This study addresses discerning causal relationships in complex systems, a key aspect of interpretable machine learning. It focuses on the unusual and intense early summer weather in South Asia during April and May 2022 that led to an increased number of forest fires. This work employs a Bayesian network (BN), constructed using the NOTEARS algorithm, to analyse the contribution of various land and atmospheric variables on the extent of burned areas. In a scenario analysis using peak values of 300-hPa meridional circulation index and 500-hPa Geopotential Height Anomalies, indicative of a strong atmospheric block, the likelihood of large burned areas (>3.06 log ha or >1150 ha) increases from 36.6% to 41.6%. This is due to a rise of conditional probabilities in the Vapor Pressure Deficit (VPD) (> 5.21 kPa) by 24.8%, and the Land Surface Temperature (LST) (>45.7°C) by 15.6%. In addition, sensitivity and spatial analyses indicate that extreme dry conditions, characterized by high LST and VPD due to the trapping effects of the omega block jet stream pattern, were the primary factors influencing the extent of burned areas during the 2022 South Asia heatwave.

Storage of interception water in the canopy (Sc) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (Mg) and Sc. The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. Sc-values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal Mgcycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that Scmaxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify Scand evaporation fluxes independently from EC measurements and field experiments.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

This study presents a comparison between satellite-based vegetation optical depth (VOD) from multi-frequency radiometry (X-, C- and L-band), VOD-derived relative water content (RWC) and auxiliary data (e.g., evapotranspiration and soil moisture), which are investigated for their sensitivity to water status of tree canopies under dry and wet conditions for a temperate forest in Thuringia, Central Germany. For this, we estimated RWC directly from VOD normalization assuming no major changes in vegetation biomass or plant structure during the study period (2015-2019).Our results show that RWC seasonalities are aligned for all investigated frequencies showing its maximum in early summer when leaves and twigs of the top and low canopy are particularly wet and photosynthetically active. Investigating drought versus non-drought years, we observed that X-band RWC is the one better capturing drought status by exhibiting low values in the extreme drought year 2018 compared to the wet year 2017 while L-band RWC reflects the ecological memory from the extreme drought conditions in 2018 in year 2019 estimates.

In the work a random forest model has been implemented as an interpretable machine learning tool in the effort to estimate the burned areas caused by fire outbreaks in India, Pakistan, and Myanmar in April and May 2022. The proposed model combines environmental and atmospheric (including upper tropospheric) factors suggested to drive patterns of burned areas, and determines the weight of each factor on the propagation of fires. Results demonstrate that the model mimics the actual burned area by considering a combination of vegetation, atmosphere, and human-related variables and improves accuracy by approximately 7% after adding jet stream features. This approach could lead to implement a semi-operational forecast system that may be tested in multiple demonstration sites.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m3•m-3), demonstrating the method’s significant potential for deriving high-resolution soil moisture information.

Soil moisture and vegetation optical depth (VOD; related to vegetation water content) retrieved from SMAP and SMOS satellites are widely used for a range of hydrosphere and biosphere applications. However, while soil moisture has been globally well-validated, VOD validation has been sparse. Furthermore, simultaneously retrieval of these parameters results in uncertainties both individually in soil moisture and VOD retrievals as well as in compensation between the parameters. Here, we show global locations where soil moisture and VOD retrievals will have lower uncertainty, based on complementary brightness temperature information content and signal-to-noise ratio metrics. In these same locations, we show that error still propagates more into VOD. However, using VOD regularization algorithms, this error is greatly reduced, especially at sub-weekly timescales where algorithmic error can be most apparent. Despite these regularization approaches that reduce errors, there are yet vast differences in available global regularized retrievals originating from different algorithmic choices.

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil-plant-atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM-VOD retrievals are feasible by inverting the τ–ω model with two independent datasets in dual channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBvand TBh), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the Degrees-of-Information (DoI) metric and propose a Signal-to-Noise Ratio (SNR) metric to assess the “retrievability” of VOD given the SMAP TBv-TBhlinear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting applications of VOD in ecology and hydrology. Results show that regions with mainly non-woody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of under-determined inversions: the L3-DCA and the MTDCA, which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (δ), representative of biomass/structure dynamics. The dynamic range of δ is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that δ ranging from 0 to 3.35.10-4is needed for modelling physically reasonable Mg values. The global average of δ shows consistent spatial patterns across vegetation distributions, and δ seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD.

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the proposed approach. As a first result the radiometer-based water potential estimates of the forest stand are concurrent in time and similar in value with their in situ (xylem) counterparts from single trees in the radiometer footprint.

Vegetation Optical Depth (VOD) is highly sensitive to Above-ground Biomass (AGB), particularly at L-band, as this band is more sensitive to the canopy layer and has greater capacity to penetrate the vegetation. However, higher frequencies, such as C- and X-bands are more related to smaller vegetation structures. Then a combination of these three bands is expected to be relevant to accurate AGB retrievals. This study presents a comparison of three VOD products at three bands and evaluates the performance of a multi-frequency VOD combination by applying a Principal Component Regression (PCR) to Forest biomass. Results show that L-band VOD captures the 84% of the biomass and the PCR is built, mainly, for this band meaning that higher frequencies do not contribute substantially to the AGB retrieval in this study, still they do not affect the retrievals negatively.

Unaffected by the atmospheric conditions and solar illumination, L-band emission and scattering are sensitive to vegetation water content and can be used to estimate crop yield. However, for rice which has an inundated period during its growing cycle, the current methods do not work due to the water under the crops. In this paper, we propose to use Global Navigation Satellite System Reflectometry (GNSS-R) signals to find how the water in rice field influence vegetation optical depth (VOD) which had been recently used to estimate the crop yield. Soil moisture (SM) and VOD in Thailand rice fields are compared to signal to noise ratio (SNR) from CYGNSS. Good correlation among them has been found. Results indicate that GNSS-R signals can be used to flag the presence of water and develop an adapted VOD algorithm that can be used to improve the estimation of rice yields.

Mapping carbon stocks in the tropics is essential for climate change mitigation. Passive microwave remote sensing allows estimating carbon from deep canopy layers through the Vegetation Optical Depth (VOD) parameter. Although their spatial resolution is coarser than that of optical vegetation indices or airborne Lidar data, microwaves present a higher penetration capacity at low frequencies (L-band) and avoid cloud masking. This work compares the relationships of airborne carbon maps in Central and South America with both (i) SMAP L-band VOD at 9 km gridding and (ii) MODIS Enhanced Vegetation Index (EVI). Models to estimate carbon stocks are built from these two satellite-derived variables. Results show that L-band VOD has a greater capacity to model carbon variability than EVI. The resulting VOD-derived carbon estimates are further presented at a detailed (9 km) spatial scale.

Global Soil Moisture and Ocean Salinity (SMOS) Level 3 (L3) soil moisture (SM) products are being routinely distributed by the Barcelona Expert Centre (BEC). The quality and accuracy of these SM products have been demonstrated not only by direct validation, but also by its adoption in a wide range of applications. Recently, changes in SMOS Level 2 (L2) SM have led to the reprocessing of the BEC SMOS L3 SM. As in previous versions, a filtering and a weighted binning based on the uncertainty of the SM retrievals by means of the Data Quality Index (DQX) was applied for the L3 production. However, the DQX was modified in the latest L2 release (v650), which could possibly have an influence in the performance of the derived products.This study assesses the impact of the current DQX-based BEC L3 SM quality filtering and binning approach and the possibility of using an alternative strategy based on the chi-squared (χ2) parameter, which is defined as the cost function of the retrieval. The study is performed over continental USA using in situ SM from the U.S. Climate Reference Network (USCRN) as a benchmark. In both approaches, similar results were obtained in terms of correlation and unbiased root mean square difference (ubRMSD). Nevertheless, the χ2-based L3 SM is in general slightly wetter and has a lower dry bias than the DQX-based L3 SM. Further assessments will be performed to stablish the optimal filtering/binning of BEC SMOS L3 SM products.

Mosquitoes are the most important vectors of some human diseases in tropical countries. In particular, Aedes ægypti is the main vector for Chikungunya, Dengue, and Zika viruses in Brazil (Scavuzzo, 2018). The infection causes flu-like symptoms, and sometimes evolves into a life-threatening condition called severe dengue or hemorrhagic dengue. It is a widespread infection that occurs in all regions of the planet's tropical climate, and now because of the climate change, it may expand to some other regions. In recent years, transmission has increased predominantly in urban areas and has become a major public health problem.Recently, there has been an increasing trend on GeoHealth studies by use of remotely sensed data for mapping health risk and monitoring vector-borne diseases. Thus, the aim of this work is to generate risk maps integrating those environmental and climate indicators with adaptive capacity factors to define regions with high risk of vector-borne diseases in Brazil. Consequently, we present a multi-indicator study, similar to the one already developed in Vietnam by Nguyen and Liou (Nguyen and Liou, 2018) for the Aedes Albopictus, but the present study is in Brazil for the Aedes ægypti and adding the information of the Soil Moisture (SM) from the Barcelona Expert Center (BEC). Then, the study combines climatological data (e.g. soil moisture, temperature and precipitation), and demographic and socioeconomic data over the Brazilian territory for the 2013-2018 period. Dengue episodes distribution data for this period have been obtained from the Notifiable Diseases Information System (SINAN), developed by Ministry of Health.

Global change is increasing the risk of forest decline worldwide, impacting carbon and water cycles. Hence, there is an urgent need for predicting forest decline occurrence. To that purpose, this study links forest decline events in Catalonia, detected by the DEBOSCAT forest monitoring program, with information from the Soil Moisture and Ocean Salinity (SMOS) satellite. Firstly, this study reviews the role of the SMOS soil moisture in a previous forest decline episode occurred in 2012, where the authors concluded that dry soils increased the probability of observing decline in broadleaved forests. Secondly, the present study detects that forest decline in 2012 and 2016 was linked to very dry soil conditions (generally with SM3·m-3). A similar analysis is proposed using SMOS Vegetation Optical Depth (VOD) data, which is a proxy of vegetation hydric status. Results and preliminary models will be presented at IGARSS 2018.

Vegetation Optical Depth (VOD) at L-band is highly sensitive to the water content and above-ground biomass of vegetation. Hence, it has great potential for monitoring crop phenology and for providing crop yield forecasts. Recently, the Multi-Temporal Dual Channel Algorithm (MT -DCA) has been proposed to retrieve L-band VOD from Soil Moisture Active Passive (SMAP) measurements. In previous research, SMAP VOD has been compared to crop phenology and has been used to derive crop yield estimates. Here, we review and expand these initial research studies. In particular, we quantify the capability of VOD to detect different crop stages, and test different VOD metrics (i.e., maximum, range and integrals of VOD) to provide crop yield estimates in the United States Corn Belt. Results show that VOD captures 50% to 70% of crop changes during growing and maturing phases, and that it explains between 44% (in heterogeneous crop regions) and 74% (in homogenous croplands) of final crop yields.

After more than 8 years in orbit the Soil Moisture and Ocean Salinity (SMOS) satellite is still in good health and several algorithms for improving its spatial resolution have been proposed and validated in a variety of catchments. However, none of them has yet been applied at the global scale. In this article we present: i) a review of the latest SMOS-BEC downscaling algorithm, which allows for its global application using an adaptive moving window and ii) a thorough validation of the resulting maps over two in-situ networks: REMEDHUS in Spain and OzNet in Australia. The proposed algorithm combines SMOS brightness temperatures (at ~40 km spatial resolution), and MODIS-derived Land Surface Temperature and Normalized Differenced Vegetation Index (at 1 km), into 1km soil moisture maps. This paper also presents a variant of the algorithm, which allows for cloud-free retrievals. A statistical comparison has been carried out when the MODIS Land Surface Temperature is replaced in the algorithm by the one provided by the ERA5 reanalysis. Fine-scale estimates show good agreement in terms of correlation and root-mean-squared error with in-situ soil moisture.

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.

The ESA's SMOS and the NASA's 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

The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first 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 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.

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.

Forests partially reduce climate change impact but, at the same time, this climate forcing threatens forest's 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 ESA's 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.