Soil Moisture Ocean Salinity (SMOS) mission was the first L-band radiometer launched in 2010 and is operational for retrieving global scale soil moisture and Vegetation Optical Depth (VOD). SMOS-INRA-CESBIO (SMOS-IC) version-2 is the latest retrieval algorithm for SMOS radiometers that outperforms existing SMOS retrieval algorithms. Research is underway to enhance the SMOS-IC product by improving surface roughness information that influences soil moisture and VOD retrievals. In the present study, we developed a new global parameterization of soil roughness using SMOS-IC retrievals. For this purpose, we retrieved the soil moisture and surface roughness (through the Hr parameter) values over bare soils using the SMOS-IC algorithm. A Random Forest (RF) model was trained with soil textural and terrain properties as inputs (explanatory variables) to model Hr over bare soils. Later, we extrapolated the Hr values obtained over bare soils to a global scale using the RF model. The newly calibrated Hr values were further used in the SMOS-IC algorithm for soil moisture and VOD retrievals (SMOS-IC v2.1). The SMOS-IC v2.1, the soil moisture product, is wetter than the original SMOS-IC v2 product and has improved performance compared to in situ ISMN soil moisture and modeled ECMWF soil moisture data sets. Regarding VOD, the SMOS-IC v2.1 VOD product showed improved spatial correlation with the reference aboveground biomass product. In addition, SMOS-IC v2.1 VOD product showed improved temporal correlation with MODIS NDVI over low-to-moderate vegetated regions.

Between 2018 and 2022, INRAE Bordeaux (IB) has developed a series of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) retrieval products from SMOS and SMAP, which are currently the only two operational L-band passive microwave satellite missions. These IB products rely on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information. The products are found to be accurate, and very well-suited for application in hydrology, agriculture, climate and vegetation monitoring. In this communication, we present an overview of the development, evaluation and new applications of these IB SM or L-VOD products.

Interannual variability in biomass represented by SMOS vegetation optical depth (VOD) and precipitation was assessed over the Contiguous United States. The greatest interannual variability in both VOD and precipitation occurred in shrubs and herbaceous (grasslands), with forests the least variable. At a continental scale, VOD was strongly correlated with annual precipitation. Results showed a significant correlation coefficient (∼ 0.93) between interannual variability of precipitation and biomass, indicating that the interannual variability of precipitation could be a good predictor of the interannual variability of biomass.

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture (SM) and vegetation optical depth (VOD), which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 11-year global dataset and SMAP, launched beginning of 2015. To ensure SM and L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors, we developed a consistent brightness temperature (TB) record by first producing consistent 40° SMOS and SMAP TB estimates based on SMOS-IC and SMAP enhanced data resp., and then fusing them via linear fusion method. We found that SMOS and SMAP TB are strongly correlated (R > 0.90 over most of the globe) but present a small bias at both the horizontal and vertical polarizations. The preliminary evaluation results show that this bias can be adjusted using a linear fit, but further evaluation procedures are still needed. In the near future, we will develop a long-term time series of SM and L-VOD products based on this merged SMOS-SMAP TB record.

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass$(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$, which is better than V16 VOD$(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$and VODCA VOD$(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$. Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

Assessment of the model-based surface soil temperature (ST) products is very important for hydrometeorological and ecological applications, as well as model refinements. Distinguished from previous regional validations using only in situ observations from sparse networks, this study focused on the evaluation of model-based ST products by considering ground observations from 15 dense networks worldwide from April 2015 to December 2017 covering a wide range of ground conditions. Four model-based ST products were selected for the assessment, including the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the Goddard Earth Observing System Model version 5 Forward Processing (GEOS-5 FP), the ERA-Interim and its successor, the newly developed ERA5. The results indicate the GEOS-5 ST product slightly outperforms other ST products by showing an averaged ubRMSD of 1.84 K. All model-based ST products underestimate in situ ST with a negative bias. All four model-based ST products are demonstrated to well capture the temporal trends of ground observations with very promising$R$values larger than 0.97. The ERA5 shows visible improvements compared to its predecessor ERA-Interim by exhibiting smaller ubRMSD, absolute bias and larger$R$values. These findings are expected to provide useful suggestions for the enhancement and specific usage of the model-based ST products.

Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as an input to the X-MEB inversion. At a first step, we produced global IB X-VOD in year 2015 using the parameters (soil roughness and effective scattering albedo) calibrated in the African continent and evaluated the retrieved X-VOD with three vegetation parameters including Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). The evaluation results indicate X-MEB model has a great potential for global VOD retrievals from AMSR2 satellite data.

Vegetation optical depth (VOD) is used to parameterize microwave extinction effects within the vegetation layer. Many studies have showed VOD presents interesting features for applications in ecology, water and carbon cycles, and VOD is only marginally impacted by signal disturbances and artefacts from atmospheric, cloud and sun illumination effects. As soil moisture (and not VOD) has generally been the main factor of interest in retrieval studies from microwave observations, there is room for improvement in the retrieved VOD products. In this context, INRAE Bordeaux recently developed alternate VOD products from the SMOS, AMSR2 and ASCAT sensors, by addressing specifically the ill-posed problem of retrieving both SM and VOD from observations which may be strongly cross-correlated. Promising results were obtained particularly in terms of spatial correlation of these alternate VOD indices with biomass.

Since the first version of the SMOS-IC retrieval product was released in 2017, its soil moisture (SM) and L-band Vegetation Optical depth (VOD) retrievals have proven to be a very interesting alternative product for the SMOS mission. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which is independent of auxiliary data, a key feature making it well-suited for application in hydrology, agriculture, climate, and carbon cycle. This paper describes the development and validation of the most recent SMOS-IC version (V2) soil moisture product. Compared with the previous version (V105), a new constraint was applied on VOD in the cost function which is minimized in the retrieval process. Soil moisture retrievals from SMOS-IC V2 & V105 were inter-compared against the “European Centre for Medium-Range Weather Forecasts” (ECMWF) modelled SM and the “International Soil Moisture Network” (ISMN) in-situ measurements during 2011-2017 over France. It was found that the average retrieval uncertainty of the new version product was lower than that of the old version, particularly when vegetation density increased. The new version of the SMOS-IC soil moisture product will be made available to the public through the CATDS (Centre Aval de Traitements des Données SMOS) website.

Vegetation Optical Depth (VOD) plays an important role in monitoring the earth ecosystems. There are many VOD products released based on different satellites and frequencies. But most of the VOD products are derived from passive microwave data, and very few active VOD products have been released to date. This study investigated retrievals of the active microwave VOD product from C-band ASCAT (Advanced SCATterometer) observations using the water cloud model in large areas. To achieve this, the ASCAT backscatter data and ECMWF soil moisture data were used as inputs to retrieve ASCAT VOD over the whole Africa. The correlation between the retrieved VOD product and proxies of vegetation density (Saatchi biomass) were used to evaluate the model performance.

Decade years of efforts on the retrieval of soil moisture based on radiative transfer model have largely improved the accuracy of soil moisture (SM). This paper focus on the other parameter, namely vegetation optical depth (VOD). We retrieved X-band VOD from AMSR-E and AMSR2 observations by inverting the L-MEB model (Wigneron et al. 2007 [1]) at X-band, considering that SM was known. As SM input to the L-MEB inversion we used the ECMWF SM product. This step avoids correlation between VOD and SM retrievals from the mono-angular AMSR-E observations. In a first step we evaluated the retrieved VOD with the Copernicus Global Land Service (CGLS) LAI. The evaluation results indicate our model has a great potential for VOD retrievals from AMSR-E/2 satellite data.

Near-real-time monitoring live fuel moisture content (LFMC) from remote sensing is paramount to wildfire early management at a large scale since LFMC is a critical variable in affecting fire ignition and fire spread rate. The geostationary satellite Himawari-8 observes the land surface every 10 minutes, making near-real-time LFMC retrieval achievable. To this end, the potential of Himawari-8 data for LFMC retrieval using the radiative transfer model was explored in this study. The performance of retrieved LFMC was validated using 16 LFMC samplings located in Australia involving two land cover types: croplands and tree cover lands. Additionally, the MODIS data was also applied and compared for the LFMC retrieval. The results showed that Himawati-8 data performed poor accuracy level with R2 and RMSE of 0.26 and 42.16%, respectively. Whereas better accuracy level was found for MODIS data, R2 and RMSE were 0.67 and 29.17%, respectively. This result indicated that the LFMC estimated from Himawari-8 is challenged. Detailed fieldwork and methodology improvements adopted for this data are needed for improving the LFMC estimate in the future.

Fuel Moisture Content (FMC) is a key indicator for assessing wildfire risk and fire spread rate since fire normally broke out under low FMC condition. However, how the FMC driving wildfire activities is still unclear. To understand the relation between FMC and fire occurrence, this study focused on analyzing the FMC retrieved from Radiative Transfer Model (RTM) and historical fires extracted from MODIS Burned Area (BA) product MCD64A1 to determine critical FMC thresholds for wildfire occurrences in southwest China. The results indicated that there were three significant FMC thresholds for forest: 138.9%, 119.0% and 53.1%, and three thresholds for grassland: 138.3%, 70.2%, and 10.6% that associated with the occurrence of wildfires. Large fires and high frequency of fires became vulnerable when FMC fell below those thresholds.

Fuel moisture content (FMC) of vegetation canopy is a critical variable in affecting wildfire behavior. Methodologies based on multiple sources of remote sensing data have shown a prominent advantage for spatial and temporal FMC mapping. However, there is no study focused on FMC retrieval using the Sentinel-2A satellite data to date. This study is to evaluate the performance of this data for FMC retrieval under the framework of the multiple coupled radiative transfer models. Due to the limited field measurements and discontinuous satellite data, only 15 field measurements from USA, South Africa, Australia and France were available for the validation of the retrieved FMC. Results show that the retrieved FMCs were promising with R2= 0.64 and RMSE = 47.16%, which demonstrated the potential usage of the Sentinel-2A data for FMC mapping and further applications for early-warning of wildfire risk.

Fuel moisture content (FMC) is a critical variable in assessing wildfire risk and its behavior. Previous studies normally focused on the methodologies based on optical remote sensing data for FMC retrieval. However, active microwave technique, which processes the advantage of high sensitivity to surface moisture, all-weather and all-time work capability and strong penetrability, attracted more attention in surface parameter monitoring, particularly for the polarimetric SAR which provides more sufficient object scatter characteristic. In this paper, we retrieved the FMC for a grassland based on the multiple linear regression analysis of polarimetric decomposition parameters from Radarsat-2 data. The results show that the correlation coefficient (R) and root mean square error (RMSE) reached to 0.658 and 30.319% when compared to the measured FMC. Finally, the presented method was used for spatial and temporal mapping of FMC in the target study area.

Spatiotemporal simulation of Fractional Vegetation Coverage (FVC) is of great significance for the protection and management of the ecological environment. In this study, the growing season FVCs of the South Qilian Mountains from 2000 to 2017 were extracted from the MODIS vegetation indices product (MOD13Q1), and then that were used to train the Back Propagation (BP) artificial neural network to estimate the annual FVCs of the South Qilian Mountains in the next 7 years (2019-2025). The results show that the established model has a good performance through verification FVC data in 2018, with the coefficient of determination (R2) is 0.9462 and the root mean square error (RMSE) is 0.0118. The simulation results of the model indicate the FVC will present a trend of growth in the following years. This study indicates that the combination of BP neural network and remote sensing data can effectively simulate the spatiotemporal pattern of fractional vegetation coverage, which can further contribute to the environmental protection.

In this study, the burn severity of several wildfires ignited at northern Australian tropical savannas area were estimated using the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) satellite data. To alleviate the spectral confusion between severe (SV) and not-severe (NSV) burnt levels caused by sparse tree distribution, the MODIS Vegetation Continuous Fields (VCF) tree cover percentage data was used to constrain the inversion. The results showed that the accuracy of burn severity estimation significantly improves when considering the tree coverage, with overall accuracy for two study sites increasing from 65% to 81% and kappa coefficient from 0.35 to 0.55. Future work will focus on extending the methodology to other ecosystems.

Fuel biomass burning plays an important role in shaping many ecosystems worldwide and produces gaseous emissions which ultimately alter global climatic processes. As for grass, we assumed the fuel biomass could be approximately estimated from the total dry weight of aboveground grass live and dead organs. Remote sensing techniques adapted methods for estimating the aboveground biomass (the live organs) were wildly explored, yet limited studies used the remote sensing technique to estimate the dry weight of aboveground dead organs. For this end, a method of assimilating leaf area index (LAI) derived from radiative transfer model into the WOrld FOod STudies (WOFOST) model was presented to simultaneously estimate the total dry weight of aboveground grass live and dead organs (i.e., the grass fuel biomass). Validation between measured and estimated fuel biomass showed that the estimated fuel biomass presents a reasonable accuracy with the R2= 0.77 and the RMSE = 223.07 gm-2.

Tianjin city, China has seen rapid urban expansion and economy development especially since the Chinese reform and opening up in 1978. Understanding their internal relationship is important for urban management. Considering of the long-history records with medium spatial resolution (30 meters), Landsat time series (LTS) has become a key remote sensing dataset for monitoring urban changes. As the impervious surface is an important indicator for assessing urban environment, in this study, we extracted the impervious surface maps in Tianjin between 1990 and 2017 based on LTS using a Continue Change Detection and Classification (CCDC) algorithm. On the other hand, we quantitatively explored the relationships between the impervious surface and Gross National Product (GDP). Results show that the impervious surface expansion in Tianjin experienced two stages between 1990 and 2017 with the area increased by 521.95 km2, and the correlation coefficient between the area of impervious surface and GDP value was as high as 0.9718.

Fire Spread Rate (FSR) is one of the key factors for fire rescue and prevention. Remote sensing images have an advantage of acquiring intuitive information timely. To achieve extracting real-time FSR from remote sensing data, a method based on the movement rate of burned area centroid is presented in this study. The FSR extracted from geostationary Himawari-8 (H-8) data in two bushfires outbroke in Esperance, Western Australia in 2015. And the FSR estimated from CSIRO (Commonwealth Scientific and Industrial Research Organization) Grassland Fire Spread Model (CGFSM) were set as the benchmark to assess the presented approach. The results illustrated that the proposed method yield a promising accuracy by comparing with the FSR from CGFSM in that two fires, with the coefficient of determination (R2) reaches to 0.76 and root-mean-square error (RMSE) is 0.50 m·s-1. Furthermore, this study provides a potential application of geostationary satellite in extracting real-time wildfire behavior.

Fuel moisture content (FMC) is a critical factor in assessing wildfire risk and its behaviour. Traditional field measurement of this variable is time-consuming and is impossible to extend to large-scale and dynamic applications. The canopy water has strong absorption characteristic in near and shortwave infrared spectra, allowing the near-real-time, multi-temporal and -spatial estimation of the FMC from remotely sensed data available. During last decade, numerous statistic- or physical model-based studies were carried out for the estimation of this variable. As FMC is responsive to weather variations, diurnal determination of this variable is essential for wildfire early-warning. With the launch of Himawari-8 in 2014, 10 mins images are available from this satellite, making real-time retrieval of the FMC achievable. Thus, this is the first study to retrieve diurnal FMC from Himawari-8 images, with the purpose for real-time wildfire risk assessment in near future.

This study focuses on the forest fire risk assessing using entirely remote sensing derived variables. These variables include Fuel moisture content (FMC), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Elevation and Slope. The Difference and Anomaly of FMC in time series are also calculated since FMC is one of the critical factors in assessing the wildfire risk. The logistic regression model is used to integrate all the variables in the fire occurred and none-occurred areas to derive the Fire Risk Index (FRI). A case study of the above methodology is applied to assess the FRI in Yunnan Province in China. The result shows that the AUC is to 0.8 for grassland and 0.81 for woodland, which indicates the good performance of the presented methodology in assessing forest fire risk.