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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Assimilation of Sentinel 1 and SMAP–based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins

In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small (< 500 km2) and hydrologically different catchments located in Central Italy. We ingested 1) the Soil Moisture Active and Passive (SMAP) Enhanced L3 Radiometer Global Daily 9 km EASE-Grid soil moisture, 2) the Advanced SCATterometer (ASCAT) H113 soil moisture product released within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF) which has a nearly daily temporal resolution and sampling of 12.5 km, and 3) a fused ASCAT/Sentinel-1 (S1) satellite soil moisture product named SCATSAR-SWI with temporal and spatial sampling of 1 day and 1 km, respectively into SWAT hydrological model via the Ensemble Kalman Filter (EnKF). Different configurations were tested with the aim of exploring the effect of the hydrological regime, the land use conditions, the spatial sampling and the revisit time of the products (which controls the amount of available data to be potentially ingested). Results show a general improvement of SWAT discharge simulations for all products in terms of error and Nash Sutcliffe efficiency index. In particular, we found a relatively good behavior of both the active and the passive products in terms of low flows improvement especially for the catchment characterized by a higher baseflow component. The benefit of the higher spatial resolution of SCATSAR-SWI obtained via S1 over ASCAT was small, likely due to very challenging areas for the S1 retrieval. Eventually, better performances were obtained for the passive product in the more forested catchment. With the aim of exploring the benefit of having more frequent satellite soil moisture observations to be ingested, we tested the performance of the ASCAT product with a reduced temporal sampling obtained by temporally matching ASCAT observations to that of SMAP. The results show a significant reduction of the performance of ASCAT, suggesting that the correction frequency (due to the higher number of observations available) for small catchments is an important aspect for improving flood forecasting as it helps to adjust more frequently the pre-storm soil moisture conditions.

Shima Azimi↗
NASA NTRS2023

Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.

Soil moisture↗
NASA NTRS2021

Leveraging NASA Soil Moisture Active Passive for Assessing Fire Susceptibility and Potential Impacts over Australia and California

Wildfires are a major concern around the globe because of the immediate impact they have on people's lives, local ecosystems, and the environment. Soil moisture is one of the most important factors that influences wildfire occurrences and spread. However, it is also one of the most challenging hydrological variables to measure routinely and accurately. Therefore, soil moisture is significantly underutilized in operational wildfire risk applications. Thus, the aim here is to use a well-established operational soil moisture product to isolate the soil moisture-fire relationship and assess the utility of using soil moisture as a leading indicator of potential fire risk. We evaluated the value of remotely-sensed soil moisture observations from the Soil Moisture Active Passive (SMAP) sensor for monitoring and predicting fire risk in Australia and California. We quantified the relationship between observed fire activity and soil moisture conditions and analyzed the soil moisture conditions for two extreme fire events. Our findings show that fire activity is strongly associated with soil moisture anomalies. Lagged correlation analysis demonstrated that a remote-sensing based soil moisture product could predict fire activity with a 1-2 month lead-time. Soil moisture anomalies consistently decreased in the months preceding fire occurrence, often from normal to drier conditions, according to a spatiotemporal analysis of soil moisture in two extreme fire events. Overall, our findings indicate that soil moisture conditions prior to large wildfires can aid in their prediction and operational satellite-based soil moisture products such as the one used here have real value for supporting wildfire susceptibility and impacts.

Nazmus Sazib↗
NASA NTRS1983

A microwave systems approach to measuring root zone soil moisture

Computer microwave satellite simulation models were developed and the program was used to test the ability of a coarse resolution passive microwave sensor to measure soil moisture over large areas, and to evaluate the effect of heterogeneous ground covers with the resolution cell on the accuracy of the soil moisture estimate. The use of realistic scenes containing only 10% to 15% bare soil and significant vegetation made it possible to observe a 60% K decrease in brightness temperature from a 5% soil moisture to a 35% soil moisture at a 21 cm microwave wavelength, providing a 1.5 K to 2 K per percent soil moisture sensitivity to soil moisture. It was shown that resolution does not affect the basic ability to measure soil moisture with a microwave radiometer system. Experimental microwave and ground field data were acquired for developing and testing a root zone soil moisture prediction algorithm. The experimental measurements demonstrated that the depth of penetration at a 21 cm microwave wavelength is not greater than 5 cm.

Newton, R. W.↗
NASA NTRSDate not supplied

Isolating the Impacts of Soil Moisture Initialization on Land Carbon Fluxes at the Sub-Seasonal to Seasonal Scale

Soil moisture, through its impact on transpiration, plays a significant role in controlling the productivity of terrestrial vegetation. Initial soil moisture conditions in a seasonal forecast may therefore affect the forecast of land carbon uptake. Here we investigate the relative impacts of soil moisture initialization and carbon reservoir initialization on forecasts of land carbon fluxes at the sub-seasonal to seasonal (S2S) scale. The bias-corrected, retrospective meteorology of NASA Global Modeling and Assimilation Office (GMAO)’s S2S ensemble forecast was used to force the stand-alone Catchment-CN model and thereby estimate terrestrial carbon responses out to nine lead months. Our results show that soil moisture initialization is a major contributor (approximately 44%) to the high global carbon uptake forecast skill seen during the first three lead months. The carbon reservoir initialization explains roughly another half of the monthly carbon forecast skill during this period and becomes relatively more important at longer leads (while the overall forecast skill decreases after the 3 lead month), suggesting a slower but longer-lasting influence of carbon reservoir initialization on carbon fluxes. Our results highlight the significance of a good soil moisture initialization for improved forecasts of carbon fluxes at leads of several months, further support for the usefulness of assimilating the satellite based soil moisture information into terrestrial biosphere models and of a short term carbon forecast to understand current events with often a lag in the availability of flux estimates.

Land carbon↗
NASA NTRS1982

Multifrequency remote sensing of soil moisture

Multifrequency sensor data collected at Guymon, Oklahoma and Dalhart, Texas using NASA's C-130 aircraft were used to determine which of the all-weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. In comparison to other active and passive microwave sensors the L-band radiometer (1) was influenced least by ranges in surface roughness; (2) demonstrated the most sensitivity to soil moisture differences in terms of the range of return from the full range of soil moisture; and (3) was less sensitive to errors in measurement in relation to the range of sensor response. L-band emissivity related more strongly to soil moisture when moisture was expressed as percent of field capacity. The perpendicular vegetation index as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture.

Theis, S. W.↗
NASA NTRS2022

Soil Carbon Losses Reduce Soil Moisture in Global Climate Model Simulations

Most agricultural soils have experienced substantial soil organic carbon losses in time. These losses motivate recent calls to restore organic carbon in agricultural lands to improve biogeochemical cycling and for climate change mitigation. Declines in organic carbon also reduce soil infiltration and water holding capacity, which may have important effects on regional hydrology and climate. To explore the regional hydroclimate impacts of soil organic carbon changes, we conduct new global climate model experiments with NASA Goddard Institute for Space Studies ModelE that include spatially explicit soil organic carbon concentrations associated with different human land management scenarios. Compared to a “no land use” case, a year 2010 soil degradation scenario, in which organic carbon content (OCC; weight %) is reduced by a factor of ∼0.12 on average across agricultural soils, resulted in soil moisture losses between 0.5 and 1 temporal standard deviations over eastern Asia, northern Europe, and the eastern United States. In a more extreme idealized scenario where OCC is reduced uniformly by 0.66 across agricultural soils, soil moisture losses exceed one standard deviation in both hemispheres. Within the model, these soil moisture declines occur primarily due to reductions in porosity (and to a lesser extent infiltration) that overall soil water holding capacity. These results demonstrate that changes in soil organic carbon can have meaningful, large-scale effects on regional hydroclimate and should be considered in climate model evaluations and developments. Further, this also suggests that soil restoration efforts targeting the carbon cycle are likely to have additional benefits for improving drought resilience.

Atmosphere-land interaction↗
NASA NTRS1980

Microwave remote sensing of soil moisture content over bare and vegetated fields

Remote measurements of soil moisture contents over bare fields and fields covered with orchard grass, corn, and soybean were made during October 1979 with 1.4 GHz and 5 GHz microwave radiometers mounted on a truck. Ground truth of soil moisture content, ambient air, and soil temperatures was acquired concurrently with the radiometric measurements. The biomass of the vegetation was sampled about once a week. The measured brightness temperatures over bare fields were compared with those of radiative transfer model calculations using as inputs the acquired soil moisture and temperature data with appropriate values of dielectric constants for soil-water mixtures. Good agreement was found between the calculated and the measured results over 10-70 deg incident angles. The presence of vegetation was found to reduce the sensitivity of soil moisture sensing. At 1.4 GHz the sensitivity reduction ranged from approximately 20% for 10-cm tall grassland to over 60% for the dense soybean field. At 5 GHz the corresponding reduction in sensitivity ranged from approximately 70 to approximately 90%.

Wang, J. R.↗
NASA NTRS2023

Considering Soil Moisture in Models of Climate Impacts on Child Health in Farming-Centric Countries

Soil moisture reflects the amount of water available to crops in the top layer of soil. As such, considering soil moisture provides important insight into water availability and ultimately crop yields in agricultural settings. In studies of climate change, food security, and health, however, soil moisture is rarely empirically considered despite its connection to crop health and yields. In this project, we aim to advance understanding of climate impacts on food security by incorporating soil moisture into quantitative models of child health. Combining spatially referenced health survey data from the Demographic and Health Surveys for 2005 and 2010 in Senegal and 2007, 2011, and 2014 in Bangladesh, with soil moisture data from the Famine Early Warning System Network Land Data Assimilation System, we explore the linkages between sub-annual and sub-seasonal climate conditions and child malnutrition in two rainfed agriculture dependent countries—Bangladesh and Senegal. Results suggest that soil moisture, measured on very short time scales, may be associated with reductions in anthropometric weight-for-height z-scores, but the relationship is highly dependent upon geographic context.

Child health↗
NASA NTRS1979

Survey of methods for soil moisture determination

Existing and proposed methods for soil moisture determination are discussed. These include: (1) in situ investigations including gravimetric, nuclear, and electromagnetic techniques; (2) remote sensing approaches that use the reflected solar, thermal infrared, and microwave portions of the electromagnetic spectrum; and (3) soil physics models that track the behavior of water in the soil in response to meteorological inputs (precipitation) and demands (evapotranspiration). The capacities of these approaches to satisfy various user needs for soil moisture information vary from application to application, but a conceptual scheme for merging these approaches into integrated systems to provide soil moisture information is proposed that has the potential for meeting various application requirements.

Schmugge, T. J.↗
NASA NTRS1993

Synergism of active and passive microwave data for estimating bare surface soil moisture

Active and passive microwave sensors were applied effectively to the problem of estimating the surface soil moisture in a variety of environmental conditions. Research to date has shown that both types of sensors are also sensitive to the surface roughness and the vegetation cover. In estimating the soil moisture, the effect of the vegetation and roughness are often corrected either by acquiring multi-configuration (frequency and polarization) data or by adjusting the surface parameters in order to match the model predictions to the measured data. Due to the limitations on multi-configuration spaceborne data and the lack of a priori knowledge of the surface characteristics for parameter adjustments, it was suggested that the synergistic use of the sensors may improve the estimation of the soil moisture over the extreme range of naturally occurring soil and vegetation conditions. To investigate this problem, the backscattering and emission from a bare soil surface using the classical rough surface scattering theory were modeled. The model combines the small perturbation and the Kirchhoff approximations in conjunction with the Peak formulation to cover a wide range of surface roughness parameters with respect to frequency for both active and passive measurements. In this approach, the same analytical method was used to calculate the backscattering and emissivity. Therefore, the active and passive simulations can be combined at various polarizations and frequencies in order to estimate the soil moisture more actively. As a result, it is shown that (1) the emissivity is less dependent on the surface correlation length, (2) the ratio of the backscattering coefficient (HH) over the surface reflectivity (H) is almost independent of the soil moisture for a wide range of surface roughness, and (3) this ratio can be approximated as a linear function of the surface rms height. The results were compared with the data obtained by a multi-frequency radiometer-scatterometer system working at frequencies between 3.0 GHz to 11.0 GHz. The data were acquired over bare soil surfaces with moisture variations due to freezing and thawing and roughness changes due to rain and erosion. The model predictions are shown to be in reasonable agreement with the data. In addition, it was shown that the same ratio when calculated from the data shows almost no dependence on the soil moisture. Finally, a simple technique which combines the backscattering coefficient at HH polarization (active sensing) with the emissivity at H polarization (passive sensing) is suggested for retrieving the soil moisture from bare soil surfaces.

Saatchi, Sasan S.↗
NASA NTRS1981

Aircraft radar response to soil moisture

An analysis is presented of aircraft response to soil moisture in the upper surface layer of agricultural fields. Measurements (taken at 1.6 GHz and 4.75 GHz using HH and HV polarizations, and at 13.3 GHz using VV polarization from an experiment conducted in 1978 at Colby, Kansas) are used to derive the radar soil moisture sensitivities and correlations. It is shown that the aircraft response to soil moisture is optimum at C-band frequencies and incidence angles of 10-20 deg. confirming previous truck-radar results. Like-polarization radar response is unaffected by vegetation but is dependent on row-tillage patterns; cross-polarization response also is unaffected by vegetation but is approximately independent of tillage patterns. These results show that remote sensing radars can be used effectively for the detection and estimation of near-surface soil moisture in agricultural fields.

Bradley, G. A.↗
NASA NTRS2022

Gatlinburg & Beatty Wildfires: Evaluating the Role of Soil Moisture in Determining Vegetation Health, Fuel Loads, and Wildfires in the Gatlinburg and Beatty Wildfires

Wildfire potential monitoring, which is increasingly vital under climate change-induced droughts, could be improved by incorporating remotely-sensed soil moisture data. To better understand the connections between soil moisture and vegetation health, stakeholders are interested in using soil moisture data in the development of fire-related indices. NASA DEVELOP partnered with the Desert Research Institute’s Western Regional Climate Center (WRCC), NOAA’s National Integrated Drought Information System (NIDIS), the North Carolina State Climate Office, and Oklahoma State University to evaluate how measures of remotely-sensed standardized soil moisture compare to vegetation health and fire fuel indices in a case study of two fire events: the 2016 Chimney Tops 2 Fire near Gatlinburg, Tennessee and the 2021 Bootleg Fire near Beatty, Oregon. The team visualized vegetation change six months prior to each event using spectral vegetation indices observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite and the Keetch-Byram Drought Index (KBDI). These visualizations were compared to soil moisture data from European Space Agency's (ESA) Climate Change Initiative Soil Moisture (CCI SM) project, collected in part by the Soil Moisture Active Passive (SMAP) satellite. Overall, period of record percentiles and fraction of available water standardizations correlated more strongly with fuel load and vegetation indices, indicating their utility for fire potential monitoring. Soil moisture conditions remained exceptionally dry for several months before the Chimney Tops 2 Fire whereas drought conditions only intensified immediately prior to the Bootleg Fire. This indicates greater sensitivity to drought conditions under Western fire regimes. These findings will inform partners’ monitoring of wildfire potential in both regions and development of early warning systems.

soil moisture↗
NASA NTRS2012

Monte Carlo Analysis of the Commissioning Phase Maneuvers of the Soil Moisture Active Passive (SMAP) Mission

The Soil Moisture Active Passive (SMAP) mission will perform soil moisture content and freeze/thaw state observations from a low-Earth orbit. The observatory is scheduled to launch in October 2014 and will perform observations from a near-polar, frozen, and sun-synchronous Science Orbit for a 3-year data collection mission. At launch, the observatory is delivered to an Injection Orbit that is biased below the Science Orbit; the spacecraft will maneuver to the Science Orbit during the mission Commissioning Phase. The delta V needed to maneuver from the Injection Orbit to the Science Orbit is computed statistically via a Monte Carlo simulation; the 99th percentile delta V (delta V99) is carried as a line item in the mission delta V budget. This paper details the simulation and analysis performed to compute this figure and the delta V99 computed per current mission parameters.

Monte Carlo simulations↗
NASA NTRS2020

Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective

Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km × 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0–5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m(exp 3)/m(exp 3)) followed by Australia (RMSE = 0.08 m(exp 3)/m(exp 3)), Asia (RMSE = 0.09 m(exp 3)/m(exp 3)) and Europe (RMSE = 0.14 m(exp 3)/m(exp 3)). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications.

Soil moisture↗
NASA NTRS2017

The Sensitivity of Ground-Reflected GNSS Signals to Near-Surface Soil Moisture, as Recorded by Spaceborne Receivers

Spatial and temporal variations in near-surface soil moisture are important to measure for climate studies, numerical weather forecasts, and drought monitoring. Several previous studies have shown success in using ground-reflected Global Navigation Satellite System (GNSS) signals as a form of bistatic radar to sense soil moisture. However, the ability of this type of data to sense soil moisture variations from space is still a nascent field of study. In the past two years, three satellites have been launched that were either designed to capture ground-reflected GNSS signals or have been modified to record these signals. The data provided by these satellites are giving scientists an unprecedented opportunity to investigate their ability to detect changes in Earth’s land surface, including but certainly not limited to near-surface soil moisture. This paper will present spaceborne observations of ground-reflected GNSS signals and evaluate their sensitivity to near-surface soil moisture. This sensitivity will be compared to empirical and theoretical sensitivities of monostatic L-band radar measurements to soil moisture. We will also comment on possibilities for retrieval algorithm development, using techniques employed for monostatic radar as a guide.

Chew, C.↗
NASA NTRS1994

The scale and persistence of soil moisture anomalies as simulated in a global model

Short term variability of climate is intimately connected with soil moisture variability. Soil moisture provides the storage and subsequent return to the atmosphere, through evaporation and transpiration, of precipitation anomalies over land. Global Circulation Model (GCM) simulations enable consistent identification of correlations and dynamical connections between the hydrologic variables, many of which are incompletely observed. One way to facilitate understanding with these increasingly intricate models is to perform sensitivity studies in which a boundary condition or process is prescribed. In this study we will report on a sensitivity study in which a GCM with a sophisticated land surface representation is used to investigate soil moisture variability in the model climate. The simulations to be used in this study were made at R15 resolution (approximately 4.5 deg latitude x 7.5 deg longitude) with prescribed sea surface temperatures (SST) in the GENESIS model (Thompson and Pollard, 1994), which is coupled to a Land Surface Transfer model (LSX) at 2 deg x 2 deg resolution (Pollard and Thompson, 1994). All the results represented here were taken from the monthly averages of the model results. The LSX model accounts for the physical effects of vegetation with two layers specified at each grid point. Vegetation attributes such as leaf area indices, fractional cover, leaf albedos, etc., were taken from the global dataset in Dorman and Sellers (1989). A six-layer soil model extends from the surface to 4.25 m depth. SST's were prescribed in two ten year experiments using monthly SST values with the daily value being interpolated from the nearest two months. In the first experiment monthly climatological values were used, and in the second, the Atmospheric Model Intercomparison Project (AMIP) observed SST's for the years 1979 through 1988 were used (Gates, 1992). Thus, the former experiment gives a measure of the intrinsic model variability, to be compared with that of the latter experiment, which includes month-to-month variability due to ocean forcing.

Fitzjarrald, D.↗
NASA NTRS2016

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E's day- and night-time observations.

soil moisture↗