all datasets so as to match with AMSR2 time period. For fire event selection, the time range was reduced from September 2013–October 2019 to avoid fire events occurring at the very beginning (or end respectively) of the period to be able to study possible pre- and post-fire anomalies. Only areas showing a unique and intense fire event over the 6-year period were considered to properly observe the factors and impacts of this event over a long time period without any other disturbance. This excluded dry areas with regular seasonal fires, such as the Sahel region. Two conditions were empirically defined as mandatory to select a fire event over a given pixel: (i) a minimum number of fires of 5 at the height of the fire and (ii) a maximum number of fires of 2.5 outside the period (±6 months) around the main fire event to ensure that the vegetation recovery is linked with the main fire event and is not affected by another significant one. Anomalies were computed with Eqs. (1) and (2), with a climatology over all dates excepted the year of the fire event, in order to remove these exceptional values. The anomaly time series were then shifted to collocate in time all fire events and averaged by ecosystem. To ensure the spatial representativeness of each ecosystem, the months with a number of available points lower than half the maximum number of points of the ecosystem were filtered out from the shifted time series.

Pre-fire climatic anomalies and post-fire vegetation anomalies were also aggregated at different timeframes and plotted, in order to compare their temporal behaviour in different ecosystems. The standard error of the mean of the measurements σ was also computed with Eq. (3): 3σp=SDpn, where SD is the standard deviation of the population p and n is the number of samples.

In evergreen forests of the South Coast of New South Wales in Australia (Fig. 4a), fires reached a maximum in January 2020 (mean number of fires = 8). They are associated with high temperature and low precipitation (anom(T) = +3∘ C, anom(P) = -80 mm). The drought started 3 years before fire (decrease in precipitation, SM, and TWS). All vegetation data exhibit the same pattern, which is (i) a constant and mild decrease since 2012, (ii) a strong decrease just before and during the fire event (∼ -0.15), and (iii) a rapid post-fire recovery (∼ 1 year). C-VOD is the most affected vegetation variable.

In California, no major pre-fire drought is visible in summer 2018 (Fig. 4b). The Mendocino Complex was the strongest of the three case studies, with 20 fires observed on average in August 2018. It provoked a decrease in all vegetation variables, particularly in L-VOD (anom(L-VOD) = -0.08) and in EVI (anom(EVI) = -0.10). Whereas C- and X-VOD regained their pre-fire values rapidly (∼ 1 year), EVI and L-VOD did not.

In the dense rainforest near Santarém (Brazilian Amazon), the number of detected fires in December 2015 is quite low (∼ 4.5) (Fig. 4c), but this value may be underestimated because of cloud coverage (Giglio et al., 2020). Vegetation variables are stable before the fire event, even if the L-VOD signal is quite noisy because only two SMOS pixels were considered here. Strong positive temperature anomalies (+3∘ C), negative precipitation anomalies (-160 mm), and TWS anomalies (-60 cm) are visible during the fire and reach their extremum at the end of the fire period. Surprisingly, SM stayed stable during the fire. L-VOD was substantially impacted by the fire (anom(L-VOD) = -0.14), as well as EVI (anom(EVI) = -0.09). C- and X-VOD were barely affected (anom(C-VOD) = -0.04, anom(X-VOD) = -0.01). EVI recovered in ∼ 2–3 years, whereas L-VOD never recovered its pre-fire level.

In this section, major fires from September 2013 to October 2019 were analysed at the ecosystem scale by shifting the anomaly time series of all variables on the fire date tfire. The considered fires are well spread spatially and temporally over the 6-year period (Fig. 3). In grasslands and savannas (Fig. 5a and b), pre-fire anomalies of hydrologic variables are slightly positive, and temperature anomalies are negative during 2 years before fire. Concurrently, vegetation variables start to increase and reach a maximum a few months before the fire event (particularly C- and X-VOD). Anomalies of vegetation variables also show a light surplus over needleleaf forests just before the fire event (Fig. 5c). Over forests (Fig. 5c, d, e), a 1-year pre-fire drought is visible through the temperature increase and the decrease in precipitation, SM, and TWS. For all ecosystems, these drought conditions intensify just before and during fire and end a few months after fire. During fire, all vegetation variables abruptly decrease in all ecosystems, EVI being the most impacted one and also faster to recover. L-VOD is particularly long to recover over forests, especially dense broadleaf ones (more than 4 years, Fig. 5e). In needleleaf forests (Fig. 5c), VODs continue to decrease for 1 year. In low-vegetation ecosystems (Fig. 5a and b), C- and X-VOD never regain their immediate pre-fire values, which were particularly high.

Anomalies of climate variables were also averaged in space and in time, within timeframes of 6 months, from 24 to 1 month pre-fire, in order to observe their general trends (Fig. 6). The error bars were computed with Eq. (3). Precipitation anomalies (Fig. 6a) are negative from 6 months pre-fire for all classes and reach -15 mm per month on average before the fire event. The precipitation deficit is more intense in dense broadleaf forests, starting 2 years pre-fire and reaching -55 mm per month before the fire event. SM anomalies (Fig. 6b) are similar for the three forest classes. The SM deficit starts 18 months pre-fire and reaches -0.04 m3 m-3 before the fire event. Savannas and grasslands are affected later (6 months pre-fire) and to a lesser extent (∼ -0.01 m3 m-3), as previously observed in Fig. 5. TWS anomalies (Fig. 6c) are negative from 24 months pre-fire for needleleaf forests and from 6 months pre-fire for dense broadleaf forests. This ecosystem is again the most impacted one, with a minimum TWS anomaly of -7 cm before fire. Temperature anomalies (Fig. 6d) show significant negative anomalies in grasslands, savannas, and needleleaf forests from 2 to 1 year pre-fire. From 6 months pre-fire, temperature anomalies show a surplus in nearly all ecosystems and reach +1.1∘ C in needleleaf forests and +0.7∘ C in dense broadleaf forests before the fire event. In summary, pre-fire drought is mainly observed in forests, with particularly low hydrological values in dense forests (rainforests) and particularly high temperatures in needleleaf forests (boreal ecosystems). Savannas and grasslands barely suffer from pre-fire drought; temperatures are even mild 1 year pre-fire.

Vegetation variable anomalies were averaged within timeframes of 6 months, from 1 to 36 months post-fire, in order to observe the global impacts and recovery time (Fig. 7). We considered that a variable has totally recovered when its anomaly is between -0.005 and +0.005. Immediately after fire, EVI is the most impacted variable, with average anomalies of -0.026 over grasslands, -0.022 over savannas, -0.033 over needleleaf forests, -0.051 over sparse broadleaf forests, and -0.048 over dense broadleaf forests (Fig. 7a). EVI recovers rapidly, in about 25 to 30 months. X-VOD is less affected over forests (-0.015) than over low vegetation (-0.025) (Fig. 7b). X-VOD gets back to normal within 3 years, savannas and shrublands taking the longest to recover. C-VOD recovers slower than X-VOD, in particular over forests (Fig. 7c). L-VOD is mainly affected over dense broadleaf forests (Fig. 7d). Negative anomalies decrease up to -0.05 6 months post-fire, then slowly increase. L-VOD is less affected than C-VOD elsewhere. It also shows a delayed impact over needleleaf forests, as for C- and X-VOD.

In south-east Australia, a strong pre-fire drought is visible not only in the climate variables but also in the mild decrease in vegetation variables (Fig. 4a), linked with VWC deficit. Ehsani et al. (2020) stated that the air temperature from December 2019 to February 2020 was about 1∘ C higher than usual, which increased evapotranspiration, while the lack of precipitation prevented the soil from satisfying the moisture demand and led to a significant vegetation drying (fuel) that facilitated the propagation of fires. After the fire event, L-VOD regained its pre-fire values within a year, meaning that the woody biomass was not entirely destroyed. Indeed, these eucalyptus forests are known to be somewhat fire resistant (Wilkinson and Jennings, 1993; Caccamo et al., 2015). They can regenerate branches and leaves by resprouting from heat-resistant buds (Burrows, 2002). The rapid recovery of vegetation data can also be explained by the recovery of VWC, linked with the post-fire increase in precipitation and SM (Konings et al., 2021). Indeed, in 2020, SM values exceeded those of the previous decade (anom(SM) = +0.15 m3 m-3), corresponding to the end of the severe drought affecting south-east Australia associated with the 2020–2021 La Niña event (BoM, 2021). The increase in SM and precipitation may also have expedited the extinction of fires (Ehsani et al., 2020).

For California, the study by Brown et al. (2020) provides a comprehensive analysis of the climate and fuel conditions leading to the 2018 Mendocino Complex and reports several events that are also noticeable in our analysis. Among these, positive rainfall and SM anomalies in winter 2016/17 are depicted in Fig. 4b, which led to the second consecutive spring with above-average accumulation of fine fuel (grasses). Positive temperature anomalies in winter 2017/18 are also visible, when a lack of storm enabled the survival of grasses. In April 2018, precipitation and warm temperatures led to above-normal spring brush and grass growth. No major drought is visible in summer 2018, but low rainfall and warm temperatures led to a rapid drying of fuels and induced a poor overnight humidity recovery. All these similarities with the findings by Brown et al. (2020) support our observations. The dramatic fire impacted EVI and L-VOD in the long term. Eucalyptus, pine trees, and chaparral were burned. Even if this type of vegetation is fire-adapted, the strength of the fire seemed to have destroyed most of it (34 % vegetation loss, Hansen et al., 2013). Increased forest fire activity in recent decades in California has likely been enabled by the legacy of fire suppression, human settlement, and anthropogenic climate change (Abatzoglou and Williams, 2016). Stephens et al. (2018) stated that the massive current tree mortality in California will undoubtedly provoke severe “mass fires” in the coming decades, driven by the amount of dry and combustible wood.

In the Santarém region (Amazon), the winter 2015 wildfire was attributed to high temperature and low precipitation linked with El Niño event (Berenguer et al., 2018), which clearly emerges from Fig. 4c. These extreme drought conditions worsened during and at the end of the fire and may explain its strength. Several factors can explain this observation. First, MODIS may not detect all fires in January 2016 in this area because of (i) the cloud coverage (Roy et al., 2008) and (ii) the dense vegetation cover hiding understorey fires (Withey et al., 2018). This would be in line with the 2016 Hansen et al. (2013) tree cover loss detection (Fig. 2). Secondly, drought may sometimes keep increasing after fire extinguishment because the removal of the vegetation cover and the deterioration of the soil contributes to maintaining a hot and dry climate (Auld and Bradstock, 1996; Veraverbeke et al., 2010). This phenomenon is also visible in the savanna and in the sparse broadleaf biome (Fig. 5b and d). Contrary to TWS and precipitation, SM stayed stable during the fire, maybe because of the reduced accuracy of SM measurements under very dense forest. The 3-year recovery time of EVI after the severe fire indicates a moderate regrowth of leaves and grasses. In contrast, L-VOD never regained its pre-fire values, meaning that trunks were impacted in the long term.

Grasslands, croplands, shrublands, and savannas do not show signs of pre-fire drought (Figs. 5a, b and 6). Indeed, in these dry ecosystems, the standard summer conditions are often prone to wildfire ignitions (Chaparro et al., 2016). A substantial increase in vegetation variables, C- and X-VOD in particular, occurs 1 to 2 years before fire, which implies an increase in vegetation density, e.g. available fuel. This is consistent with the fact that the C and X bands are more sensitive to dry low shrubland vegetation (Jackson et al., 1982; de Jeu et al., 2008). Immediately before fire, both VOD and SM values drop down, suggesting a decrease in VWC, especially over grasslands (Fig. 5a). The increase in vegetation material combined with the decrease in VWC may contribute to triggering large wildfires (Forkel et al., 2017; Kuhn-Régnier et al., 2021). Indeed, the fire risk in savannas is highest for dry vegetation with enough fuel to enable drastic burning (Mbow et al., 2004). This vegetation growth might be enabled by negative pre-fire temperature anomalies and a light positive anomaly in pre-fire hydrological variables (Fig. 6). Vegetation variables are less impacted by fires (Fig. 7), which are rapid and burn through the grass layer, resulting in less destruction than in forests (Menaut et al., 1990; Gignoux et al., 1997). L-VOD in particular is slightly impacted because the burned vegetation is mainly leaf biomass. EVI quickly recovers after fire, probably because fire burns most of the AGB of grass species but spares their large underground root systems, resulting in a rapid establishment of new shoots (Hochberg et al., 1994). The exceptionally high pre-fire vegetation variable values are never regained.

In needleleaf forest anomaly time series (Fig. 5c), the numerous missing values correspond to the filtering of snow in winter. These wildfires are located in the Northern Hemisphere temperate and boreal forests and mostly occur in late spring and summer (Fig. 3). De Groot et al. (2013) explained that most fires in Canada occur during summer, due to lightning strikes, whereas most fires in Russia occur during spring and are human-caused. We found a strong pre-fire drought in this ecosystem (low SM and high temperature 1 year pre-fire, Fig. 6), which is well documented for previous fire episodes (Weber and Stocks, 1998). Our results are in line with those of Forkel et al. (2012), who found that previous-summer SM was a good predictor for burned area in Siberian larch forests. Indeed, negative summer anomalies led to low frozen water the following winter and to less water being released during the following spring–summer season, which in turn eased the outbreak of large wildfires. VODs also showed a light surplus before fire, possibly linked with litter thickening (e.g. dead needles, cured grass, leaf litter), which also facilitates fire propagation (de Groot et al., 2013). We found a delayed impact of fire on vegetation variables, as well as a longer recovery time than in other ecosystems, of about 3–4 years (Figs. 5c and 7). This duration is slightly less than what was found in other studies (5 years in Canada, Goetz et al., 2006; 5 to 8 years in North America, Jin et al., 2012), but our findings still confirm previous results from Yang et al. (2017), who showed with NDVI analyses over North America that the fire effect on needleleaf trees was stronger and longer than on other vegetation types. Fires in North America are predominantly stand-replacing and high-intensity crown fires (Stocks et al., 2004; Jin et al., 2012), whereas fires in Eurasia are generally lower-intensity surface fires and less destructive for the vegetation (de Groot et al., 2013). These different fire regimes are influenced by tree species (Rogers et al., 2015). Time series were plotted separately over each continent (Fig. S1). L-VOD and EVI recover more slowly in North America than in Eurasia (∼ 4 vs. ∼ 2 years for L-VOD, ∼ 3 vs. ∼ 2 years for EVI), confirming these different boreal fire regimes. Moreover, we found that L-VOD is moderately impacted during fire, which can be attributed to the dominant destruction of needles and branches by boreal fires (Alexander and Cruz, 2011).

Sparse broadleaf forests (AGB ≤ 150 Mg ha-1) subject to wildfires are mostly located in subtropical and temperate areas of the Americas, West Africa, Australia, and South-East Asia (Fig. 3). A drying trend is visible 1 year pre-fire (Figs. 5d and 6). The link between drought and wildfires was previously observed by de Marzo et al. (2021) in the Argentine Gran Chaco, by Cheng et al. (2013) in the Mexican Yucatán forest, and by Vadrevu et al. (2019) in South-East Asian forests, with a prominent influence of precipitation variations over temperature variations. L-VOD and EVI are particularly impacted by fire, but they recover quickly (1 year for EVI, 2.5 years for L-VOD). Yang et al. (2017) also found a rapid recovery time over North American broadleaf trees due to their fire-adaptive resprouting regeneration mode. The same observations were made in the fire-prone Argentine Chaco forest by Torres et al. (2014).

Dense broadleaf forests are mostly located in the tropics (Fig. 3). We can notice a few fires in the densest rainforests (Congo basin, central Amazon) because (i) they are usually too humid to burn (Cochrane, 2003; Forkel et al., 2017), (ii) MODIS active fire detections are underestimated under thick cloud coverage or for understorey fires (Giglio et al., 2020), and (iii) seasonally flooded areas were excluded in order to use only robust VOD estimations (Bousquet et al., 2021). A consistent drought is visible 8 months before fire events (Fig. 5e), with high negative SM, TWS, and precipitation anomalies (Fig. 6). Chen et al. (2013) also found TWS deficits before severe fire seasons across the southern Amazon. Indeed, rainfall shortage generates high water deficits (i.e. high negative TWS and SM anomalies), which cause tree mortality and leaf shedding (visible in pre-fire EVI decrease) and thus increase fuel availability (Aragão et al., 2018). Nevertheless, no pre-fire VOD decrease is observed here, showing that tree species of dense forests can maintain their VWC. Drought-related fires were suggested to prevail over deforestation fires in the Amazon and are predicted to increase in the near future (Aragão et al., 2018). The opening of forest canopies also boosts incident radiation levels which leads to temperature rise (Ray et al., 2005). The combination of fuel increase in a drier and hotter environment converts forests into fire-prone ecosystems (Aragão et al., 2018). We also found that dense broadleaf forests were the ecosystem most impacted by fire (Fig. 7) because the absolute pre-fire values of vegetation variables are particularly high and because it is not a fire-adapted ecosystem (Cochrane, 2003). L-VOD in particular decreases strongly and recovers very slowly (Fig. 7d), as previously observed over the Santarém fire (Fig. 4c). The strong post-fire decrease in L-VOD is due not only to biomass destruction but also to water stress in the remaining vegetation (Konings et al., 2021). This finding confirms the significant and damaging impact of fires in the dense broadleaf ecosystem previously observed by Silva et al. (2018) and de Faria et al. (2021). L-VOD was previously proven to be more sensitive to high AGB values than C- and X-VOD (Rodriguez-Fernandez et al., 2018). Here, we suggest that L-VOD depicts the fire impact on high-AGB areas better than the other vegetation variables.

For all biomes, EVI is the most rapid index to recover, because leaves rapidly resprout. EVI and X-VOD seem better adapted for grassland fire monitoring; C-VOD is better for savanna fire monitoring; and L-VOD is better for forest fire monitoring.

Normalized anomalies of vegetation variables were also plotted with respect to the number of fires in the dense broadleaf ecosystem, immediately after fire (1–3 months post-fire, Fig. 8a) and over a longer period (1–2 years post-fire, Fig. 8b). A quasi-linear relationship is visible between all vegetation estimates and the number of fires. As previously observed in Sect. 4.2, EVI and L-VOD are the most impacted variables immediately after fire (Fig. 8a), while L-VOD is still significantly affected 1 to 2 years after fire (up to -0.06, Fig. 8b). L-VOD then shows a clear response to fire events over high-AGB areas, not only immediately but also in the long term and proportionally to the number of fires within a SMOS pixel. Thanks to its sensitivity to coarse woody elements and to its deep penetration through the vegetation layer, L-VOD is better correlated with high AGB than other vegetation variables (Rodriguez-Fernandez et al., 2018) and could be used for post-fire recovery monitoring over dense forests. One must keep in mind that not only the biomass density (AGB) but also its hydrological status (VWC) are depicted in the VOD.