Teachers use visual and verbal cues in instructional videos to guide attention and support learning. While previous studies have examined different cueing designs, the effects of combining visual and verbal cues (dual-modality cues) on learning are still unclear. This study investigated whether verbal cues characterized by prosodic elements, including pitch, pauses, and stress, could diminish the benefits of visual cues emphasized by color and bold text. Using a 2 (visual cues: without vs. with) × 2 (verbal cues: without vs. with) between-subjects experimental design, we collected eye-tracking data from 128 undergraduate participants. The results showed that the dual-modality cues resulted in shorter fixation durations and frequencies during text processing, increased extraneous load, and decreased germane load, leading to poorer learning performance. These findings partially confirm the hypothesis concerning the interference effect between cue modalities and highlight the advantages of a single-modal cue for video learning.

Existing research indicates that original federated learning is not absolutely secure; attackers can infer the original training data based on reconstructed gradient information. Therefore, we will further investigate methods to protect data privacy and prevent adversaries from reconstructing sensitive training samples from shared gradients. To achieve this, we propose a defense strategy called SLGD, which enhances model robustness by combining sparse learning and gradient perturbation techniques. The core idea of this approach consists of two parts. First, before processing training data at the RSU, we preprocess the data using sparse techniques to reduce data transmission and compress data size. Second, the strategy extracts feature representations from the model and performs gradient filtering based on the l 2 norm of this layer. Selected gradient values are then perturbed using Von Mises–Fisher (VMF) distribution to obfuscate gradient information, thereby defending against gradient reconstruction attacks and ensuring model security. Finally, we validate the effectiveness and superiority of the proposed method across different datasets and attack scenarios.

The spatial representativeness error of in situ soil moisture (SM) is recognized as a major source of uncertainty when validating satellite SM products with a spatial resolution of tens of kilometers. Site underrepresentation is primarily caused by environmental heterogeneity, but their relationship remains poorly understood. Here, we assessed the spatial representativeness of in situ SM from 322 strictly screened stations worldwide relative to coarse-resolution (~0.25°) satellite footprint based on the extended triple collocation (ETC) method. We then evaluated the influence of the heterogeneity of four environmental factors (soil texture, land cover types, elevation, and vegetation coverage) on site representativeness. Moreover, we calculated SM variability within the satellite footprint based on 1-km SM data to explore its relationship with environmental heterogeneity. Results indicate that about 63% of the sites have relatively good spatial representativeness (ETC-derived correlation coefficient$\ge 0.7$). Soil texture and land cover exhibit greater heterogeneity across the mid and high latitudes of the Northern Hemisphere. The larger heterogeneity in elevation and vegetation coverage is primarily found in regions with significant ridges and dense vegetation, respectively. Land cover is the major factor influencing the spatial representativeness of SM sites, and the increase in the heterogeneity of land cover enhances SM variability, which negatively impacts site representativeness. The in situ SM can be more representative when the proportion of the land cover type where the site is located is higher or when there are fewer land cover types within the satellite footprint. Moreover, it is found that the newly proposed metric of the similar area ratio of sites, as a measure of land cover heterogeneity, can effectively reflect SM variability. This metric can also serve as a supplementary criterion for selecting representative sites, particularly in situations where sites are sparse and the ETC method is inapplicable. These findings provide useful references for robust evaluation of satellite SM products based on in situ measurements (e.g., in situ SM upscaling and SM site deployment).

The uncertainty inherent in validating satellite-derived soil moisture (SM) products is significantly attributed to the spatial underrepresentation of in situ SM measurements. The main reason for this phenomenon is the varying environmental conditions (called as environmental heterogeneity) within the satellite footprint. To better understand this issue, we assessed the spatial representativeness of in situ SM from 383 strictly screened stations worldwide relative to the coarse-resolution (~0.25°) satellite footprint and analyzed the effects of four environmental factors (i.e., soil texture, land cover, elevation, and vegetation coverage) using the extended triple collocation (ETC) technique. Results show about 63% of the sites have satisfactory levels of spatial representativeness (ETC derived correlation coefficient ⩾0.7). Land cover is the foremost factor affecting the spatial representativeness of SM sites. The in situ SM can better represent the true variability of SM when the proportion of land cover types where the site is located is higher or there are fewer land cover types within the satellite pixels.

The spatial resolution of existing satellite soil moisture products is very coarse (~25 km), and thus there is usually significant spatial heterogeneity in the land surface covered by satellite footprints. However, the effects of spatial heterogeneity on satellite soil moisture products are largely under-studied previously. The study firstly evaluated seven satellite soil moisture products comprising SMAP (DCA, IB, and MTDCA), SMOS-IC, AMSR2 (LPRM and JAXA) and FY-3C at a global scale using the extended triple collocation (ETC) method. Then, the skills of these products under a wide range of surface heterogeneity including heterogeneity in vegetation coverage, terrain, land cover, and soil texture were ascertained. The results indicate: (1) SMAP-IB and SMAP DCA products generally outperform others, followed by SMOS-IC and SMAP MTDCA which also exhibit satisfactory performance; (2) heterogeneity in vegetation coverage, terrain, and land cover generally decreases the R value of satellite soil moisture products, while heterogeneity in soil texture has an insignificant effect on product skills; (3) L-band products demonstrate greater stability compared to C/X-band datasets across various surface heterogeneity.

Satellite soil moisture products have great potential for many applications, such as drought monitoring and landslide warning. However, these applications often require accurate and continuous soil moisture records, and the missing values in satellite soil moisture datasets often hamper the usefulness of these products for such applications. This study firstly proposed and compared three approaches, i.e., linear regression, linear rescaling, and random forest to fill the missing values in the SMAP soil moisture products in both temporal and spatial dimensions, based on the seamless ERA5 data from 2016 to 2019. Then, a total of twelve auxiliary data were incorporated into the training datasets of random forest to improve the accuracy of gap-filled SMAP data. Finally, the gap-filled SMAP data were compared with the original SMAP data and validated by in situ measurements from 1071 sites worldwide. The results indicate: 1) when using only the ERA5 datasets, the random forest performs better than linear regression and linear rescaling methods in the training phase, but its skill degrades noticeably in the validation phase; 2) by adding the auxiliary data, the performance of random forest improves significantly in the validation phase; 3) the gap-filled SMAP data maintain or even exceed the accuracy of the original SMAP soil moisture, demonstrating the effectiveness of the proposed gap-filling method.

With the rapid development of decentralized financial (DeFi), the total value locked (TVL) in DeFi continues to increase. A big number of adversaries exploit logic vulnerabilities to attack DeFi applications for profit, such as flash loan attacks and price manipulation attacks. However, the current vulnerability detection tools for smart contracts cannot be directly used to detect the logic vulnerabilities generated by the combination of different protocols. How to characterize and detect DeFi attacks that exploited logic vulnerabilities is a big challenge. In this work, we propose a deep-learning-based attack detection system on DeFi, called DeFiScanner, in which we design a novel neural network that includes a global model, a local model, and a fusion model to characterize DeFi attacks. First, the unstructured emitted events are automatically and efficiently normalized. Second, the transaction-related features of normalized emitted events are enriched with the global model and the semantic features of emitted events are extracted with the local model. Finally, the transaction-related features and the semantic features of emitted events are fused efficiently with the fusion model to detect DeFi attacks. We collect a dataset that consists of 50 910 real-world DeFi transactions on Ethereum (ETH). The extensive experimental results demonstrate the effectiveness of DeFiScanner. The true positive rate (TPR) and the area under the receiver operating characteristic (ROC) curve of the system reach 0.91 and 0.97, respectively.

The comprehensive and robust assessment of diverse global-scale satellite-based soil moisture products from various satellite data sources (e.g., different frequencies and incidence angles) and retrieval algorithms is essential for the refinements as well as applications of these products. To date, soil moisture retrieval algorithms and products are rapidly evolving and their updated iterations are ongoing. In support of the validation activities of recently developed/reprocessed satellite soil moisture products, the study firstly assessed eight commonly-employed satellite soil moisture datasets comprising SMAP (DCA, IB, and MTDCA), SMOS-IC, AMSR2 (LPRM and JAXA), FY-3C, and ESA CCI on a global scale using three different strategies, i.e., ERA5 reanalysis soil moisture dataset with similar spatial resolution to satellite products,in situmeasurements from densely-instrumented networks worldwide with mitigated spatial mismatch between ground site and satellite pixel, and the Extended Triple Collocation (ETC) method that can obtain error indicators relative to ground truth. The skills of these products under a broad range of vegetation density, land cover and climate types, and surface heterogeneity (heterogeneity in terrain, land cover, soil texture, and vegetation coverage) were also examined. The results indicate: (1) different soil moisture products show overall consistency in skill ranking under three different evaluation strategies, except for SMAP DCA, SMAP-IB, and SMAP MTDCA in terms ofRvalue; (2) ESA CCI, SMAP-IB, SMAP DCA products generally perform better than the others under three strategies, and SMOS-IC and SMAP MTDCA also show satisfactory performance concerning ubRMSD andRvalues; (3) vegetation density exerts visible influences on satellite soil moisture datasets. Specifically, the C/X-band (AMSR2 and FY-3C) and L-band (SMAP and SMOS) products display the optimal skills under sparse and moderate vegetation coverage respectively, and the impacts of vegetation density on C/X-band products are evidently stronger than those on L-band datasets. The errors of satellite soil moisture data also increase as the increase of heterogeneity in terrain, land cover, and vegetation coverage, while the effect of heterogeneity in soil texture on the skill of satellite soil moisture products is insignificant; (4) the skills of L-band products are more stable than those of C/X-band datasets under different ground conditions.

Climatology (mean seasonal cycle) often dominates the errors in satellite soil moisture (SM) products, which is highly essential for the water-carbon cycle in tropical rainforests. Although microwave observations at L-band are expected to provide more accurate SM information benefiting from their stronger penetration capacity, the SM mapping in rainforests by L-band measurements is still challenging and is less investigated by the community. To bridge the research gap, five L-band satellite SM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, MTDCA and IB were assessed by using the FLUXNET SM data in tropical rainforests. To cope with the time inconsistence between satellite and ground data, the SM climatology variations in rainforests for diverse time periods were compared using long-term ERA5 SM. The results indicate the SM climatology is relatively stable over different time periods during 2001-2020 for rainforest sites. Based on the stability of SM climatology, L-band SM products were demonstrated to satisfactorily capture the SM climatology variations in rainforests, especially for SMOS-IC and SMAP-IB. The results are expected to provide guidelines for the hydro-ecological applications using satellite SM products in tropical rainforests.

Blending both active and passive microwave measurements are expected to provide more robust surface soil moisture (SSM) estimations over various environmental conditions compared to that from the single sensor. The integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) with the similar observation scale active (i.e., the Advanced Scatterometer, ASCAT) sensors provides a considerable opportunity to improve the accuracy of SSM mapping, which however is rarely investigated to date. In this study, we implemented the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficient (σ) for estimating SSM using the machine learning approach, by fully considering the error sources in physically-based retrieval approaches (e.g., τ–ω model). The independent validation results using ground data from 14 dense networks show the integration of SMAP and ASCAT measurements can satisfactorily achieve better SSM retrievals compared to ASCAT SSM, SMAP-SCA-V SSM and ESA CCI SSM products, with the lowest ubRMSE of 0.042 m3/m3and the highest R of 0.76. This study is expected to enrich the understanding of SSM retrieval from active and passive microwave satellites, and provide SSM product with higher accuracy for eco-hydrological applications.

Soil moisture (SM) is influenced by changes in meteorological elements and vegetation, as well as by environmental heterogeneity. Due to the prevalence of extreme events in the past two decades, this complexity is growing in the 21st century. In this study, the global spatiotemporal trend of SM and its possible influencing factors were investigated by using the satellite-based ESA CCI SM from 2000-2021. The results reveal global SM generally declines at a rate of -1×10-4m3m-3yr-1, dominated by a drying trend in the southern hemisphere. From a global perspective, the driving force of precipitation and vegetation on SM fluctuation is stronger than that of temperature. Different environmental variables (e.g., land cover, soil texture, and terrain) have different regulatory effects on SM changes. In contrast to other types, there are general wetting trends of SM in croplands, savannas, forests, loam soils, and high elevations (> 1000 m). The response of SM to temperature and vegetation is greatly limited under barren and forests, respectively. The impact of temperature on SM is enhanced with increasing clay content or decreasing sand content. The high positive correlation between precipitation and SM is rarely influenced by environmental factors compared to temperature and vegetation.

Estimation of surface soil temperature (ST) using passive microwave remote sensing is still a great challenge, especially under diverse land surface conditions. The microwave radiation imager (MWRI) on board Fengyun (FY)-3D satellite is the latest Chinese FY-3 series multiband microwave sensor in orbit, providing a valuable opportunity for estimating surface ST on a global scale. In this study, the FY-3D brightness temperature from 10.7 to 36.5 GHz and the topsoil temperature simulations from ERA5-Land and GEOS-FP from 2019 to 2020 were used to build the Multi-Band Temperature Retrieval Model (MBTRM). The universal global optimization algorithm was employed to establish the MBTRM on a per-pixel basis globally. Ground measurements from a total of 1221 sites worldwide were adopted to fully examine the performance of the proposed MBTRM. Moreover, four widely used passive microwave-based ST models/products were compared. The results reveal the proposed MBTRM performs the best with an averaged root mean square difference (RMSD) of 3.03 K and 3.79 K at descending and ascending overpass respectively and a correlation coefficient larger than 0.9. The advantage of the newly developed MBTRM is that it is user friendly (with explicit formula) and can be applied globally with satisfactory accuracy, and provides a good supplement to optical satellite based temperature products.

Data reconstruction attack has become an emerging privacy threat to Federal Learning (FL), inspiring a rethinking of FL's ability to protect privacy. While existing data reconstruction attacks have shown some effective performance, prior arts rely on different strong assumptions to guide the reconstruction process. In this work, we propose a novel Conditional Generative Instance Reconstruction Attack (CGIR attack) that drops all these assumptions. Specifically, we propose a batch label inference attack in non-IID FL scenarios, where multiple images can share the same labels. Based on the inferred labels, we conduct a “coarse-to-fine” image reconstruction process that provides a stable and effective data reconstruction. In addition, we equip the generator with a label condition restriction so that the contents and the labels of the reconstructed images are consistent. Our extensive evaluation results on two model architectures and five image datasets show that without the auxiliary assumptions, the CGIR attack outperforms the prior arts, even for complex datasets, deep models, and large batch sizes. Furthermore, we evaluate several existing defense methods. The experimental results suggest that pruning gradients can be used as a strategy to mitigate privacy risks in FL if a model tolerates a slight accuracy loss.

The Fengyun (FY)-3D satellite launched in November 2017, provides the latest multi-frequency brightness temperature (TB) of Chinese FY-3 series satellites. In this study, the FY-3D TB at five frequencies from 10.7 to 89 GHz during 2019 to 2020 were intercalibrated against AMSR2 TB using two intercalibration methods, i.e., the commonly used global linear regression method and the global per-pixel linear regression method proposed in the study. The effects of different environmental variables (e.g., land cover and its heterogeneity, climate types, water body fraction, terrain and vegetation coverage) on the calibration accuracy were investigated. The results reveal the global per-pixel linear regression method performs much better than global linear regression method with an averaged root mean square difference (RMSD) of 2.93 K at ascending overpass and 2.34 K at descending overpass. The water body fraction has the greatest influence on calibration accuracy, followed by terrain, land cover heterogeneity, and vegetation coverage. The RMSD is relatively lower in savannas and barren lands as well as in arid, cold and tropical climatic zones than that in other land cover and climate types.

Usage-based insurance (UBI) provides reasonable vehicle insurance premiums based on vehicle usage and driving behavior. In general, there are three major issues in realizing intelligent UBI systems. First, UBI evaluation mechanisms are not auditable to drivers. Insurers may thus deliberately adjust the UBI premiums. Second, the process of collecting driving data by insurers may lead to serious privacy breaches. Third, forging safer driving data for reducing insurance premiums may cause economic losses for insurers. To address these challenges, in this study, we propose CCUBI, a cross-chain-based premium competition scheme with privacy preservation for intelligent UBI systems. We introduce tamper-resistant blockchain and smart contracts to construct credible insurance mechanisms. The cross-chain technology connects these blockchains in the entire network to form an open premium competition scheme. Vehicle owners can assess designated insurers by sharing historical data with them to get a suitable CCUBI plan. In addition, we propose a data aggregation method used for CCUBI analysis with privacy preservation. Vehicle owners only publish proofs of the driving data. Proofs can still maintain privacy and computability in cross-chain flows. Finally, we adopt roadside units to detect forged driving data. We conduct a detailed security analysis. Experimental results also demonstrate the efficiency of CCUBI.

As the second generation of Chinese polar-orbiting meteorological satellite missions, the Fengyun (FY)-3D satellite provides the latest multi-frequency brightness temperature (TB) of FY-3 series satellites. The microwave radiation imager (MWRI) boarded on FY-3D has similar sensor configuration as Advanced Microwave Scanning Radiometer 2 (AMSR2), and thus the intercalibration of these two sensors can make their TB data more consistent and continuous to facilitate their joint applications. In this study, the FY-3D H-pol and V-pol TB at five frequencies from 10.7 to 89 GHz during 2019 to 2020 were calibrated against AMSR2 TB over land. Two categories of intercalibration methods were compared, including global intercalibration method, i.e., global linear regression, and per-pixel-based intercalibration methods, i.e., per-pixel linear regression joint global linear regression, per-pixel linear regression joint inverse distance interpolation, per-pixel linear regression joint nearest neighbor interpolation, and global per-pixel linear regression. Furthermore, the effects of diverse environmental variables (i.e., land cover and its heterogeneity, climate types, water body fraction, terrain and its complexity, soil texture, and vegetation coverage) on FY-3D calibration accuracy were fully investigated. The results indicate that all five approaches can reduce the bias between FY-3D and AMSR2 TB, and the root-mean-square difference (RMSD) also reduces accordingly. Among them, the global per-pixel linear regression method performs the best with the lowest averaged RMSD of 2.93 K (at ascending overpass) and 2.34 K (at descending overpass), followed by the per-pixel linear regression joint inverse distance interpolation. The global linear regression method performs the worst with the largest RMSD of 4.69 and 3.82 K at ascending and descending overpass, respectively. The RMSD is relatively larger in temperate and polar climate zones, as well as in grasslands and croplands than in other climate and land cover types. The calibration errors generally decrease as the altitude increases, while they increase with the increase in land cover heterogeneity. The water body fraction exerts the greatest impact on the calibration accuracy, and the RMSD reaches 3 K when the water body fraction is greater than 15%. Soil texture, terrain complexity, and vegetation coverage generally have little influence on the calibration accuracy. These findings can provide a good reference for the intercalibration of satellites with similar configuration to generate long-term climate data records.

The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA’s Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active–passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter ($\sigma ^{0}$) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between$\sigma ^{0}$(no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations ($\sigma ^{0}_{\text {hh}}$versus TBh,$\sigma ^{{0}}_{\text {hh}}$versus TBv,$\sigma ^{{0}}_{\text {vv}}$versus TBh, and$\sigma ^{{0}}_{\text {vv}}$versus TBv) with the$\sigma ^{{0}}_{\text {vv}}$and TBhcombination displaying an overall slightly higher correlation. The linear relationship between$\sigma ^{{0}}$and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active–passive joint algorithms for future satellite missions.

The Tibetan Plateau, known as the “Third Pole” of the world, is extremely sensitive to global climate change. Reliable soil moisture information is essential for understanding the impact of the Tibetan Plateau on the Asian monsoon. The study assessed a total of four satellite soil moisture products, namely the SMAP-SCA (V7), ESA CCI (V0.52), AMSR2 LPRM (V001), and FY-3C (V1) over the Tibetan Plateau using three densely-instrumented soil moisture networks (i.e., Maqu, Naqu, and Pali). Moreover, the possible error sources of these products were thoroughly investigated over the Tibetan Plateau, which had not yet been fully explored in previous studies. The results reveal the ESA CCI (V0.52) and SMAP-SCA (V7) generally perform better in the three networks with higher correlation coefficient ($R$) and lower standard deviation of the difference (STDD) compared to other products. The bias of ESA CCI (V0.52) is demonstrated to be dependent on the GLDAS Noah soil moisture, but it correlates much better with soil moisture measurements and also displays lower STDD value than the GLDAS Noah. SMAP brightness temperatures demonstrate much higher sensitivity to soil moisture than the AMSR$2~C$-band and FY-3C$X$-band measurements regardless of vegetation, surface roughness, and land cover heterogeneity. The auxiliary surface temperature used in SMAP-SCA (V7) also performs better than that used in AMSR2 LPRM (V001) and FY-3C (V1) though it is slightly colder than the ground measurements. These factors contribute to the better performance of SMAP-SCA (V7) soil moisture product compared to other satellite datasets. The AMSR2 LPRM (V001) product produces evidently larger absolute values than ground observations, but it can track the temporal trend of soil moisture in sparsely vegetated areas (Naqu and Pali). The FY-3C (V1) product exhibits some abnormal saturation values in Maqu with the highest vegetation biomass, while it performs better in Naqu and Pali with relatively lower vegetation coverage. The surface temperature derived from AMSR2 LPRM and FY-3C shows large uncertainties over the Tibetan Plateau which may be caused by the limited data used to calibrate the empirical surface temperature models.

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.

Soil moisture is an essential variable both in environmental monitoring research and application. With the requirement of high-precision soil moisture data, it is highly necessary to construct in-situ soil moisture sensors Web in high density, in which the economics, complexity, and low-power consumption of sensor Webs should be essentially considered. However, most current existing soil moisture monitoring networks have limitations due to high power consumption, complex architecture, and expensive equipment. These issues are not conducive to high-density soil moisture observation. In view of the existing problems in the current soil moisture in-situ sites, an effective resolution for high-density soil moisture observation is needed. For the first time, we are bringing Narrow-Band Internet of Things (NB-IoT), a low-power Internet-of-Things technology, into geospatial sensor Web for soil moisture observation. Moreover, we built a high-density in-situ soil moisture sensor Web via NB-IoT and compared it with ZigBee at the Baoxie experimental zone in Wuhan, China, there about 1 km2, and we acquired data for up to 12 months. We analyze the acquisition record of battery status, signals, and soil moisture. We analyzed the battery life, the impact of the signal on the battery performance, the signal quality, and data acquisition clearly and intuitively. This unprecedented study and application, we discovered and concluded that the low-power sensor Web of the NB-IoT communication protocol could be suitable for high-density soil moisture observation.