Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E_SWI, SMOS_SWI, AMSR2_SWI, and ASCAT_SWI, with the L-band-based SMAPL3E_SWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI_SWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3E_SWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

The role of meteorological droughts and initial conditions (land and atmosphere) in soil moisture drought (SMD) propagation are not yet fully understood. This work uses a drought event‐based causal framework to investigate the relative importance of meteorological drought (MD) duration and intensity and initial conditions that result in surface and rootzone SMD, considering their event‐level propagation time (PT) over South Asia. Initially, spatial variability of drought propagation is assessed by the Propagation Ratio (PR) computed based on MD counts that trigger SMD at various lags. PR depicts 2–3 months slower rootzone propagation than at surface. The gradual decrease in PR with increasing regional aridity indicates faster propagation over humid regions. The causal impact of initial conditions and MD parameters on propagating SMD are evaluated using normalized mutual information and a newly proposed normalized conditional mutual information. We found greater importance of triggering MD parameters followed by initial soil moisture condition on propagating SMD. This behavior is more evident for the surface layer propagation at shorter PT. There is a confounding effect of initial atmospheric conditions on drought propagation through initial soil moisture, depicting the significance of land‐atmosphere interactions prior to propagation. In the rootzone propagation, initial soil moisture has a greater influence on propagation, especially at longer PT, indicating the significance of soil moisture persistence. Stronger causal links obtained through the joint influence of MD parameters on SMD suggest the importance of accounting for MD duration and intensity simultaneously, which are not considered in drought index‐based propagation studies.

Vegetation serves as a vital carbon sink, crucial for regulating CO2 and O2 levels in the atmosphere. However, the declining health of vegetation can contribute to a rise in greenhouse gas emissions. Utilizing remote sensing satellite imagery, we can effectively monitor global changes in vegetation health in near-real time. Various vegetation indices have been developed to monitor specific biochemical properties. Yet, many of these indices fall short in detecting health deterioration caused by multiple stressors such as excessive heat, salinity, and water scarcity. Indices that are primarily sensitive to single-leaf parameters may not fully capture the complex stress responses in vegetation. To address this limitation, we introduce a novel vegetation health indicator: the Sentinel-2-based Vegetation Health Index (SVHI). This index is designed to detect stress-induced changes in chlorophyll, water, and protein content. It was validated using global sensitivity analysis (GSA) with physical models and laboratory-based spectroscopy experiments. We have performed a global sensitivity analysis utilizing radiative transfer models to support SVHI’s performance. It indicates strong sensitivity to variations in chlorophyll and water content. Following GSA, a lab-based spectroscopy experiment was conducted to detect the effect of water stress and chlorophyll stress on the vegetation indices. In experiment performed on water stress, SVHI demonstrated five and 1.1 times greater sensitivity than NDVI and NDMI respectively in the early stages of water loss (150%–85% leaf water content), confirmed by Tukey’s HSD test (p < 0.05). It was also observed that NDVI failed to show a statistically significant change during this period (p = 0.63). The experiment performed on the effect of chlorophyll revealed that NDMI could not detect chlorophyll degradation, while SVHI retained sensitivity throughout the chlorophyll decline. Further, we have performed a corn crop phenology analysis using Sentinel-2 data to confirm the effectiveness of SVHI. The analysis revealed that the proposed index successfully distinguishes characteristic changes in vegetation over time. In addition, as compared to NDMI, SVHI differentiates non-vegetated areas, such as water bodies, from vegetated areas. Finally, a temporal analysis of the vegetation indices reveals that SVHI is highly correlated with both NDVI (R2=0.958) and NDMI (R2=0.993), indicating its capability to capture variations in both greenness and moisture content.

The global distribution of hydrological regimes determines land‐atmosphere feedbacks by regulating the water, energy and carbon fluxes. Strong land‐atmosphere feedbacks are observed over the drier surface where evapotranspiration and partitioning of energy fluxes are regulated by soil moisture availability. In this study, we propose an advantageous method compared to conventional methods—soil moisture (from Soil Moisture Active Passive (SMAP) and ERA5) and soil diurnal temperature range (from ERA5 and GLDAS) covariation—to estimate the critical soil moisture (), a threshold that differentiates the water‐limited regime (WLR) and energy‐limited regime (ELR). Results show SMAP and ERA5‐based have consistent spatial patterns and distribution across various biomes and climate zones. High (0.25–0.3 m³m³) are noted over tropical regions and low (0.15–0.2 m³m³) are observed over semi‐arid and dry sub‐humid (savanna, shrubland and grassland) regions, which indicate relatively weaker and stronger land feedbacks and persistence of WLR, respectively. Additionally, we find that variability is strongly dependent on mean soil moisture state, aridity, vapor pressure deficit and bioclimatic conditions. A seasonal analysis indicates that landscapes largely stay in WLR during winter and spring, in ELR during summer and in transitional regime during autumn, especially over the western USA, Brazilian savanna, Sahelian grassland, African shrublands, peninsular India, and southern Australia. Although such patterns are prominently observed in ERA5 regimes, SMAP reports the prevalence of dynamic regime during summer. While seasonally dominant regimes provide the expectation of dry extremes development, the probability of dynamic regime shows the gross expectation of WLR and ELR, and changing land‐atmosphere coupling strength.

Hydrological regimes are fundamental for analyzing land‐atmosphere (LA) interactions. These regimes are delineated into water‐ and energy‐limited regime by estimating their critical soil moisture threshold using the evaporative fraction , temporal rate of soil moisture drying , and diurnal soil temperature range . However, the impact of inconsistencies stemming from on our understanding of LA feedbacks has not yet been examined. The present work, for the first time, conducts a comprehensive intercomparison of , regime properties (persistence, chances of transitioning, and temporal similarity), and LA coupling strength estimated using covariability of , , and with . We find that , and are in close agreement in semi‐arid and sub‐humid climatic regions characterized by herbaceous vegetation. results in higher persistence of water‐limited regime and less frequent regime transitioning compared to and . However, identifies similar regimes for >80%of the time in 84% of regime transitioning regions. The tipping potential of regimes identified by , and is lowest during the dry seasons, which increases during the summer. identifies stronger LA coupling over western USA, south‐central Australia and peninsular India, compared to and . Central Asian grasslands are identified to have mild LA coupling using , while and do not indicate notable coupling. Overall, represents a viable proxy for and toward enhancing the understanding of local LA coupling with potential applications in assessing ecosystem responses, evolution of dry extreme events, and land surface modeling.