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During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points. Using systematic satellite observations of land surface temperature and soil moisture during soil dry-downs, the spatially-explicit global distribution of the critical soil moisture threshold of plant water stress and its drivers is uncovered. [ABSTRACT FROM AUTHOR]

Furthermore, previous studies generally focused on estimating soil moisture in mineral soils since the soil dielectric models used in soil moisture retrieval algorithms were usually mineral-soil-based models. Therefore, satellite soil moisture products have become increasingly abundant, greatly promoting the various application of satellite soil moisture datasets [[13], [15]]. Soil moisture is an important component of the global terrestrial ecosystem and has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) [[1]]. In Wu and Wen [[26]], the research progress in observing and simulating L-band microwave emissions, ground soil moisture measurements, and soil moisture retrieval from L-band passive microwave observations over the Third Pole, i.e., the Tibetan Plateau, was summarized. [Extracted from the article]

The dominance of vapor pressure deficit (VPD) and soil water content (SWC) for plant water stress is still under debate. These two variables are strongly coupled and influenced by climatic drivers. The impacts of climatic drivers on the relationships between gross primary production (GPP) and water stress from VPD/SWC and the interaction between VPD and SWC are not fully understood. Here, applying statistical methods and extreme gradient boosting models—Shapley additive explanations framework to eddy‐covariance observations from the global FLUXNET2015 data set, we found that the VPD‐GPP relationship was strongly influenced by climatic interactions and that VPD was more important for plant water stress than SWC across most plant functional types when we removed the effect of main climatic drivers, e.g. air temperature, incoming shortwave radiation and wind speed. However, we found no evidence for a significant influence of elevated CO2 on stress alleviation, possibly because of the short duration of the records (approximately one decade). Additionally, the interactive effect between VPD and SWC differed from their individual effect. When SWC was high, the SHAP interaction value of SWC and VPD on GPP was decreased with increasing VPD, but when SWC was low, the trend was the opposite. Additionally, we revealed a threshold effect for VPD stress on GPP loss; above the threshold value, the stress on GPP was flattened off. Our results have important implications for independently identifying VPD and SWC limitations on plant productivity, which is meaningful for capturing the magnitude of ecosystem responses to water stress in dynamic global vegetation models. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), known as the "Third Pole," is a climate-sensitive and ecology-fragile region. In this study, the spatiotemporal trends of soil moisture (SM) and vegetation were analyzed using satellite-based ESA CCI SM and MODIS LAI data, respectively, in the growing season during the last 20 years (2000–19) over the TP covering diverse climate zones. The climatic drivers (precipitation and air temperature) of SM and LAI variations were fully investigated by using both ERA5 reanalysis and observation-based gridded data. The results reveal the TP is generally wetting and significantly greening in the last 20 years. The SM with significant increasing trend accounts for 21.80% (fraction of grid cells) of the TP, and is about twice of the SM with significant decreasing trend (10.19%), while more than half of the TP (58.21%) exhibits significant increasing trend of LAI. Though the responses of SM and LAI to climatic factors are spatially heterogeneous, precipitation is the dominant driver of SM variation with 48.36% (ERA5) and 32.51% (observation-based) precipitation data showing the strongest significant positive partial correlation with SM. Temperature rise largely explains the vegetation greening, though precipitation also plays an important role in vegetation growth in arid and semiarid zones. The combined trend of SM and LAI indicates the TP is mainly composed of wetting and greening areas, followed by drying and greening regions. The change rate of SM is negative at low altitudes and becomes positive as altitude increases, while the LAI value and its change rate decrease as altitude increases. [ABSTRACT FROM AUTHOR]

The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM mapping with improved accuracy. However, this integration remains largely underexplored. In this context, the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficients for global-scale SSM estimation was investigated, by fully considering the potential error sources in conventional radiative transfer models (RTMs) as well as other SSM linked factors. Based on ground measurements from globally distributed dense networks with mitigated mismatch issues and spatial/temporal independent evaluation strategies, this study: (i) comprehensively evaluated four classical machine learning approaches, including Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN), and chose the best performing RF method to implement the final integration of SSM; (ii) compared the integration retrievals to those made using data from a single sensor (SMAP or ASCAT) with the same machine learning framework, as well as to the SMAP passive, ASCAT active, and ESA CCI active-passive combined SSM products. The results show the integration retrievals achieve satisfactory performance by obtaining an averaged unbiased root mean squared error (ubRMSE) of 0.042 m3/m3 and a temporal correlation of 0.756, which are superior to machine learning based SSM estimated from a single active or passive sensor, and also outperform the SMAP, ASCAT, and ESA CCI products. Moreover, the temporal resolution is evidently improved compared to the SMAP and ASCAT SSM products, with a temporal ratio exceeding 60% for most areas across the globe. Therefore, blending active and passive measurements affords a more reliable SSM mapping with increased sampling at the global scale, and could contribute to improved hydro-ecological applications. • SMAP was integrated with ASCAT for global SSM retrieval. • Diverse perturbing factors in RTMs were fully considered. • The integrated SSM showed high skill with increased temporal resolution. • RF exhibited better performance than other machine learning approaches. [ABSTRACT FROM AUTHOR]

Recently, large efforts have been made to develop surface soil moisture (SSM) products based on observations from passive microwave L-band satellites at the global scale. Despite vast previous efforts to assess satellite SSM products based on in situ data, the performance of L-band SSM products remains still little known over the tropical areas including the rainforests. To close this knowledge gap, a comprehensive evaluation of five L-band SSM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, and MTDCA, as well as the recently developed SMAP INRAE-BORDEAUX (SMAP-IB) was conducted in tropical areas from 2015 to 2020. The investigation was implemented by using in situ observations, and expanded by the Triple Collocation Analysis (TCA) and double instrumental variable (IVd) methods. The results revealed that all the five L-band SSM products show a good capacity to estimate SSM in most moderately vegetated areas of the tropical areas while they exhibit some uncertainties in dense vegetation (e.g., rainforests). The SSM climatology of the five L-band products in rainforests agreed generally well with that of in situ measurements especially for SMOS-IC (R = 0.75), followed by SMAP-IB (R = 0.72). Notably, the newly developed SMAP-IB shows satisfactory performance in the tropics with the highest R of 0.73 and the smallest unbiased root mean square difference (ubRMSD) of 0.041 m3/m3 from the study based on in situ SM data, and R of >0.81 from the combined TCA/IVd method in most vegetation conditions. SMAP-DCA SSM demonstrates comparable performance to SMAP-SCA-V (R = 0.66) from the in situ based analysis. This study is expected to deepen our understanding on the skill of L-band SSM products in tropical areas and further promote possible upgrades in algorithms and applications in these regions. • Five L-band SSM products were evaluated over the tropics. • The newly developed SMAP-IB showed satisfying performances. • The combined TCA/IVd and in situ analyses led to consistent results. • L-band SSM shows acceptable climatology results in rainforests. [ABSTRACT FROM AUTHOR]

The comprehensive evaluation of model- and satellite-based surface soil temperature (ST) products is a prerequisite for applications of these datasets in hydrology, ecology, and climate change, as well as in passive microwave soil moisture retrieval algorithms. Distinguished from existing regional validations, this study used ground soil temperature observations of approximately 800 stations from 5 sparse and 15 dense networks worldwide to fully assess six model- and satellite-based surface ST products from April 2015 to December 2017. The products consist of five model-based ST from 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 Global Land Data Assimilation System (GLDAS) Noah, the ERA-Interim and the newly released ERA5 produced by the European Centre for Medium Range Weather Forecasts (ECMWF), and one satellite-based ST retrieved from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by using the Land Parameter Retrieval Model (LPRM). The accuracy of these products was comprehensively assessed per availability of ground networks, soil temperature interval, soil moisture interval, land cover, climate zone, and elevation. The results show that the GEOS-5 exhibits the smallest averaged unbiased root mean square difference (ubRMSD) of 1.84 K among all the surface ST products. All model-based products show a high skill in capturing the temporal trend of ground observations with an averaged correlation coefficient larger than 0.97. The ERA5 surface ST obtains visible improvements compared to its predecessor ERA-Interim by showing smaller ubRMSD and absolute bias values. All model-based surface ST products generally show lower values than ground ST, while their bias tends to be warmer as soil temperature and soil moisture increase except for the highest temperature and moisture conditions. Moreover, unstable performance of most model-based products in shrubland and grassland, tropical, arid and cold climate, and high elevation regions is also demonstrated by larger ubRMSD values. The averaged ubRMSD of the satellite-based LPRM ST is 3.04 K, and more attentions should be paid to the impacts of elevations and underlying surfaces on improving this product. These new findings will be valuable for future refinement of ST datasets, algorithms used to estimate soil moisture from satellite data, and applications in various disciplines. • Six model- and satellite-based ST were assessed from ~800 sites worldwide. • The effect of soil temperature and moisture intervals on ST data was assessed. • ERA5 surface ST shows visible improvements compared to ERA-Interim. • Model-based STs are warmer as ST increases except for the highest ST conditions. • All model-based ST products show high skills with R larger than 0.97. [ABSTRACT FROM AUTHOR]

Comprehensive assessments on the reliability of remotely sensed soil moisture products are undeniably essential for their advancement and application. With the establishment of extensive dense networks across the globe, mismatches between satellite footprints and ground single-point observations can be feasibly relieved. In this study, five remotely sensed soil moisture products, namely, the Soil Moisture Active Passive (SMAP), two Soil Moisture and Ocean Salinity (SMOS) products, the Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2) and the European Space Agency (ESA) Climate Change Initiative (CCI), were systematically investigated by utilizing in-situ soil moisture observations from global dense and sparse networks. Distinguished from previous studies, several perturbing factors comprising the surface temperature, vegetation optical depth (VOD), surface roughness and spatial heterogeneity were taken into account in this investigation. Furthermore, products' skills under various climate regions were also evaluated. Through the results, the SMAP product captures temporal trends of ground soil moisture, exhibiting an averaged R of 0.729, whereas for overall accuracy, ESA CCI outperformed other products with a slightly smaller ubRMSE of 0.041 m3 m−3 and a bias of −0.005 m3 m−3. This complementarity between SMAP and ESA CCI was further demonstrated under different climate conditions and can afford the reference of their integration for a more reliable global soil moisture product. Though some underestimations still exist, the newly developed SMOS- INRA-CESBIO (SMOS-IC) was illustrated to gain considerable upgrades with regard to R and ubRMSE compared to SMOS-L3 product, especially in dense VOD conditions achieving the highest R compared to other products. Generally, the underestimations of the European Centre for Medium-Range-Weather Forecasts (ECMWF) surface temperature used for SMOS under moderate or high VOD, heterogeneity, and most surface roughness conditions were consistent with the underestimations of the soil moisture product and provide the directions of product promotions. As for LPRM surface temperature, the worse skills can partially explain the unsatisfactory performances for LPRM soil moisture products. In spite of relatively acceptable skills of SMAP and SMOS-IC soil moisture products concerning R under moderate or dense VOD, small surface roughness, low heterogeneity conditions and temperate and cold climate types, advances in soil moisture products under high or even slightly low VOD, high roughness or topography complexity and heterogeneity, as well as in tropical or desert regions, remain challenging. It is expected that these findings can contribute to algorithm refinements, product enhancements (e.g., fusion and disaggregation) and hydrometeorological usages. • The impacts of perturbing factors, heterogeneity and climate types were assessed. • SMOS-IC showed better performance concerning R and ubRMSE. • The complementarity between SMAP and ESA CCI was observed. • The underestimation of SMOS surface temperature contributed to the dry bias in SMOS soil moisture products. [ABSTRACT FROM AUTHOR]

Soil moisture is a fundamental ecological component for climate and hydrological studies. However, the distribution patterns of soil moisture are spatially heterogenous and influenced by multiple environmental factors. The knowledge is still limited in assessing the large-scale spatial heterogeneity of soil moisture in in situ data modelling, in situ network design, spatial down-scaling, and remote sensing-based soil moisture retrieval. Heterogeneity models are effective in characterizing spatial disparities, but they are not capable of examining the maximum regional disparities. To address this bottleneck, the authors of this study developed a geographically optimal zones-based heterogeneity (GOZH) model. By progressively optimizing geographical zones of soil moisture and quantifying the heterogeneity among zones, GOZH may help identify individual and interactive determinants of soil moisture across a large study area. It was applied to identify spatial determinants of in situ soil moisture data collected at 653 monitoring stations in the Northern Hemisphere in unfrozen and frozen seasons from April 2015 to December 2017, with only thawed data considered in both seasons. Correspondingly, a series of variables were derived from Google Earth Engine (GEE) remote sensing data. The results demonstrated the significant regional disparities of soil moisture, and the combinations of determinants are critically different among geographical zones and between unfrozen and frozen seasons. At a global scale, the combinations of determinants can explain about 48% of the spatial pattern of soil moisture. Spatial heterogeneity of soil moisture in frozen seasons is much more complex than that in unfrozen seasons regarding geographical zones and explanatory variables. The variability of soil moisture during unfrozen seasons can be more explainable than that during frozen seasons, which was a convincing evidence for previous studies that soil moisture predictions were mostly performed during unfrozen seasons. Primary variables that determine spatial patterns of soil moisture are changed from climate variables during the unfrozen season to geographical variables during the frozen season. Results show that GOZH model can effectively explore spatial determinants of soil moisture through avoiding the underestimation of individual variables, overestimation of multiple variables, and finely divide zones. The research findings from this study provide an in-depth understanding of the spatial heterogeneity of soil moisture and can be implemented in more effective in situ sampling network design, spatial down-scaling of soil moisture, and accurate inversion of surface parameters from the satellite data of soil moisture. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays a significant role in water, energy, and carbon cycles, and is a key variable related to multiple sustainable development goals. The frequent occurrence of extreme events in the 21st century complicates the understanding of terrestrial SM change and its drivers under diverse environmental conditions, particularly in the global scope. Here we globally explored the spatial-temporal trend of satellite-based SM (ESA CCI SM) and its possible drivers under a variety of environmental factors (land cover, soil texture, terrain, and vegetation coverage) during 2000–2021. Results indicate that global SM has generally declined with a rate of −0.10 × 10−3 m3m−3 yr−1 which is mainly dominated by the drying trend of the southern hemisphere. The moisture replenishment role of precipitation dominates in areas with relatively high SM values, and the positive influence of vegetation and precipitation on SM is prominent in regions where SM values are relatively low. But vegetation has a pronounced negative impact on SM north of 60°N and in southeastern China. Compared with precipitation and vegetation, topsoil temperature has less impact on SM globally. Land cover types, soil properties, elevation, and vegetation cover influence SM variability and its response to climate and vegetation to varying degrees. For example, the overall wetting trend of SM is unique under loam soils and high elevations (> 1000 m) in terms of different soil texture and elevations. The increase in vegetation cover within a certain amount is accompanied by the wetting of SM, but in densely vegetated regions such as forests, the water retention effect of vegetation on SM is weakened. An evaluation of 'Dry gets drier, wet gets wetter' (DDWW) paradigm from the SM viewpoint shows the paradigm summarizes 41.06% of areas with significant SM changes. Precipitation replenishment and vegetation water retention profoundly impact the 'wet gets wetter' and 'dry gets wetter' patterns. Temperature is a strong influencing factor of the 'wet gets drier' pattern, while the reduction of vegetation and precipitation jointly affect the 'dry gets drier' pattern. These findings can deepen the understanding of SM changes in relation to climate, ecology, and land surface features, particularly in the 21st century with profound changes of the environment. • Soil moisture (SM) trend and its driving mechanisms are investigated globally. • SM presents an overall drying trend, particularly in the southern hemisphere. • Precipitation and vegetation have greater influence on SM than temperature does. • The SM trend and its drivers are significantly affected by environmental factors. • SM variation in arid zones generally follows the 'dry get drier' paradigm. [ABSTRACT FROM AUTHOR]

• Performances of five SMAP SMs are evaluated under various environmental conditions. • SMAP-IB shows better performance in both overall median R and ub RMSD. • Lower performances with rough surface, higher soil wetness and dense vegetation. • The higher the soil organic matter, the more degraded the performances of five SMs. • The ub RMSD of SMAP-IB, SCA-H and MT-DCA is higher on a relatively smooth surface. Since the launch of Soil Moisture Active Passive (SMAP) mission in 2015, several advanced global surface soil moisture (SM) products derived from different retrieval schemes have been released. However, the factors contributing to the performance differences of these SMAP products have not been fully investigated. This study tries to fill the gap by assessing and intercomparing five SMAP products, namely, the single channel algorithm V/H-pol (SCA-V/H), dual channel algorithm (DCA), multi-temporal dual channel algorithm (MT-DCA) and the new SMAP-INRAE-BORDEAUX (SMAP-IB), using the International Soil Moisture Network (ISMN) ground measurements worldwide during 2016–2020. After reviewing the retrieval algorithms, the impact of five potential perturbing factors on the skills of all products is reported; these factors including mean surface soil temperature (MSST), surface roughness (log(SR)), vegetation density (characterized by vegetation optical depth (VOD)), mean surface soil wetness (MSSM) and soil organic matter (SOM). Results shows that the SMAP-IB product has the highest overall R value of 0.75 with ISMN ground SM, and the smallest unbiased root mean square difference (ub RMSD) values same as SCA-V and DCA (∼0.058 m3/m3). The performance of all five products in terms of R and ub RMSD rapidly degrades over the regions with high VOD, rough surface and low MSST. In contradiction with the fact that product performance degrades with increasing roughness, the ub RMSD of all five products increases with decreasing log(SR) on a relatively smooth surface (log(SR) < 6) with larger growth rates for SMAP-IB, SCA-H and MT-DCA. Regarding characterizing the temporal variations of in situ SM anomalies (R anom), R anom increases as the ground becomes wet for all products within a certain range (MSSM < 0.3 m3/m3). In particular, SMAP-IB and MT-DCA present higher R anom values than the other three products under wet soil surfaces (MSSM > 0.3 m3/m3). The two metrics R and R anom decrease significantly with increasing SOM for SMAP-IB compared to the other products, and SMAP-IB obtains the lowest values for both metrics when SOM > 20%. These findings are expected to provide useful information for appropriate product selection and improvement. [ABSTRACT FROM AUTHOR]

ESA's Soil Moisture Ocean Salinity (SMOS, since 2009) and NASA's Soil Moisture Active Passive (SMAP, since 2015) are the only two space-borne L-band radiometer missions currently in orbit, which provide key information on global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD). However, to date very few studies considered merging SMOS and SMAP for both SM and VOD retrievals simultaneously. This study presents the first global long-term and continuous SM and L-band VOD (L-VOD) dataset retrieved after merging the SMOS and SMAP brightness temperature (TB) observations, called the SMOS-SMAP-INRAE-BORDEAUX or SMOSMAP-IB product. We first developed a fitted SMOS TB dataset at a fixed incidence angle of 40°, and next applied a monthly linear rescaling of SMAP TB to SMOS TB for each polarization to produce a merged SMOS/SMAP TB (θ = 40°) dataset. The retrievals were then based on a mono-angular retrieval algorithm sharing a similar forward model with the SMOS-IC and the official SMOS retrieval algorithms. Results showed that the inter-calibration approach we used here could effectively remove the bias between the SMAP TB and fitted SMOS TB, with bias values reduced to 0.01 K (−0.02 K) compared to 3.45 K (1.65 K) for V (H) polarization before inter-calibration. The SMOSMAP-IB SM and L-VOD retrievals based on this new inter-calibrated SMOS/SMAP TB led to metrics that were equally good or better than those of other products (i.e., ESA CCI, SMOS-IC and the official SMAP products). When considering only long duration products, SMOSMAP-IB SM retrievals exhibited (i) the highest overall median R value of 0.72 with in-situ data from ISMN (International Soil Moisture Network) during 2013–2018, followed by SMOS-IC (R = 0.68) and CCI (R = 0.67), and (ii) the same smallest ub RMSD values as CCI (ub RMSD = 0.057 m3/m3 vs 0.061 m3/m3 for SMOS-IC). L-VOD retrievals from SMOSMAP-IB were found to have comparable spatial and temporal skills to SMOS-IC. Spatially, they both correlated well with aboveground biomass (R = 0.87), and temporally, they both showed a good representation of the short vegetation NDVI signal and of the forest area loss in the Brazilian Amazon from 2011 to 2019. Developing SMOSMAP-IB is a step forward towards building a time-continuous L-band SM and VOD products in response to the possible failure of one of the SMOS or SMAP sensors in the future. • The first SM and L-VOD product (SMOSMAP-IB) that fuses SMOS and SMAP is proposed. • SMOSMAP-IB SM correlates better with in-situ data than other retrieval products. • SMOSMAP-IB SM has similar ub RMSD skill scores as CCI SM. • SMOSMAP-IB L-VOD has comparable spatial and temporal skills as SMOS-IC L-VOD. [ABSTRACT FROM AUTHOR]

High-quality and long-term surface soil moisture (SSM) and root-zone soil moisture (RZSM) data are critical for agricultural water management of Jiangsu province, which is a major agricultural province in China. However, few studies assessed the accuracy of SSM and RZSM datasets in croplands of Jiangsu province. The study addressed this gap by firstly using observations from ninety-one sites to assess thirteen satellite and model-based SSM products (Advanced Scatterometer (ASCAT), European Space Agency Climate Change Initiative (ESA CCI) Combined/Passive/Active, Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP)-Multi-Temporal Dual-channel Algorithm (MTDCA)/Level 3 (L3)/Level 4 (L4)/SMAP-INRAE-BORDEAUX (IB)/Multi-channel Collaborative Algorithm (MCCA), the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land), and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah)), and four RZSM products (ERA5-Land, GLDAS-Noah, SMAP-L4 and ESA CCI (retrieved using ESA CCI Combined SSM coupled with an exponential filter)). We also inter-compared time-invariant and time-variant Triple Collocation Analysis (TCA)-based R with in situ -based R calculated using SSM anomalies. Various evaluation strategies were compared using different groups of available sites and temporal samplings. Our results showed that the model-based and combined SSM products (i.e. , ERA5-Land, SMAP-L4, ESA CCI Combined/Passive/Active, GLDAS-Noah, ASCAT) performed better than the other SSM products and ERA5-Land, SMAP-L4 and ESA CCI RZSM generally performed better than the GLDAS-Noah RZSM product with higher R. Similar performance rankings were observed among time-invariant and time-variant TCA- R and in situ -based R , in which the TCA- R values for all SSM datasets were higher than the in situ -based R as the representativeness errors of the in situ measurements may bias in situ -based R. The accuracy of the ESA CCI, GLDAS-Noah and ERA5-Land SSM products was expected to be enhanced by considering the water effect and high uncertainties were observed for MTDCA and SMAP-MCCA SSM over dense vegetation periods and regions. Also, it is important to select appropriate evaluation strategies to conduct the SSM and RZSM evaluations according to the situation as the available sites and temporal samplings may bias the evaluation results. • SSM and RZSM products were evaluated over croplands of Jiangsu province. • Reanalysis products outperformed other SSM and RZSM products over croplands. • Similar performances were found for time-invariant/variant TCA- R and in situ -based R. • The impacts of WF, VOD and data samplings on SSM products' accuracy were studied. [ABSTRACT FROM AUTHOR]

Passive microwave remote sensing at L-band (1.4 GHz) provides an unprecedented opportunity to estimate global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD), which are essential to monitor the Earth water and carbon cycles. Currently, only two space-borne L-band radiometer missions are operating: the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions in orbit since 2009 and 2015, respectively. This study presents a new mono-angle retrieval algorithm (called SMAP-INRAE-BORDEAUX, hereafter SMAP-IB) of SM and L-band VOD (L-VOD) from the dual-channel SMAP radiometric observations. The retrievals are based on the L-MEB (L-band Microwave Emission of the Biosphere) model which is the forward model of SMOS-IC and of the official SMOS retrieval algorithms. The SMAP-IB product aims at providing good performances for both SM and L-VOD while remaining independent of auxiliary data: neither modelled SM data nor optical vegetation indices are used as input in the algorithm. Inter-comparison with other SM and L-VOD products (i.e., MT-DCA, SMOS-IC, and the new versions of DCA and SCA-V extracted from SMAP passive Level 3 product) suggested that SMAP-IB performed well for both SM and L-VOD. In particular, SMAP-IB SM retrievals presented the higher scores (R = 0.74) in capturing the temporal trends of in-situ observations from ISMN (International Soil Moisture Network) during April 2015–March 2019, followed by MT-DCA (R = 0.71). While the lowest ub RMSD value was obtained by the new version of SMAP DCA (0.056 m3/m3), SMAP-IB SM retrievals presented best scores for R, ub RMSD (~ 0.058 m3/m3) and bias (0.002 m3/m3) when considering only products independent of optical vegetation indices (e.g., NDVI). L-VOD retrievals from SMAP-IB, MT-DCA, and SMOS-IC were well correlated (spatially) with aboveground biomass and tree height, with spatial R values of ~0.88 and ~ 0.90, respectively. All three L-VOD products exhibited a smooth non-linear density distribution with biomass and a good linear relationship with tree height, especially at high biomass levels, while the L-VOD datasets incorporating optical information in the algorithms (i.e., SCA-V and DCA) showed obvious saturation effects. It is expected that this new algorithm can facilitate the fusion of both SM and L-VOD retrievals from SMOS and SMAP to obtain long-term and continuous L-band earth observation products. • A new algorithm (SMAP-IB) is proposed for the retrieval of SM and L-VOD from SMAP. • Performances of SMAP-IB SM and L-VOD are inter-compared with four other products. • SMAP-IB SM showed best scores for correlation and similar to SCA-V for ub RMSD. • SMAP-IB L-VOD showed high scores according to all used criteria. [ABSTRACT FROM AUTHOR]

• Impact of TGD construction on drought propagation process were quantified. • Propagation of drought duration and severity were aggravated after TGD operation. • Hydrological drought and soil moisture drought propagation speed was affected. • Potential influence factors were explored at different periods from 1981 to 2020. The construction of Three Gorges Dam (TGD) has exerted substantial effects on local hydrological regime and eco-system since its operation in 2003. Due to this significant anthropogenic activity, drought propagation processes from Meteorological drought to Hydrological (M-H) and Soil moisture (M-S) drought are inevitably modified. However, current studies are still insufficient to answer whether the TGD operation mitigated or exacerbated the drought propagation process due to its complex impacts. To explore this key issue, this study proposed an integrated framework to quantify the impact of TGD construction on drought propagation process in the Yangtze River Basin (YRB). The drought characteristics propagation models and propagation time were calculated to identify the modification of drought propagation process between different TGD operation scenarios. Trend and attribution analyses were also utilized to explore potential influence factors. Results demonstrated that TGD construction did significantly aggravate the drought characteristics propagation. Specifically, the slopes of hydrological drought duration and severity in linear propagation models increased 23.98% and 29.23% in average. While for M-S propagation, the slopes of duration and severity models increased 28.72% and 21.91%, respectively. This research also found that, after TGD operation, the M-H propagation speed had been slowed down but the M-S lag time became shorter. Additionally, the variations and trend patterns of six potential influence factors at four separate periods were full analyzed to provide implications for the drought propagation modifications. And TGD operation was confirmed to become a major factor inducing the variations of annual runoff, which accounted for 56.75% for the entire YRB. The above quantitative findings are expected to reveal the potential changes of drought propagation process caused by the construction of TGD in YRB. The proposed integrated framework is also beneficial to the drought early warning system and understanding of drought development not only in YRB, but also in other basins at the global scale. [ABSTRACT FROM AUTHOR]