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The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m-3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O-F residuals, ~0.01 (~0.003) m3 m-3 for surface (root-zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Accurate global mapping of soil moisture is the goal of the Soil Moisture Active Passive (SMAP) mission, which is expected to improve the estimation of water, energy, and carbon exchanges between the land and the atmosphere. Like other satellite products, the SMAP soil moisture retrievals need to be validated, with the validation relying heavily on in situ measurements. However, a one-to-one comparison is ill advised due to the spatial mismatch of the large SMAP footprint (∼40 km) and the point scale in situ measurements. This study uses a recently developed hyper-resolution land surface model—HydroBlocks—as a tool to upscale in situ soil moisture measurements for the SMAPVEX15 (SMAP Validation Experiment 2015) field campaign during 2–18 August 2015. Calibrated against in situ observation, HydroBlocks shows a satisfactory Kling-Gupta efficiency (KGE) of 0.817 and RMSE of 0.019 m3/m3 for the calibration period. These results indicate that HydroBlocks can be used to upscale in situ measurements for this site. Different from previous studies, here in situ measurements are upscaled using a land surface model without bias correction. The upscaled soil moisture is then used to evaluate SMAP (passive) soil moisture products. The comparison of the upscaled network to SMAP shows that the retrievals are generally able to capture the areal-averaged soil moisture temporal variations. However, SMAP appears to be oversensitive to summer precipitation. We expect these findings can be used to improve the SMAP soil moisture product and thus facilitate its usage in studying the water, energy, and carbon cycles.

While remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior. Plain Language Summary: Belowground processes, like how roots use soil water for transpiration and how soil water is used for soil evaporation, remain as large uncertainties in the Earth system. This is because these belowground processes are difficult to widely observe and thus tend to only be studied at sparsely located field sites. We address these limitations here by simultaneously using high resolution (70 m scale) remote sensing measurements of land surface temperature (LST), which integrates ecosystem soil and vegetation behavior and water use, and ground networks of soil moisture (SM) measurements between 5 and 100 cm. We find that the relationship between LST and SM at different soil depths shows how ecosystems use moisture across the soil profile. Across vegetation types, our analysis suggests most water use originates from upper soil layers where soil evaporation occurs and where roots presumably preferentially draw water under nominal climatic conditions. Grassland sites tend to have a greater preference for use of moisture in upper soil layers than for forested sites, which show an increase in deeper water use below 50 cm. We therefore demonstrate that such methods can reveal how ecosystems respond to SM across the rootzone and across a range of globally available sites. Key Points: High resolution satellite retrievals of land surface temperature can reveal ecosystem water use when coupled with soil moisture (SM) networksAcross vegetation types, evaporation tends to mainly use water from the upper soil layers with a decrease of SM use with depthGrassland sites tend to have more frequent ecosystem water use of upper layer SM than forested sites [ABSTRACT FROM AUTHOR]

The reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long‐term IA temporal variability remain unknown. Here, 1861 county‐level IA information from Government Censored Data (GCD) during 2000–2021 are collected, cross‐validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter‐annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD‐based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms. Plain Language Summary: Understanding how much land is irrigated (Irrigated Area or IA) is crucial for accurate water use and crop modeling. Traditional IA maps are developed using data from the Food and Agriculture Organization (FAO) and remote sensing indices. Recent improvements, like machine learning, have enhanced IA mapping. However, the relative performances of these methods and their reliability in capturing long‐term changes in irrigated areas remain unknown. This study collects government‐censored county‐level IA data from 2000 to 2021, which is subsequently used for evaluating commonly used IA data sets. Results show that data sets directly using FAO data can reasonability capture the spatial distribution of IA but may miss year‐to‐year changes. On the other hand, data sets relying only on vegetation data tend to overestimate irrigated areas in humid regions. The latest machine learning‐based IA data sets are relatively more accurate but still contain significant uncertainties in IA trends. These biases are closely linked to agricultural suitability (calculated using precipitation and potential evapotranspiration), which may be useful for enhancing future IA mapping algorithms. Key Points: Remote sensing vegetation index is insufficient for identifying irrigated area over humid regionsMachine learning based irrigated area is sensitive to the quality of training sampleAll the irrigated area data sets are biased in representing the temporal dynamics of irrigated area [ABSTRACT FROM AUTHOR]

Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water state–water flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state–flux coupling strength bias—involving both evapotranspiration and runoff—are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias, even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The rescaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water state–water flux coupling strength biases during the operation of an LDAS. Significance Statement: Land data assimilation is the process by which land surface model estimates of water states (e.g., soil moisture) and water fluxes (e.g., runoff and evapotranspiration) are improved via the incorporation of observations. Over the past decade, substantial improvements have been made in the precision of land surface model states via the assimilation of satellite-based soil moisture information. However, to date, these improvements have not yet been extended into water flux estimates like runoff and evapotranspiration. This is a critical shortcoming since advances in important weather and hydrologic forecasting applications are dependent on the improved estimation of such fluxes. We demonstrate that this shortcoming is linked to the inability of existing land surface models to accurately describe the impact of variations in water states on water fluxes and propose strategies for overcoming this issue in future land data assimilation systems. [ABSTRACT FROM AUTHOR]

Accurate coarse-scale soil moisture information is required for robust validation of current- and next-generation soil moisture products derived from spaceborne radiometers. Due to large amounts of land surface and rainfall heterogeneity, such information is difficult to obtain from existing ground-based networks of soil moisture sensors. Using ground-based field data collected during the Soil Moisture Experiment in 2002 (SMEX02), the potential for using distributed modeling predictions of the land surface as an upscaling tool for field-scale soil moisture observations is examined. Results demonstrate that distributed models are capable of accurately capturing a significant level of field-scale soil moisture heterogeneity observed during SMEX02. A simple soil moisture upscaling strategy based on the merger of ground-based observations with modeling predictions is developed and shown to be more robust during SMEX02 than upscaling approaches that utilize either field-scale ground observations or model predictions in isolation. © 2004 Elsevier Ltd. All rights reserved.