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Land surface‐atmosphere coupling and soil moisture memory are shown to combine into a distinct temporal pattern for wildfire incidents across the western United States. We investigate the dynamic interplay of observed soil moisture, vegetation water content, and atmospheric dryness in relation to fuel loading, fire ignition and post‐fire recovery. We find that positive soil moisture anomalies around 5 months before fire ignition increase biomass growth in the subsequent months, thereby shaping fire‐prone vegetation conditions. Then, concurrent decrease in soil moisture, vegetation dehydration, and atmospheric dryness collectively contribute to the occurrence of fire ignition events. This is followed by a rapid recovery in both soil and atmospheric moisture within several weeks after the fire incidents. Our findings provide insights into understanding of wildfire ignition dynamics, supporting fire modeling and enabling improved fire predictions, early warning systems, and mitigation strategies. Plain Language Summary: We find that wildfire are part of a distinct temporal pattern of soil moisture, vegetation water content and atmospheric dryness dynamics that begin about 5 months before the incidents. We analyze anomalies in soil moisture, vegetation water content, vapor pressure deficit and precipitation before and after large wildfires during the warm season across various US climate regions. We document distinct patterns in soil aridity and atmospheric dryness, fuel load, and vegetation dehydration that contribute to the occurrence of wildfires. Our observation‐based analyses show that soil moisture anomalies significantly control the growth of biomass several months before the fire occurrence, which in turn affects the availability of fire fuel. Notable dry anomalies with fuel accumulation in the dry western climate regions are observed approximately 4 to 5 months before the fires, followed by a quick recovery trend several weeks after the fire in wet regions. Our results indicate that in the majority of the climate regions, there are significant dry anomalies preceding the occurrence of wildfires. Understanding the dynamics of these environmental variables can be the basis for improved wildfire hazard predictions and mitigation strategies. Key Points: Wetter‐than‐average soil moisture 4–5 months before wildfires results in increased aboveground biomass accumulationSoil moisture decrease and atmospheric drying after peak biomass lead to dry fuel availability at the time of wildfire ignitionThe temporal pattern is evident in mapped data on soil moisture, vegetation water content and wildfire incidents across western United States [ABSTRACT FROM AUTHOR]

Changes in surface water and energy balance can influence weather through interactions between the land and lower atmosphere. In convecting atmospheres, increases in convective available potential energy (CAPE) at the base of the column are driven by surface turbulent fluxes and can lead to precipitation. Using two global satellite datasets, we analyze the impact of surface energy balance partitioning on convective development by tracking CAPE over soil moisture drydowns (interstorms) during the summer, when land–atmosphere coupling is strongest. Our results show that the sign and magnitude of CAPE development during summertime drydowns depends on regional hydroclimate and initial soil moisture content. On average, CAPE increases between precipitation events over humid regions (e.g., the eastern United States) and decreases slightly over arid regions (e.g., the western United States). The soil moisture content at the start of a drydown was found to only impact CAPE evolution over arid regions, leading to greater decreases in CAPE when initial soil moisture content was high. The effect of these factors on CAPE can be explained by their influence principally on surface evaporation, demonstrating the importance of evaporative controls on CAPE and providing a basis for understanding the soil moisture–precipitation relationship, as well as land–atmosphere interaction as a whole. Significance Statement: Land–atmosphere coupling is a long-standing topic with growing interest within the climate and modeling communities. Understanding and characterizing the feedbacks between the land surface and lower atmosphere has important implications for weather and climate prediction. One component of land–atmosphere coupling not yet fully understood is the soil moisture–precipitation relationship. Our work quantifies the land influence on one pathway for precipitation, convection, by tracking the evolution of atmospheric convective energy as soils dry between storms. Using global satellite observations, we find clear spatial and temporal trends that link summertime convective development to soil moisture content and evaporation. Our observational results provide a benchmark for evaluating how well weather and climate models capture the complex coupling between land and atmosphere. [ABSTRACT FROM AUTHOR]

The surface water and energy balances can be coupled or uncoupled depending on whether the evaporation regime is water‐limited or energy‐limited. As the landscape loses soil moisture during drydowns, a transition between the regimes may occur, which signifies a nonlinear change in water‐energy‐carbon coupling. Regions that switch often between these two regimes, that is, are dominated by neither regime, are particularly vulnerable to climate variability and change. To robustly identify these tipping points, we identify drydown events based on global soil moisture data sets from remote sensing. The event identification does not rely on precipitation information and is robust with respect to measurement noise. Then, the soil moisture thresholds delineating the evaporation regime transitions are determined by Sequential Monte Carlo Sampling and a two‐stage parametrization strategy. Based on the estimated soil moisture thresholds across the globe, we estimate observation‐based water availability indices which quantify the nonlinear controls of soil moisture on evaporation. This framework is tested and applied globally using Soil Moisture Active Passive soil moisture retrievals. Combined with a new tippling‐point metric that describes the frequency of evaporation regime transitions, we identify regions that switch often between different evaporation regimes at the global scale. Given unit shifts in soil moisture, these regions will experience the most change in how their surface water and energy are coupled. Plain Language Summary: Evaporation links the land surface water, energy, and carbon balances. Soil moisture can reduce the amount of available energy that is used for evaporation at the land surface. Evaporation can thus change from an energy‐limited regime to a water‐limited regime at a critical soil moisture value. Additionally, at some low soil moisture value, evaporation altogether diminishes to zero (wilting‐point soil moisture threshold). Besides evaporation, drainage can be the dominant hydrology regime of the landscape. This is the case for very wet soils (above so‐called field capacity or soil moisture threshold at which gravity is no longer an adequate force to dewater the porous media). There are thus several hydrologic regimes during interstorm period, separated by three threshold soil moisture values. We develop robust methods to estimate these thresholds using estimates of surface soil moisture from NASA's Soil Moisture Active Passive satellite mission. The regime transitions and thresholds make the hydrologic response of the landscape to climate forcing via a nonlinear process. We develop a new metric to globally map where the landscape water balance is at the tipping‐point between regimes. Landscapes that exist near the tipping point are particularly vulnerable to climate variability and change. Key Points: Remotely sensed soil moisture is used to estimate the thresholds between drainage‐dominated, energy‐ and water‐limited evaporation regimesThe soil moisture thresholds defining the regime changes are mapped globally and shown to be related to climate and soil textureRegions at the tipping‐point between water‐ and energy‐limited evaporation are mapped across the globe [ABSTRACT FROM AUTHOR]

A frequently expressed viewpoint across the Earth science community is that global soil moisture estimates from satellite L‐band (1.4 GHz) measurements represent moisture only in a shallow surface layer (0–5 cm) and consequently are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Using this argumentation, many observation‐based land surface studies avoid satellite‐observed soil moisture. Here, based on peer‐reviewed literature across several fields, we argue that such a viewpoint is overly limiting for two reasons. First, microwave soil emission depth considerations and statistical considerations of vertically correlated soil moisture information together indicate that L‐band measurements carry information about soil moisture extending below the commonly referenced 5 cm in many conditions. However, spatial variations of effective depths of representation remain uncertain. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. Even some deeper‐rooted species (i.e., shrubs and trees) preferentially or seasonally draw water from the upper soil layers. Therefore, L‐band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated (i.e., relevant beyond only shallow soil processes like soil evaporation). Our commentary encourages the application of satellite soil moisture across a broader range of terrestrial hydrosphere and biosphere studies while urging more rigorous estimates of its effective depth of representation. Key Points: L‐band satellite soil moisture effective depth of representation is often deeper than the commonly referenced 5 cm limitIsotopic tracer studies reveal common preferential plant water use of moisture in these upper soil layers either primarily or seasonallyThe optimal soil moisture product changes in time and space, with satellite soil moisture often being optimal for many vegetated surfaces [ABSTRACT FROM AUTHOR]

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil–plant–atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM–VOD retrievals are feasible by inverting the $\tau -\omega $ model with two independent datasets in dual-channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBs; TBv and ${{\mathrm {TB}}}_{h}$), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the degrees-of-information (DoI) metric and propose a signal-to-noise ratio (SNR) metric to assess the “retrievability” of VOD given the Soil Moisture Active Passive (SMAP) TBv–TBh linear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting the applications of VOD in ecology and hydrology. Results show that regions with mainly nonwoody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of underdetermined inversions: the L3 dual-channel algorithm (L3-DCA) and the multitemporal dual-channel algorithm (MTDCA), which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here. [ABSTRACT FROM AUTHOR]

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the single channel algorithm (SCA) is first applied to Soil Moisture and Ocean Salinity (SMOS) brightness temperatures at vertical polarization (${\mathrm{ TB}}_{V}$) to estimate SM at coarse resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5°, and 52.5°)-adaptive calibration of single scattering albedo ($\omega$) and soil roughness ($h_{s}$) parameters. These effective parameters are used together with fine-scale multiangular Sentinel-1 backscatter in a single-pass active–passive downscaling approach to estimate ${\mathrm{ TB}}_{V}$ at fine scale (1 km) for each SMOS incidence angle. These ${\mathrm{ TB}}_{V}\mathrm {s}$ are finally inverted to obtain the corresponding high-resolution SM maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of $\omega $ and a decreasing trend of $h_{s}$ with SMOS incidence angle, with almost no variability of $\omega $ across land cover types. The active–passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fine ${\mathrm{ TB}}_{V}$ maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution SM maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well with in situ measurements. Our results indicate that model-based microwave approaches to estimate SM can be adequately adapted to account for the incidence angle diversity of planned missions, such as Copernicus Microwave Imaging Radiometer (CIMR), Radar Observing System for Europe in L-band (ROSE-L), and Sentinel-1 next generation. [ABSTRACT FROM AUTHOR]

The transition of evapotranspiration between energy‐ and water‐limitation regimes also denotes a nonlinear change in surface water and energy coupling strength. The regime transitions are primarily dominated by available moisture in the soil, although other micro‐meteorological factors also play a role. Remotely sensed soil moisture is frequently used for detecting evapotranspiration regime transitions during inter storm dry downs. However, its sampling depth does not include the entire soil profile, over which water uptake is dominated by plant root distribution. We use flux tower, surface (θs; observations at 5 cm), and vertically integrated in situ soil moisture (θv ${\theta }_{v}$; 0–50 cm) observations to address the question: Can surface soil moisture robustly identify evapotranspiration regime transitions? Results demonstrate that θs and θv are hydraulically linked and have synchronized evapotranspiration regime transitions. As such, θs and θv capture comparable statistics of evapotranspiration regime prevalence, which supports the utility of remote‐sensing θs for large‐scale land‐atmosphere exchange analysis. Plain Language Summary: During dry down periods between storms, soil moisture availability can be a limiting factor for evapotranspiration (water‐limited regime). In contrast, evapotranspiration is insensitive to soil moisture for adequately wet conditions, and is primarily determined by atmospheric evaporative demand (energy‐limited regime). During interstorm drydowns, the landscape changes from one regime to another. The timing of evapotranspiration regime change signifies a change in the coupling of landscape water and energy balances. Soil moisture based on microwave remote sensing has been frequently used for analyzing global‐scale evapotranspiration regime transitions. However, microwave signals are mainly sensitive to surface soil moisture (θs), which is not directly a sampling of root water uptake from the deeper soil profile. Therefore, the applicability of remotely sensed θs information for evapotranspiration regime identification is uncertain. In this study, we use flux tower, surface (θs, observations at 5 cm) and vertically integrated (θv, 0–50 cm) in situ observations to examine the reliability of θs for evapotranspiration regime identification. Results demonstrate that θs and θv are equivalently skillful for identifying evapotranspiration regime changes and the time a landscape spends in the water‐limited regime. Therefore, remotely sensed θs can be used to robustly analyze evapotranspiration regime changes and the associated large‐scale land‐atmosphere exchanges. Key Points: Observation‐based analysis shows that surface and root zone soil moisture are consistent in evapotranspiration regime identificationDuring interstorm drydowns, surface soil moisture availability signifies the transition from energy‐ to water‐limited regimeWe demonstrate the general reliability of earth‐orbit retrieved surface soil moisture for large‐scale land‐atmosphere interaction studies [ABSTRACT FROM AUTHOR]

With a changing climate, it is becoming increasingly critical to understand vegetation responses to limiting environmental factors. Here, we investigate the spatial and temporal patterns of light and water limitation on photosynthesis using an observational framework. Our study is unique in characterizing the nonlinear relationships between photosynthesis and water and light, acknowledging approximately two regime behavior (no limitation and varying degree of limitation). It is also unique in using an observational framework instead of using model-derived photosynthesis properties. We combine data from three different satellite sensors, i.e., solar-induced chlorophyll fluorescence (SIF) from TROPOMI, surface soil moisture from SMAP, and above-ground canopy density from MODIS. We find both single-regime and two-regime models describe SIF sensitivity to soil moisture and photosynthetically active radiation (PAR) across the globe. The distribution and strength of soil moisture limitation on SIF are mapped in the water-limited environments while distribution and strength of PAR limitations are mapped in the energy-limited environments. Two-regime behavior is detected in 73% of the cases for water limitation on photosynthesis, while two-regime detection is much lower at 36% for light limitation on photosynthesis. SIF sensitivity to PAR strongly increases along moisture gradients, reflecting mesic vegetation’s adaptation to making rapid usage of incoming light availability on the weekly timescales. The transition point detected between the two regimes is connected to soil type and mean annual precipitation for the SIF-soil moisture relationship and for the SIF-PAR relationship. These thresholds have therefore an explicit relation to properties of the landscape, although they may also be related to finer details of the vegetation and soil interactions not resolved by the spatial scales here. The simple functions and thresholds are emergent behaviors capturing the interaction of many processes. The observational thresholds and strength of coupling can be used as benchmark information for land surface and Earth system models, especially those that characterize gross primary production mechanisms and vegetation dynamics. [ABSTRACT FROM AUTHOR]

Climate variability and change shift environmental conditions on global land surfaces, creating uncertainties in predicting hydrologic flows, crop yields, and land carbon uptake. Land surfaces can present varying degrees of inertia to atmospheric forcing variability (e.g., precipitation). This study asks: are regions with the most variable environmental forcing necessarily the regions with the largest land surface variability? Specifically, it seeks to determine why land surfaces show varying responsiveness to environmental forcing. The degree to which and the mechanisms for how landscapes modulate the forcing are evaluated using a decade‐long satellite observation record of Africa's diverse climates. Surface responsiveness is quantified using intra‐seasonal energy flux variability, based on the observed diurnal temperature amplitude. We map the responsiveness and analyze the underlying mechanisms over intra‐seasonal timescales (especially interstorms). We show that, at a location, land surface responsiveness is dependent on the soil moisture distribution and the nonlinear relationship between energy fluxes and soil moisture. Land surfaces with greater responsiveness to climate are those with soil moisture distributions that span the threshold between evaporation regimes and spend most of their time in the water‐limited regime. Consequently, surface responsiveness mechanisms drive land surface variability beyond high climatic variability. Since we find these results to hold from intra‐seasonal to interannual timescales, we expect that these responsive regions will be most vulnerable to long‐term shifts in climate forcing. The quantification of these phenomena and determination of their geographic distributions based on observations can help assess land surface models used to evaluate hydrologic consequences of climate change. Plain Language Summary: Variations in rainfall and incoming sunlight amount shift land surface conditions (i.e., evaporation) and constrain our ability to predict crop yields and carbon sequestration. Nevertheless, some landscapes respond more to rainfall and sunlight variability than others. Using recent decade‐long satellite observations of surface temperature and soil moisture across Africa, we investigate whether regions with the most rainfall and sunlight variability are also those with the largest land surface variability and why. Indeed, we find that more rainfall and sunlight variability do increase the land evaporation and temperature variability. However, regions that spend time in the water‐limited evaporative regime (a drier soil state where soil moisture influences evaporation rates) have an amplified land surface response to atmospheric variability. These regions tend to be semi‐arid, but isolated humid regions also show this behavior because the water‐limited regime is defined by factors beyond mean moisture availability. While these mechanistic findings are based on daily‐to‐weekly land and atmospheric variability, we find these results also hold on longer timescales of year‐to‐year variations. As a result, these regions spending time mainly in the water‐limited regime likely have increased vulnerability to climatic changes in rainfall and sunlight amount. Key Points: Observed intraseasonal energy flux variance (from diurnal temperature amplitude) is used to quantify surface responsiveness to forcingsIntraseasonal mechanisms based on the energy flux‐soil moisture relationship and weather variability describe responsiveness patternsSpending more time in the water‐limited evaporative regime enhances surface responsiveness across intraseasonal to interannual timescales [ABSTRACT FROM AUTHOR]

Adaptive and targeted allocation of mobile sensing agents, in the form of unmanned aerial vehicles (UAVs) with software defined radar (UAV-SDRadar) payloads, enable mapping of surface soil moisture in regions where in situ wireless sensor networks (WSNs) undersample soil moisture or upscaling models perform poorly. This work presents an optimization-based UAV path planning methodology that seeks to maximize UAV flight coverage over areas where a complementing WSN yields upscaled soil moisture estimates with high uncertainty. By recursively mapping soil moisture over such areas, the combined UAV and WSN instrumentation can gradually capture the domain’s true mean soil moisture. A series of numerical simulations are presented to demonstrate the algorithm’s basic function while considering real-world and feasible operational scenarios. [ABSTRACT FROM AUTHOR]

An active–passive microwave retrieval algorithm for simultaneous determination of soil surface roughness parameters [vertical root-mean-square (RMS) height (${s}$) and horizontal correlation length (${l}$)] is presented for bare soils. The algorithm is based on active–passive microwave covariation, including the improved Integral Equation Method (I2EM), and is tested with global soil moisture active passive (SMAP) observations. The estimated retrieval results for ${s}$ and ${l}$ are overall consistent with values in the literature, indicating the validity of the proposed algorithm. Sensitivity analyses showed that the developed roughness retrieval algorithm is independent of permittivity for ${\varepsilon }_{s} > 10$ [-]. Furthermore, the physical model basis of this approach (I2EM) allows the application of different autocorrelation functions (ACF), such as Gaussian and exponential ACFs. Global roughness retrieval results confirm bare areas in deserts such as Sahara or Gobi. However, the type of ACF used within roughness parameter estimation is important. Retrieval results for the Gaussian ACF describe a rougher surface than retrieval results for the exponential ACF. No correlations were found between roughness results and the amount of precipitation or the soil texture, which could be due to the coarse spatial resolution of the SMAP data. The extension of this approach to vegetated soils is planned as an add-on study. [ABSTRACT FROM AUTHOR]

Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently launched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, while Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36 km resolution (2002–2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m3/m3. NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable through the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability. Measurement(s) wetness of soil • surface soil Technology Type(s) microwave spectroscopy • satellite imaging Factor Type(s) temporal resolution • spatial resolution Sample Characteristic - Environment planetary surface Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14533554 [ABSTRACT FROM AUTHOR]

Plant hydraulic and photosynthetic responses to individual rain pulses are not well understood because field experiments of pulse behavior are sparse. Understanding individual pulse responses would inform how rainfall intermittency impacts terrestrial biogeochemical cycles, especially in drylands, which play a large role in interannual global atmospheric carbon uptake variability. Using satellite-based estimates of predawn plant and soil water content from the Soil Moisture Active Passive (SMAP) satellite, we quantify the timescales of plant water content increases following rainfall pulses, which we expect bear the signature of whole-plant mechanisms. In wetter regions, we find that plant water content increases rapidly and dries along with soil moisture, which we attribute to predawn soil–plant water potential equilibrium. Global drylands, by contrast, show multi-day plant water content increases after rain pulses. Shorter increases are more common following dry initial soil conditions. These are attributed to slow plant rehydration due to high plant resistances using a plant hydraulic model. Longer multi-day dryland plant water content increases are attributed to pulse-driven growth, following larger rain pulses and wetter initial soil conditions. These dryland responses reflect widespread drought recovery rehydration responses and individual pulse-driven growth responses, as supported by previous isolated field experiments. The response dependence on moisture pulse characteristics, especially in drylands, also shows ecosystem sensitivity to intra-annual rainfall intensity and frequency, which are shifting with climate change. [ABSTRACT FROM AUTHOR]

Rain pulses followed by interstorm drying periods are the fundamental units of water input into ecosystems on subweekly time scales. It is essential to understand landscape‐scale vegetation responses on these unit time scales as they may describe sensitivity of landscape water, carbon, and energy cycles to shifts in rainfall intensity and frequency, even if the average seasonal precipitation remains unchanged. Because pulse investigations are primarily carried out in drylands, little is known about the characteristics and extent of ecosystem plant pulse responses across the broader range of climates and biomes. Using satellite‐based plant water content (from vegetation optical depth) and plant carbon uptake observations from eddy covariance towers across the continental United States climate gradient (dry to humid), we characterize large‐scale plant carbon and water uptake responses to rain pulses during spring and summer months. We find that while all ecosystems in the study region show discernable plant water content and carbon flux responses to rain pulses, drier ecosystems exhibit more frequent and longer duration responses. Unlike mesic environments, drylands show significantly different responses under varying antecedent soil moisture and pulse magnitude conditions; the largest water and carbon uptakes follow large pulses on initially wet soils. We detect soil moisture thresholds primarily in drylands, which can partly explain dryland vegetation's different responses under dry and wet conditions. We conclude that vegetation responds to individual pulses of water availability across all climates and therefore a range of ecosystems are sensitive to rainfall distributions beyond simple seasonal precipitation totals. Key Points: Ecosystem carbon fluxes and plant water content are sensitive to individual summer soil moisture pulses across dry to humid US biomesEcosystem carbon uptake and plant water content increase following moisture pulses more frequently in drier biomesUnlike mesic vegetation, dryland vegetation responses vary depending on pulse magnitudes and antecedent moisture conditions [ABSTRACT FROM AUTHOR]

Microwave brightness temperature observations from the NASA Soil Moisture Active Passive (SMAP) mission and gauge‐based precipitation data over the United States are used to reconstruct the soil water loss function and then historical (1979–2019) hydrological fluxes in the form of evapotranspiration (ET) and drainage (D) are quantified. Over the period of study, with the exception of snowy and hyper‐arid regions, we observe a correlation of R2 > 0.6 between SMAP‐precipitation derived drainage estimates and streamflow measurements from the U.S. Geological Survey (USGS). There is a bias between estimated drainage and USGS streamflow with an underestimation of about 1 mm day−1 in southwest United States to 3 mm day−1 in parts of the eastern United States. SMAP‐derived sensitivities of drainage and ET partitioning with respect to precipitation anomalies are also calculated. In parts of the Great Plains the drainage partitioning exhibits a near‐linear response, while in the southeast United States, the response is nonlinear. Partitioning also is examined for 6 four‐digit hydrologic unit basins wherein year‐to‐year variations in drainage partitioning are shown to be key mediators in translating precipitation anomalies into streamflow anomalies. Observation‐driven drainage and ET estimates are obtained without relying on full hydrologic and Land Surface Models (LSMs). This independence (isolation from model parameterization assumptions) provides a path toward using satellite‐derived landscape hydrological diagnostics to assess hydrologic models and LSMs as well as to guide their further development. Key Points: A large amount of hydrologic information is encoded within satellite remote sensing observations of soil moistureSMAP‐era soil moisture and precipitation information, without reliance on models, can characterize landscape hydrological partitioningHistorical remote sensing and precipitation‐based drainage estimates closely track streamflow measurements across the United States [ABSTRACT FROM AUTHOR]

In this paper, we present the Pipeline for Observational Data Processing, Analysis, and Collaboration (PODPAC) software. PODPAC is an open-source Python library designed to enable widespread exploitation of NASA earth science data by enabling multi-scale and multi-windowed access, exploration, and integration of available earth science datasets to support analysis and analytics; automatic accounting for geospatial data formats, projections, and resolutions; simplified implementation and parallelization of geospatial data processing routines; standardized sharing of data and algorithms; and seamless transition of algorithms and data products from local development to distributed, serverless processing on commercial cloud computing environments. We describe the key elements of PODPAC's architecture, including Nodes for unified encapsulation of disparate scientific data sources; Algorithms for plug-and-play processing and harmonization of multiple data source Nodes; and Lambda functions for serverless execution and sharing of new data products via the cloud. We provide an overview of our open-source code implementation and testing process for development and deployment of PODPAC. We describe our interactive, JupyterLab-based end-user documentation including quick-start examples and detailed use case studies. We conclude with examples of PODPAC's application to: encapsulate data sources available on Amazon Web Services (AWS) Open Data repository; harmonize processing of multiple earth science data sets for downscaling of NASA Soil Moisture Active Passive (SMAP) soil moisture data; and deploy a serverless SMAP-based drought monitoring application for use access from mobile devices. We postulate that PODPAC will also be an effective tool for wrangling and standardizing massive earth science data sets for use in model training for machine learning applications. [ABSTRACT FROM AUTHOR]

Plant water content observations using microwave remote sensing measurements allow monitoring of landscape‐scale plant water stress. During soil drying following rainfall events, we use a Granger causality framework to quantify the degree to which environmental factors drive satellite‐based plant water content loss across Africa's diverse biomes. After soil drying into the water‐limited regime, satellite observations show that plants dry while solar radiation, vapor pressure deficit, and diurnal temperature amplitude increase. We find that soil drying primarily drives plant water content loss across African drylands, though with regional effects of diurnal temperature amplitude increases (found to indicate vapor pressure deficit increases here). We also detect interactions between these factors that reinforce plant drying during periods of soil moisture loss. Our results provide observational evidence across Africa that individual and interactive components of surface drying and heating can all drive plant water stress, especially during intermittent poststorm drying periods. Plain Language Summary: Plant water content represents the amount of water within the volume of vegetation canopy, and its dynamics can capture plant water stress. Since plant water content can now be monitored at large spatial scales using satellites, we use a rigorous statistical framework to quantify how the climatic factors soil water content, daily temperature range, light intensity, and atmospheric dryness individually drive plant drying across the expanse of diverse biomes across Africa. We specifically conduct our analysis on water‐limited periods about a week after rainfall events, where we show that plant water content progressively declines while soil dries and light intensity, heat, and atmospheric dryness increase. We find that plant drying after rainfall is driven by soil water loss across Africa's drier ecosystems and regionally by daily temperature range increases. Additionally, the interconnectedness of the plant, soil, and atmospheric conditions evaluated here reinforces plant drying in these ecosystems. We conclude that surface drying and heating can individually and interactively drive plant water stress at landscape scales. Key Points: Granger causality tests are used to identify environmental drivers of plant water stress based on satellite plant water contentSoil moisture loss and diurnal surface heating drive plant drying across African drylands after rainfall in the water‐limited regimeLand‐atmosphere interactions and feedbacks in the soil‐plant continuum reinforce this plant water loss during soil moisture drydowns [ABSTRACT FROM AUTHOR]

Soil moisture dynamics in the presence of dense vegetation canopies are determinants of ecosystem function and biogeochemical cycles, but the capability of existing spaceborne sensors to support reliable and useful estimates is not known. New results from a recently initiated field experiment in the northeast United States show that the National Aeronautics and Space Administration (NASA) SMAP (Soil Moisture Active Passive) satellite is capable of retrieving soil moisture under temperate forest canopies. We present an analysis demonstrating that a parameterized emission model with the SMAP morning overpass brightness temperature resulted in a RMSD (root‐mean‐square difference) range of 0.047–0.057 m3/m3 and a Pearson correlation range of 0.75–0.85 depending on the experiment location and the SMAP polarization. The inversion approach included a minimal amount of ancillary data. This result demonstrates unequivocally that spaceborne L‐band radiometry is sensitive to soil moisture under temperate forest canopies, which has been uncertain because of lack of representative reference data. Key Points: Soil moisture monitoring networks were deployed at two forested sites for SMAP validation experimentSMAP brightness temperature was used to retrieve soil moisture over predominantly forested sitesThe study demonstrates the capability of spaceborne L‐band radiometry to retrieve soil moisture under forest canopies [ABSTRACT FROM AUTHOR]

Plant hydraulic and photosynthetic responses to individual rain pulses are not well understood because pulse experiments are sparse. Understanding individual pulse responses would inform how rainfall intermittency impacts terrestrial biogeochemical cycles, especially in drylands which play a large role in global atmospheric carbon uptake interannual variability. Using satellite-based estimates of predawn plant and soil water content from the Soil Moisture Active Passive (SMAP) satellite, we quantify the timescales of plant water content increases following rainfall pulses, which we expect bear the signature of whole-plant mechanisms. In wetter regions, we find that plant water content increases rapidly and dries along with soil moisture, which we attribute to predawn soil-plant water potential equilibrium. Global drylands, by contrast, show multi-day plant water content increases after rain pulses. Shorter increases are more common following dry initial soil conditions. These are attributed to slow plant rehydration due to high plant resistances using a plant hydraulic model. Longer multi-day dryland plant water content increases are attributed to pulse-driven growth, following larger rain pulses and wetter initial soil conditions. These dryland responses reflect widespread drought recovery rehydration responses and individual pulse-driven growth responses, which supports isolated field experiments. The response dependence on moisture pulse characteristics also implies ecosystem sensitivity to intra-annual rainfall intensity and frequency, which are expected to shift in a future climate. [ABSTRACT FROM AUTHOR]

Soil-salinization affects, to a different extent, more than onethird of terrestrial river basins (estimate based on the Food and Agriculture Organization Harmonized World Soil Database, 2012). Among these, many are endorheic and ephemeral systems already encompassing different degrees of aridity, land degradation, and vulnerability to climate change. The primary effect of salinization is to limit plant water uptake and evapotranspiration, thereby reducing available soil moisture and impairing soil fertility. In this, salinization resembles aridity and— similarly to aridity—may impose significant controls on hydrological partitioning and the strength of land–vegetation–atmosphere interactions at the catchment scale. However, the long-term impacts of salinization on the terrestrial water balance are still largely unquantified. Here, we introduce a modified Budyko's framework explicitly accounting for catchment-scale salinization and species-specific plant salt tolerance. The proposed framework is used to interpret the water-budget data of 237 Australian catchments—29% of which are already severely saltaffected— from the Australian Water Availability Project (AWAP). Our results provide theoretical and experimental evidence that salinization does influence the hydrological partitioning of saltaffected watersheds, imposing significant constraints on water availability and enhancing aridity. The same approach can be applied to estimate salinization level and vegetation salt tolerance at the basin scale, which would be difficult to assess through classical observational techniques. We also demonstrate that plant salt tolerance has a preeminent role in regulating the feedback of vegetation on the soil water budget of saltaffected basins. [ABSTRACT FROM AUTHOR]

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 $\text{m}^{3}/\text{m}^{3}$ volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/ $\text{m}^{2}$. Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter $h$ and the polarization mixing parameters $Q$ , a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or $\tau$). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms. [ABSTRACT FROM AUTHOR]

To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation (Eo) perturbations on hydrologic response functions constructed from precipitation and satellite soil moisture observations across the United States. Despite nonlinearities in the evaporation (E) and drainage (D) responses and opposing‐sign perturbations, changes in individual fluxes are superposable. Empirical frameworks (Budyko) can misrepresent changes in E/D partitioning by neglecting shifts/trends in hydrologic regime and subseasonal precipitation dynamics. E/D both increase to balance mean precipitation (P¯) increases, and increased Eo reduces soil moisture. E and D are generally more elastic to changes in P¯ than Eo. The results suggest that (1) the impacts of regional hydrologic perturbations may allow for simple superposition/scaling, (2) changes in timing/intensity of precipitation may have substantial impacts on mean moisture states and fluxes, and (3) changes to the distribution of surface moisture states are likely more relevant for E/D partitioning than common aridity indices. Plain Language Summary: We use satellite observations of soil moisture and expected increase in air temperature to determine how evaporation and soil drainage (to groundwater recharge and rivers/streamflow) will change in a warmer climate. The impacts of drier air, more rainfall, and more extreme rainfall (drier dry days and wetter wet days) can largely be considered separately and then added, which will help when predicting a specific location's water balance using scenarios from global climate models. In typical scenarios, soils are likely to dry, evaporation is likely to increase, and—when precipitation increases—drainage to groundwater/streams is likely to increase as well. Evaporation and drainage are relatively more sensitive to changes in precipitation and humidity in the Western United States than the East, and the Corn Belt is particularly susceptible to changes in precipitation intensity. Common methods of determining changes in evaporation and drainage, which neglect changes in soil moisture, may have large errors in global change scenarios. Key Points: Aridity indices (Budyko) fail to capture effects of daily dynamics and precipitation extremes on long‐term hydroclimate changeClimate perturbations to evaporation/drainage are superposable despite nonlinear flux mechanisms and mixed‐sign effectsLong‐term soil drying tied to trends in increased evaporation, decreased drainage to groundwater and streamflow [ABSTRACT FROM AUTHOR]

Land surface energetic partitioning between latent, sensible, and ground heat fluxes determines climate and influences the terrestrial segment of land‐atmosphere coupling. Soil moisture, among other variables, has a direct influence on this partitioning. Dry surfaces characterize a water‐limited regime where evapotranspiration and soil moisture are coupled. This coupling is subdued for wet surfaces, or an energy‐limited regime. This framework is commonly evaluated using the evaporative fraction–‐soil moisture relationship. However, this relationship is explicitly or implicitly prescribed in land surface models. These impositions, in turn, confound model‐based evaluations of energetic partitioning‐–soil moisture relationships. In this study, we use satellite‐based observations of surface temperature diurnal amplitude (directly related to available energy partitioning) and soil moisture, free of model impositions, to estimate characteristics of surface energetic partitioning–‐soil moisture relationships during 10–‐20‐day surface drying periods across Africa. We specifically estimate the spatial patterns of water‐limited energy flux sensitivity to soil moisture (m) and the soil moisture threshold separating water and energy‐limited regimes (θ*). We also assess how time evolution of other factors (e.g., solar radiation, vapor pressure deficit, surface albedo, and wind speed) can confound the energetic partitioning–‐soil moisture relationship. We find higher m in drier regions and interestingly similar spatial θ* distributions across biomes. Vapor pressure deficit and insolation increases during drying tend to increase m. Only vapor pressure deficit increases in the Sahelian grasslands systematically decrease θ*. Ultimately, soil and atmospheric moisture availability together play the largest role in land surface energy partitioning with minimal consistent influences of time evolution of other forcings. Plain Language Summary: Whether available, incoming solar energy is used for evaporating surface water, or surface heating largely depends on water availability across the landscape. Under dry conditions (water limitation), increasing soil moisture increases evaporation and surface cooling. In this regime, droughts and heatwaves can be initiated and sustained because drying is positively reinforced. Under wetter conditions (energy limitation), increasing soil moisture does not generally influence evaporation. Climate models rely on these soil moisture–evaporation relationships to describe associations between water and energy cycles and predict future climate. However, due to difficulty observing evaporation at large scales, these relationships assume different forms across climate models which contribute to divergences and uncertainty in making climate projections. We use satellite observations of soil moisture and daily temperature range (quantifying surface heating; inversely related to evaporation) to evaluate these relationships free of model impositions across Africa. Following rain events during surface drying, daily temperature range increases with greatest increases in drylands indicating strong surface influence on atmosphere. Increasing incoming sunlight and atmospheric dryness after rainfall accompany and amplify this surface heating. Regions with surface moisture strongly influencing surface heating/evaporation, such as the Sahel, are expected to have soil moisture variations that can initiate future rain and/or drought/heatwave events. Key Points: Soil moisture threshold separating water and energy‐limited evaporation regimes is estimated across diverse African biomesCoupling between moisture and energetic partitioning under water limitation is strongest in drier African biomesAnomalous vapor pressure deficit time evolution during surface drying can confound energetic partitioning–‐soil moisture relationships [ABSTRACT FROM AUTHOR]

Surface soil moisture measurements are typically correlated to some degree with changes in subsurface soil moisture. We calculate a hydrologic length scale, λ, which represents (1) the mean‐state estimator of total column water changes from surface observations, (2) an e‐folding length scale for subsurface soil moisture profile covariance fall‐off, and (3) the best second‐moment mass‐conserving surface layer thickness for a simple bucket model, defined by the data streams of satellite soil moisture and precipitation retrievals. Calculations are simple, based on three variables: the autocorrelation and variance of surface soil moisture and the variance of the net flux into the column (precipitation minus estimated losses), which can be estimated directly from the soil moisture and precipitation time series. We develop a method to calculate the lag‐one autocorrelation for irregularly observed time series and show global surface soil moisture autocorrelation. λ is driven in part by local hydroclimate conditions and is generally larger than the 50‐mm nominal radiometric length scale for the soil moisture retrievals, suggesting broad subsurface correlation due to moisture drainage. In all but the most arid regions, radiometric soil moisture retrievals provide more information about ecosystem‐relevant water fluxes than satellite radiometers can explicitly "see"; lower‐frequency radiometers are expected to provide still more statistical information about subsurface water dynamics. Key Points: A subsurface coupling length scale is established to estimate total column water changes from surface soil moisture observationsThe length scale is equivalent to the e‐folding scale for soil profile covariance and the appropriate mass‐conserving surface layer thickness for a bucket modelSatellite‐retrieved soil moisture provides information on more soil water than satellite radiometers can "see" for most of Earth's land surface [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) mission currently provides three surface soil moisture products based solely on instrument measurements. The three soil moisture products are: 1) the radiometer-only 36 km gridded; 2) a radiometer-only enhanced product gridded at 9 km; and 3) a high-resolution (3 km) SMAP-Sentinel active–passive product. It is important to validate these released SMAP soil moisture products over various land covers and hydroclimatic domains before they are routinely used in scientific research and applications. This paper evaluates SMAP-based soil moisture products for typical Indian conditions of extreme seasonal variability that leads to changes from very wet to dry soil, especially for the paddy dominated region. The assessment metrics indicate that the enhanced passive-only soil moisture product meets the SMAP accuracy requirement of 0.04 m3/m3 during the nongrowing season (NGS) with unbiased root-mean-square error (ubRMSE) values ranging between 0.025 and 0.036 m3/m3. However, this product underperformed during the paddy growing season (GS) with ubRMSE values ranging between 0.063 and 0.097 m3/m3. In addition, the SMAP-Sentinel active–passive soil moisture product shows satisfactory performance during the NGS (ubRMSE, 0.017–0.051 m3/m3), but during the GS, ubRMSE ranged between 0.089 and 0.104 m3/m3. Use of the vegetation water content climatology and low clay fraction in SMAP baseline algorithm (auxiliary database) that mismatched with the actual values may be the possible source of errors and biases in the SMAP soil moisture products. The reported study provides guidelines for the application of enhanced SMAP soil moisture products in India, especially for the tropical region, and provides information that can be used to improve the retrieval algorithm. [ABSTRACT FROM AUTHOR]

Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vegetation parameter retrievals. In order to realize this constraint, an understanding of microwaves interaction with the surface and vegetation across frequencies is necessary. Different microwave frequencies have varied interactions with the soil–vegetation medium and increasing penetration into the soil and canopy with the decreasing frequency. In this study, we examine the contributions of different scattering mechanisms to coincident observations from two microwave frequencies (L and P) of airborne synthetic aperture radar instruments. We quantify contributions of surface, vegetation volume, and double-bounce scattering components. Results are analyzed and discussed to guide future multi-frequency retrieval algorithm designs. [ABSTRACT FROM AUTHOR]

The U.S. Department of Agriculture National Agricultural Statistics Survey (NASS) collects and publishes crop growth status and soil moisture conditions in major U.S. agricultural regions. The operationally produced weekly reports are based on survey information. The surveys are based on visual assessments and—in the case of soil moisture—report soil moisture levels in one of four categories (Very Short, Short, Adequate, and Surplus). In this study, we show that these reports have remarkable correspondence with the National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) Level‐4 Soil Moisture (L4SM) product. This consistency allows for combining the two different types of data to produce a value‐added assessment, which enables cropland soil moisture mapping and state‐level statistics. Moreover, it enables daily assessment rather than weekly. In this study classification thresholds are derived for L4SM by mapping cumulative distribution functions of L4SM surface and root zone SM to the categorical NASS SM conditions. The results show that, year over year, the SMAP cumulative SM distributions are consistent with the NASS SM conditions and, furthermore, that the temporal evolution of the SMAP‐derived thresholds is consistent with the seasonal crop growth cycles from year to year. The results signify that the SMAP SM retrievals are relatable to SM estimation conducted in agricultural crop land by land managers and farmers, which underlines the general applicability of the SMAP data. Key Points: USDA NASS surveyed soil moisture has a remarkable correspondence with the soil moisture measured by NASA's SMAP missionThe consistency allows a production of a value‐added combination of the two data setsThe combination features mapped fields rather than state‐by‐state tables, and it is refreshed daily rather than weekly [ABSTRACT FROM AUTHOR]

Land–atmosphere feedbacks occurring on daily to weekly time scales can magnify the intensity and duration of extreme weather events, such as droughts, heat waves, and convective storms. For such feedbacks to occur, the coupled land–atmosphere system must exhibit sufficient memory of soil moisture anomalies associated with the extreme event. The soil moisture autocorrelation e-folding time scale has been used previously to estimate soil moisture memory. However, the theoretical basis for this metric (i.e., that the land water budget is reasonably approximated by a red noise process) does not apply at finer spatial and temporal resolutions relevant to modern satellite observations and models. In this study, two memory time scale metrics are introduced that are relevant to modern satellite observations and models: the "long-term memory" τ L and the "short-term memory" τ S . Short- and long-term surface soil moisture (SSM) memory time scales are spatially anticorrelated at global scales in both a model and satellite observations, suggesting hot spots of land–atmosphere coupling will be located in different regions, depending on the time scale of the feedback. Furthermore, the spatial anticorrelation between τ S and τ L demonstrates the importance of characterizing these memory time scales separately, rather than mixing them as in previous studies. [ABSTRACT FROM AUTHOR]

The partitioning of incident precipitation into evapotranspiration, runoff, drainage, storage change, and hydrologic fluxes depends on the soil moisture state. With the availability of global remotely sensed soil moisture fields, the functional dependence of each flux on soil moisture may be identifiable. In this study we develop an observation‐driven approach to map key hydroclimatology fields using remotely sensed soil moisture and gauge‐based precipitation data only. National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) low‐frequency microwave brightness temperature observations and precipitation fields, from the National Centers for Environmental Prediction, are the sole inputs into an adjoint‐state variational estimation framework. Furthermore, the proposed methodology does not rely on micrometeorological information, or land surface models. The approach is flexible by design so that almost any partitioning pattern can result from estimation and corresponding evapotranspiration and drainage fields can be quantified. Three‐year averaged summer season evapotranspiration estimates are compared with available vapor flux at in situ AmeriFlux eddy‐covariance sites. Basin‐averaged drainage over major U.S. hydrologic units is also compared with U.S. Geological Survey streamgages measurements. The remote sensing‐based estimated hydroclimate fields explain about 70% of the variance in the in situ measurements. This exploratory study adds to the body of evidence emerging in literature that a significant amount of hydrologic information is encoded in the dynamic fields of remotely sensed soil moisture. Observation‐driven hydroclimate data fields that are independent of land surface models can provide valuable insights into the state of the water cycle and guide future development of land surface models. Key Points: A significant amount of hydrologic information is encoded in the dynamic fields of surface soil moistureVariational estimation is used to partition precipitation into evapotranspiration and drainage fluxesObservation‐driven maps of evapotranspiration and drainage hydroclimates explain about 70% of their corresponding in situ variance [ABSTRACT FROM AUTHOR]

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function $\beta $ is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of $\beta $ , which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of $\beta $ that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale. [ABSTRACT FROM AUTHOR]

Evapotranspiration and photosynthetic carbon uptake are both constrained by water availability, with presumed water‐limited and water‐independent but energy‐limited regimes. We investigate water availability's relationships with evaporative fluxes and photosynthetic measures at continental scale using surface and satellite‐based observations. The observation‐driven approach is independent of prior model‐based estimates. We find water‐limited and water‐independent regimes for both energy and carbon cycle processes in spatial and temporal aggregate, differences in water‐limitation relationships across ecohydrological regions, and more consistent signs of water limitation in photosynthetic than evaporative processes. Ecosystem water use efficiency's response to water availability changes in sign with aridity state across time and space generally but shows negative correlations in temporal anomalies. Plain Language Summary: Evaporation and plant CO2 uptake both require water to be available at the land surface. From satellite and weather station data, we find simple relationships between these processes and the amount of available water. Even though evaporation and CO2 uptake are linked through water loss from plant leaves, we find that the relationships between each of these and the amount of available soil water are different; it seems that plant CO2 uptake is similarly sensitive to the amount of available water, in wet and dry environments, while evaporation is much more sensitive in dry environments. The amount of water used in the landscape for similar amounts of photosynthesis changes with soil wetness in the Western United States but remains relatively constant in the eastern United States. Key Points: Water‐limited and energy‐limited regimes are observed in the energy and carbon cycles at scale, which vary with regionGross primary productivity and solar‐induced fluorescence are similar in response to moisture limitation/abundanceEcosystem water use efficiency's response to water availability changes in sign with aridity state [ABSTRACT FROM AUTHOR]

Accurately characterizing evapotranspiration is critical when predicting the response of the hydrologic cycle to climate change. Although Earth system models estimate similar magnitudes of global evapotranspiration, the magnitude of each contributing source varies considerably between models due to the lack of evapotranspiration partitioning data. Here we develop an observation‐based method to partition evapotranspiration into soil evaporation and transpiration using meteorological data and satellite soil moisture retrievals. We apply the methodology at 1,614 weather stations across the continental United States during the summers of 2015 and 2016. We evaluate the method using vegetation indices inferred from satellites, finding strong spatial correlations between modeled transpiration and solar‐induced fluorescence (r2 = 0.87), and modeled vegetation fraction and leaf area index (r2 = 0.70). Since the sensitivity of evapotranspiration to environmental factors depends on the contribution of each source component, understanding the partitioning of evapotranspiration is increasingly important with climate change. Plain Language Summary: Water moves from the land surface to the overlying atmosphere by evaporation. The two main sources of evaporation include (1) evaporation from soils and (2) evaporation from pores on plants, called transpiration. Although methods exist to measure total evaporation over an ecosystem, it is challenging to observe soil evaporation and transpiration separately over an ecosystem. Consequently, the amount of estimated soil evaporation and transpiration varies considerably across models. In this study, we develop an observation‐based method to estimate the fraction of water moved from the land to the atmosphere by plants, or the fraction of total evaporation that comes from transpiration. The method primarily relies on weather station data and soil moisture estimates from a recently launched satellite. We apply the method across the continental United States during the summers of 2015 and 2016 and evaluate it using observations of plants inferred from other satellites. Looking toward the future, it is important to estimate transpiration and soil evaporation correctly because they respond differently to changes in climate. Key Points: Summertime evapotranspiration is partitioned into soil evaporation and transpiration using an observation‐based statistical modelThe statistical model relies on data from weather stations and remotely sensed soil moisture from the Soil Moisture Active Passive satelliteEvapotranspiration partitioning estimates are consistent with satellite‐inferred leaf area index and solar‐induced fluorescence [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m 3 /m 3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m 3 /m 3 . The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture. [ABSTRACT FROM AUTHOR]