Soil Moisture Active Passive Brightness Temperature Research Articles

Evaluating the effective resolution of enhanced resolution SMAP brightness temperature image products

The MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) includes conventional- and enhanced-resolution radiometer brightness temperature (TB) images on standard, compatible grids from calibrated satellite radiometer measurements collected over a multi-decade period. Recently, the CETB team processed the first 4 years of enhanced resolution Soil Moisture Active Passive (SMAP) L-band (1.41 GHz) radiometer TB images. The CETB processing employs the radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm to create enhanced resolution images, which are posted on fine resolution grids. In this paper, we evaluate the effective resolution of the SMAP TB image products using coastline and island crossings. We similarly evaluate the effective resolution of the SMAP L1C_TB_E enhanced resolution product that is based on Backus-Gilbert processing. We present a comparison of the spatial resolution of the rSIR and L1C_TB_E enhanced resolution products with conventionally-processed (gridded) SMAP data. We find that the effective resolution of daily CETB rSIR SMAP TB images is slightly finer than that of L1C_TB_E and about 30% finer than conventionally processed data.

From field observations to temporally dynamic soil surface roughness retrievals in the U.S. Corn Belt

Soil surface roughness in the U.S. Corn Belt varies outside of the growing season due to rainfall and soil management activities such as tillage. This is not consistent with the assumption that roughness is a static parameter in the Soil Moisture Active Passive (SMAP) soil moisture retrieval algorithm. SMAPVEX16-IA, an extensive field campaign focused on calibration of SMAP retrievals in croplands, occurred during a period when roughness is theoretically at a minima. Satellite-scale measurements of rms height and correlation length collected via pinboard, gridboard, and lidar methodologies resulted in a ensemble of model roughness coefficients that, at h = [0.13, 0.97], is significantly rougher than the original SMAP assumption in croplands of h = 0.108. Comparing simulated brightness temperatures using the ensemble h to SMAP observations resulted in a best-fit angular sensitivity coefficient of N=−2 for both horizontal and vertical polarizations. Satellite-scale h is then retrieved for a four-year study period (excluding growing seasons) via the single channel algorithm from SMAP brightness temperatures and in situ observations of soil moisture and temperature. Roughness retrievals are highly variable in the spring as rainfall events decrease h followed by an increase during soil dry-down; h steadily increases in the fall post-harvest as fields are tilled across the network. Retrievals of h are effectively independent of polarization and demonstrate the potential to simultaneously retrieve soil moisture and roughness in the absence of crops. These soil roughness retrievals could provide new information on tillage and other soil management activities.

Reconstruct SMAP brightness temperature scanning gaps over Qinghai-Tibet Plateau

• SMAP brightness temperature ( TB ) scanning gaps were compensated. • Daily variation of TB was analyzed in function of influencing factors. • Seamless TB benefits the refinements of SMAP soil moisture products. Soil Moisture Active Passive (SMAP) satellite was used to monitor global soil moisture and freeze–thaw state using surface brightness temperature (TB). However, due to the limitation of scanning width and shifting track, the daily TB observation had stripe gaps in varying degrees, which imped scientific applications of SMAP official product. To solve this issue, we proposed a temporal fitting algorithm to produce daily seamless TB data over Qinghai-Tibet Plateau (QTP) from 2015 to 2021. This method was composed of two steps: (1) overall time trend fitting which used a spline function to fit the overall mean time trend for each pixel from 2015 to 2021; (2) daily variation correction which accounted for several influential factors including surface temperature, precipitation, and vegetation to establish an Ordinary Least Squares (OLS) model for predicting daily variation of TB in numerous time slices. Consequently, the missing TB observation was reconstructed by combining the overall temporal trend and daily variation. A 10-fold cross-validation and product comparison were carried out to validate the robustness of the proposed method. For cross-validation, we randomly removed available SMAP observations in certain periods as unavailable, and then compared the reconstructed values with the removed practical ones. The cross-validation results indicated the reconstructed TB agreed well with actual SMAP TB, with a high overall coefficient of determination (R 2 = 0.94) and low RMSE (RMSE = 3.7 K), implying the desirable performance of our proposed method. By comparison with Soil Moisture and Ocean Salinity (SMOS) TB, the reconstructed data had an approximate performance with actual SMAP TB. Furthermore, the effects of surface temperature and precipitation on TB in different periods (frozen and unfrozen) over QTP were also analyzed. Our method has the potential to generate seamless SMAP products.

Downscaling SMAP Brightness Temperatures to 3 km Using CYGNSS Reflectivity Observations: Factors That Affect Spatial Heterogeneity

NASA’s Soil Moisture Active Passive (SMAP) mission only retrieved ~2.5 months of 3 km near surface soil moisture (NSSM) before its radar transmitter malfunctioned. NSSM remains an important area of study, and multiple applications would benefit from 3 km NSSM data. With the goal of creating a 3 km NSSM product, we developed an algorithm to downscale SMAP brightness temperatures (TBs) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data. The purpose of downscaling SMAP TB is to represent the spatial heterogeneity of TB at a finer scale than possible via passive microwave data alone. Our SMAP/CYGNSS TB downscaling algorithm uses β as a scaling factor that adjusts TB based on variations in CYGNSS reflectivity. β is the spatially varying slope of the negative linear relationship between SMAP emissivity (TB divided by surface temperature) and CYGNSS reflectivity. In this paper, we describe the SMAP/CYGNSS TB downscaling algorithm and its uncertainties and we analyze the factors that affect the spatial patterns of SMAP/CYGNSS β. 3 km SMAP/CYGNSS TBs are more spatially heterogeneous than 9 km SMAP enhanced TBs. The median root mean square difference (RMSD) between 3 km SMAP/CYGNSS TBs and 9 km SMAP TBs is 3.03 K. Additionally, 3 km SMAP/CYGNSS TBs capture expected NSSM patterns on the landscape. Lower (more negative) β values yield greater spatial heterogeneity in SMAP/CYGNSS TBs and are generally found in areas with low topographic roughness (<350 m), moderate NSSM variance (~0.01–0.0325), low-to-moderate mean annual precipitation (~0.25–1.5 m), and moderate mean Normalized Difference Vegetation Indices (~0.2–0.6). β values are lowest in croplands and grasslands and highest in forested and barren lands.

A deep neural network based SMAP soil moisture product

In this paper, it is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas – when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) – using NASA's Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015–03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.

A Physics-Based Algorithm to Couple CYGNSS Surface Reflectivity and SMAP Brightness Temperature Estimates for Accurate Soil Moisture Retrieval

Remotely sensed soil moisture (SM) with high accuracy and high spatial&#x2013;temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth (<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>) and the surface roughness parameter (<inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula>). The CYGNSS L1 Version 2.1 data of the years 2017&#x2013;2018 and 2019&#x2013;2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and <i>in situ</i> SM data on a global scale (<inline-formula> <tex-math notation="LaTeX">$R =0.679$ </tex-math></inline-formula>, RMSE <inline-formula> <tex-math notation="LaTeX">$=0.051\,\,\text{m}^{3}\text{m}^{-3}$ </tex-math></inline-formula>, and MAE <inline-formula> <tex-math notation="LaTeX">$=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ </tex-math></inline-formula> against SMAP SM; <inline-formula> <tex-math notation="LaTeX">$R = 0.729$ </tex-math></inline-formula> against <i>in situ</i> SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2&#x0025; and spatial coverage increased by 31.6&#x0025;.

An Intercomparison Study of Algorithms for SMAP Brightness Temperature Resolution Enhancement With or Without Information From AMSR2

Soil moisture is an essential variable for understanding water and heat exchanges between land and the atmosphere. Presently, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band remote sensing technology has been widely employed for routine measurement of soil moisture from space. However, the spatial resolution of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band soil moisture products obtained from microwave radiometers is too low (dozens of kilometres) to meet the needs of practical applications, such as hydrology modeling, weather forecasts, agricultural applications and water resource management. Therefore, this article proposes a new concept to downscale the soil moisture active passive (SMAP) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band brightness temperature by using advanced microwave scanning radiometer-2 (AMSR2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band TB data, including the time-series regression (TSR) algorithm and two-dimensional discrete wavelet transform algorithm. An intercomparison study was conducted over a semiarid area located in the Shandian river basin with the other two algorithms of the Backus–Gilbert (BG) optimal interpolation and natural neighbor interpolation without using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band TB data. The results revealed that the BG algorithm outperformed the NNI, 2D-DWT, and TSR algorithms compared with the original 36-km SMAP TB and airborne 1-km TB data. However, the soil moisture retrievals within one 9-km pixel with 8 soil moisture stations showed that the downscaled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band TB with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -band data are reliable with lower unbiased root-mean-squared errors compared with resolution-enhanced TB without AMSR2 data.

Classification of the frozen/thawed surface state of Northern land areas based on SMAP and GCOM-W1 brightness temperature observations at 1.4 GHz and 6.9 GHz

ABSTRACT In this letter, the method created earlier by the authors and the information product SPL3FTP_E of the Soil Moisture Active Passive (SMAP) satellite for determining frozen/thawed state of soil surface on the example of test sites placed on North Slope of Alaska, U.S.A., Canada, Finland and Russian Federation were compared. As an indicator of the frozen/thawed state of soil surface, the polarization index calculated on the basis of the reflectivity of soils was proposed. The soil reflectivity was determined in the L-band based on the SMAP radiometric observations at a frequency of 1.4 GHz using the values of brightness temperatures measured by the Global Change Observation Mission – Water 1/Advanced Microwave Scanning Radiometer 2 (GCOM-W1/AMSR2) at a vertical polarization and a frequency of 6.9 GHz, as an estimate of the soil effective temperature. As a result, it was shown that the developed method makes it possible to increase accuracy of the frozen/thawed states determination of soil surface from 3% to 9% in relation to the SMAP data (SPL3FTP_E) for twelve Arctic test sites.

Comparison of soil dielectric mixing models for soil moisture retrieval using SMAP brightness temperature over croplands in India

The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models -- Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R2) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 m3/m3), followed by Wang & Schmugge model (RMSE = 0.08 m3/m3), Hallikainen model (RMSE = 0.09 m3/m3), Dobson model (RMSE = 0.10 m3/m3) and original SMAP radiometer SM (RMSE = 0.12 m3/m3). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms.

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

Abstract. The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

The Soil Moisture Active Passive (SMAP) satellite provides global soil moisture products with reliable accuracy since 2015. However, significant gaps of SMAP soil moisture appeared over Tibetan Plateau. To address this issue, we proposed two methods, machine learning and geostatistics technique to fill the spatial gaps of SMAP L3 soil moisture. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 soil moisture using a series of input variables such as SMAP brightness temperature (TBH, TBV) in ascending orbits, surface temperature, MODIS NDVI, land cover, DEM and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits to reconstruct complete soil moisture data over the Tibetan Plateau. For the geostatistics technique, the Ordinary Kriging was applied to the available SMAP L3 soil moisture pixels to interpolate complete soil moisture data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the SMAP official SM products with high coefficients of determination (R2_RF = 0.97, R2_OK = 0.85) and low RMSE (RMSE_RF = 0.015 cm3/cm3, RMSE_OK = 0.036 cm3/cm3). Furthermore, the gap-filling soil moisture data present a better correlation with the SMOS soil moisture data (R = 0.55 ~ 0.7) than the GLDAS simulations (R = 0.18 ~ 0.62).

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation System.

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ~0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ~700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ~0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates

Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications.

Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals

AbstractSurface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.

A comparison of SMAP and SMOS L-band brightness temperature observations over the global landmass

ABSTRACT This study compared the consistency of brightness temperature (Tb) measurements over the global landmass as observed by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) radiometers. We evaluated the consistency of horizontally and vertically polarized (H-Pol and V-Pol) SMAP and SMOS Tb data via the Pearson correlation coefficient (r), the bias (B, SMOS minus SMAP), and the Hovmöller diagram analysis. The r and B results were displayed and analyzed through the global land covers and Koppen climates. The results indicate a strong consistency of SMAP and SMOS Tb data for the r (mostly > 0.8) and B (mostly varying from −5 to 5 K) metrics. For different land covers and climates, relatively low r values (< 0.8) were found for the continental climates, deciduous needleleaf forests, mixed forests, and barren/sparsely vegetated land covers. The SMOS Tb gave warmer values with respect to the SMAP Tb over the global landmass at the dual polarizations. But the B values are larger at the V-Pol (global average is 0.70 K) than at the H-Pol (global average is 0.16 K). Only the croplands yielded significantly positive B values at the H-Pol. The largest discrepancies of SMAP and SMOS Tb data were observed in Eurasia and area of eastern North Africa and we speculate that the RFI (Radio Frequency Interference) contamination from the SMOS is a reason that may explain this phenomenon. We suggest users pay careful attention to the RFI contamination when using SMOS Tb in applications. As revealed by the Hovmöller diagrams, SMAP and SMOS Tb data all resolved the same seasonally dynamic changes for both dual polarizations. This study is helpful to construct a long-term and reliable Tb product by combining SMAP and SMOS Tb data to retrieve soil moisture information.

Enhanced-Resolution SMAP Brightness Temperature Image Products

The NASA-sponsored Calibrated Passive Micro- wave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record Project team has generated a multisensor, multidecadal time series of high-resolution radiometer products designed to support climate studies. This project uses image reconstruction techniques to generate conventional and enhanced-resolution daily brightness temperature images on a standard set of map projections. Sensors included in CETB are the Aqua Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Scanning Multichannel Microwave Radiometer, and all Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder radiometers. These span frequencies between 6 and 89 GHz. This paper considers the issues of adding the L-band (1.6 GHz) Soil Moisture Active Passive (SMAP) radiometer measurements to the CETB climate record, with emphasis on optimizing the reconstruction to provide the highest possible spatial resolution at the lowest noise level. SMAP radiometer reconstruction on SMAP-standard grids is also considered. Simulation is used to optimize the reconstruction, and the results confirmed using actual data. A comparison of the performance of the Backus–Gilbert approach and the radiometer form of the Scatterometer Image Reconstruction algorithm is provided. These are compared to the conventional drop-in-the-bucket gridded imaging.

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

AbstractThe Soil Moisture Active Passive (SMAP) level 4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, a quantitative estimate of the relative skill of SMAP Level‐4 and model‐only surface soil moisture (vs. true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high‐quality, ground‐based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data‐rich areas like the United States and Europe. In contrast, much larger improvement is observed in data‐sparse regions, including much of Africa and central Australia, where model‐only simulations are disproportionately impacted by low‐quality model forcing. Therefore, ground validation conducted in data‐rich areas does not adequately sample the added value of SMAP data assimilation for data‐sparse regions and substantially underestimates the added skill provided by the SMAP level 4 system.

Mapped Hydroclimatology of Evapotranspiration and Drainage Runoff Using SMAP Brightness Temperature Observations and Precipitation Information

AbstractThe 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.

SMOS and SMAP Brightness Temperature Assimilation Over the Murrumbidgee Basin

With the launch of the Soil Moisture and Ocean Salinity (SMOS) mission in 2009 and the Soil Moisture Active-Passive (SMAP) mission in 2015, a wealth of L-band brightness temperature (Tb) observations has become available. In this letter, SMOS and SMAP Tbs are assimilated separately into the Community Land Model over the Murrumbidgee basin in south-east Australia from April 2015 to August 2017. To overcome the seasonal Tb observation-minus-forecast biases, Tb anomalies from the seasonal climatology are assimilated. The use of climatologies derived from either SMOS or SMAP observations using either 2 years or 7 years of data yields nearly identical results, highlighting the limited sensitivity to the climatology computation and their interchangeability. The temporal correlation between soil moisture data assimilation results and in situ observations is slightly improved for top-layer soil moisture (+0.04) and for root-zone soil moisture (+0.05). The soil moisture anomaly correlation improves moderately for the top-layer soil moisture (+0.15), with a smaller positive impact on the root zone (+0.05).

Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics.

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