Root-zone Soil Moisture Estimates Research Articles

High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type

Root-zone soil moisture (RZSM) plays a key role for most water and energy budgets, as it is particularly relevant in controlling plant transpiration and hydraulic redistribution. RZSM data is needed for a variety of different applications, such as forecasting crop yields, improving flood predictions and monitoring agricultural drought, among others. Remote sensing provides surface soil moisture (SSM) retrievals, whose key advantage is the large spatial coverage on a systematic basis. This study tests a simple method to retrieve RZSM estimates from high-resolution SSM derived from SMAP (Soil Moisture Active Passive). A recursive exponential filter using a time constant τ is calibrated per land cover type, which uses as an intermediate step a long-term ISBA-DIF (Interaction Soil Biosphere Atmosphere—Diffusion scheme) dataset over an area located in Catalonia, NE of Spain. The τ values thus obtained are then used as an input to the same recursive exponential filter, to derive 1 km resolution RZSM estimates from 1 km SMAP SSM, which are obtained from the original data by downscaling to a 1 km resolution, through the DISPATCH (DISaggregation based on a Physical and Theoretical scale CHange) methodology. The results are then validated with scaled in situ observations at different depths, over two different areas, one representative of rainfed crops, and the other of irrigated crops. In general, the estimates agree well with the observations over the rainfed crops, especially at a 10 cm and 25 cm depth. Nash–Sutcliffe (NS) scores ranging between 0.33 and 0.58, and between 0.37 and 0.56 have been found, respectively. Correlation coefficients for these depths are high, between 0.76 and 0.91 (10 cm), and between 0.71 and 0.90 (25 cm). For the irrigated sites, results are poorer (partly due to the extremely high heterogeneity present), with NS scores ranging between −2.57 and 0.16, and correlations ranging between −0.56 and 0.48 at 25 cm. Given the strong correlations and NS scores found in the surface, the sensitivity of the filter to different τ values was investigated. For the rainfed site, it was found, as expected, with increasing τ, increasing NS and correlations with the deeper layers, suggesting a better coupling. Nevertheless, a strong correlation with the surface (5 cm) or shallower depths (10 cm) observed over certain sites indicates a certain lack of skill of the filter to represent processes which occur at lower levels in the SM column. All in all, a calibration accounting for the vegetation was shown to be an adequate methodology in applying the recursive exponential filter to derive the RZSM estimates over large areas. Nevertheless, the relative shallow surface at which the estimates correlate in some cases seem to indicate that an effect of evapotranspiration in the profile is not well captured by the filter.

Estimation of root-zone soil moisture using crop water stress index (CWSI) in agricultural fields

ABSTRACT Due to the limited availability of Root-Zone Soil Moisture (RZSM) information at the regional scale, this paper explores the use of thermal infrared remote sensing to estimate RZSM in agricultural fields. This study presents the Crop Water Stress Index (CWSI) derived from thermal infrared data used as an indicator to estimate root zone soil moisture. Theoretical limits were calculated using canopy and air temperature difference, which is related to vapor pressure deficit. An empirical model was developed using continuous remotely sensed optical, thermal infrared data with limited meteorological data collected from a wheat site, the Dookie experimental farm, Victoria, Australia during 2012 and 2013. Linear and exponential models predicting RZSM using CWSI were constructed and compared in two different cropping seasons. Cross-validation results demonstrate that the linear model predicted RZSM with an error of 3.9% in 2012 and 5.3% in 2013 cropping seasons. The proposed method is applied to another root-zone soil moisture dataset collected during 2002–04 cropping seasons from a corn field site in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site in the USA. Validation results showed that the model produces reasonable RZSM estimates, except for the high rainfall distribution during cropping seasons even though the crop types of the calibration and validation sites were different. The efficacy of canopy temperature in RZSM estimations was demonstrated using Dookie and OPE3 RZSM dataset. The potential limitation is that sparse vegetation in the initial growth stages produces negative values in CWSI due to the dominant soil surface. Overall, the results support the potential role of the theoretical crop water stress index in root-zone soil moisture estimations.

In-situ and triple-collocation based evaluations of eight global root zone soil moisture products

Root zone soil moisture (RZSM) is a vital variable for vegetation growth, drought monitoring and agricultural water management. Satellite remote sensing measures soil moisture at the surface layer, while RZSM is derived usually by model-based simulations. Here, we provide the first comprehensive evaluation of eight RZSM products at a global scale, including GLDAS NOAH, ERA-5, MERRA-2, NCEP R1, NCEP R2, JRA-55, SMAP level 4 and SMOS level 4 datasets. An in-situ validation based on the stations from the International Soil Moisture Network (ISMN) and a triple collocation (TC) evaluation are both conducted to assess the accuracy of these RZSM products. SMAP exhibits the median highest correlation and the median lowest RMSE with in-situ stations over North America. In the TC analysis, MERRA-2 shows the highest median correlation and the median lowest error standard deviation with the unknown truth, followed by GLDAS, SMAP, JRA-55 and ERA-5. A temporal pattern analysis indicates that SMOS has a dry bias relative to other datasets and NCEP R1 has larger seasonal variations relative to other datasets over Asia and North America. The TC analysis indicates that MERRA-2, SMAP, GLDAS, JRA-55, and ERA-5 have better performance relative to other datasets. SMAP is not as good as GLDAS, MERRA-2 and JRA-55 in RZSM estimation over forest areas. The possible factors influencing RZSM performance are discussed, including precipitation forcing, assimilated observations, radio frequency interference issue and validation methods. These results and conclusions may provide new insights for the improvement of model-based RZSM estimation.

Root zone soil moisture estimation with Random Forest

Accurate estimates of root zone soil moisture (RZSM) at relevant spatio-temporal scales are essential for many agricultural and hydrological applications. Applications of machine learning (ML) techniques to estimate root zone soil moisture are limited compared to commonly used process-based models based on flow and transport equations in the vadose zone. However, data-driven ML techniques present unique opportunities to develop quantitative models without having assumptions on the processes operating within the system being investigated. In this study, the Random Forest (RF) ensemble learning algorithm, is tested to demonstrate the capabilities and advantages of ML for RZSM estimation. Interpolation and extrapolation of RZSM on a daily timescale was carried out using RF over a small agricultural catchment from 2016 to 2018 using in situ measurements. Results show that RF predictions have slightly higher accuracy for interpolation and similar accuracy for extrapolation in comparison with RZSM simulated from a process-based model combined with data assimilation. RF predictions for extreme wet and dry conditions were, however, less accurate. This was inferred to be due to infrequent sampling of such conditions that led to poor learning in the trained RF model and to incomplete representation of relevant subsurface processes at the study sites in the RF covariates. Since RF does not depend on parameters required to estimate subsurface water flow, it is more advantageous than a process-based model in data-poor regions where soil hydraulic parameters are incomplete or missing, especially when the primary goal is only the estimation of soil moisture states.

Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region

Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2‒4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3 (0.037 m3 m−3) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network. The average ubRMSE values are ~0.05‒0.06 m3 m−3 for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.

Impact of regional characteristics on the estimation of root-zone soil moisture from the evaporative index or evaporative fraction

Accurate knowledge of root-zone soil moisture is of vital significance to many applications in agriculture, such as crop yield estimation, precision irrigation, salinity and groundwater management. Remote-sensing methods based on optical and thermal satellite imagery have been proposed to estimate fine-resolution (30 m grid cells) maps of root-zone soil moisture θ¯ or degree of saturation s¯ over large regions. These methods usually calculate the evaporative fraction ΛSEB as the ratio of the latent heat flux to the difference of the net radiation and ground heat flux. Then, they estimate θ¯ or s¯ from an empirical relationship with ΛSEB. A similar approach estimates θ¯ or s¯ using the evaporative index ΛPET, which is the ratio of the actual to potential evapotranspiration. However, previous research has shown that a single relationship between either θ¯ or s¯ and ΛSEB does not apply to all regions. The objective of this study is to evaluate the impact of regional soil, vegetation, and climatic conditions on the form and strength of the ΛSEB−θ¯, ΛPET−θ¯, ΛSEB−s¯, and ΛPET−s¯ relationships. To accomplish this goal, Extended Fourier Amplitude Sensitivity Test (eFAST) is applied to a physically-based model (HYDRUS 1-D) that simulates both evapotranspiration and soil moisture dynamics. The sensitivity results show that, within a given climatic region, soil characteristics such as the percent clay and percent silt are most important in determining the shape of the relationships, while vegetation characteristics such as leaf area index and maximum rooting depth have the greatest effect on the strength of these relationships. The total annual precipitation, which helps determine the climatic region, also has a strong effect on both the form and strength of the relationships. The parameters that define the ΛSEB−θ¯ and ΛPET−θ¯ relationships are also estimated using the regional characteristics. Estimating the parameters in this way allows the methods to be adapted to local conditions and has the potential to improve the θ¯ and s¯ estimates.

Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model.

From an agricultural perspective, drought refers to an unusual deficiency of plant available water in the root-zone of the soil profile. This paper focuses on evaluating the benefit of assimilating soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model for agricultural drought monitoring. This will be done by examining the standardized soil moisture anomaly index. The skill of the SMAP-enhanced Palmer model is assessed over three agricultural regions that have experienced major drought since the launch of SMAP in early 2015: (1) the 2015 drought in California (CA), USA, (2) the 2017 drought in South Africa, and (3) the 2018 mid-winter drought in Australia. During these three events, the SMAP-enhanced Palmer soil moisture estimates (PM+SMAP) are compared against the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall dataset and Normalized Difference Vegetation Index (NDVI) products. Results demonstrate the benefit of assimilating SMAP and confirm its potential for improving U.S. Department of Agriculture-Foreign Agricultural Service root-zone soil moisture information generated using the Palmer model. In particular, PM+SMAP soil moisture estimates are shown to enhance the spatial variability of Palmer model root-zone soil moisture estimates and adjust the Palmer model drought response to improve its consistency with ancillary CHIRPS precipitation and NDVI information.

Cramer-Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing.

Signals of opportunity (SoOp) reflectometry (SoOp-R) is a maturing field for geophysical remote sensing as evidenced by the growing number of airborne and spaceborne experiments. As this approach receives more attention, it is worth analyzing SoOp-R's capabilities to retrieve subsurface soil moisture (SM) by leveraging communication and navigation satellite transmitters. In this research, the CRLB is used to identify the effects of variable SoOp-R parameters on the best achievable estimation error for root-zone soil moisture (RZSM). This study investigates the use of multiple frequency, polarization, and incidence angle measurement configurations on a two-layered dielectric profile. The results also detail the effects of variable SM conditions on the capability of SoOp-R systems to predict subsurface SM. The most prevalent observation is the importance of using at least two frequencies to limit uncertainties from subsurface SM estimates. If at least two frequencies are used, the CRLB of a profile is retrievable within the root-zone depending on the surface SM content as well as the number of independent measurements of the profile. For a depth of 30 cm, it is observed that a CRLB corresponding to 4% RZSM estimation accuracy is achievable with as few as 2 dual-frequency-based SoOp-R measurements. For this depth, increasing number of measurements provided by polarization and incidence angle allow for sensing of increasingly wet SM profile structures. This study, overall, details a methodology by which SoOp-R receiver system can be designed to achieve a desired CRLB using a trade-off study between the available measurements and SM profile.

Soil Moisture Monitoring of the Plant Root Zone by Using Phenology as Context in Remote Sensing

In this study, the phenological behavior and energy balance of plants are used as a sensory mechanism for root-zone soil moisture monitoring using both in-situ and satellite remote sensing data. The commonly used in-situ measurements are not feasible for mapping soil moisture at large-scale agricultural areas. Local direct root-zone soil moisture measurements cannot be reliably interpolated owing to the high spatial variability of soil structure and the vegetative content. Remote sensing methods are negatively affected by vegetation coverage and density regarding penetration and backscattering characteristics. In order to overcome these limitations, we propose a root-zone soil moisture estimation method utilizing a context-aware data clustering process, which can be applied prior to any statistical analysis, for empirical evaluation of data. In this aspect, the crops’ phenological stages and soil–air temperature differences are defined as the two contexts for data clustering. Parameters such as canopy–air temperature difference, land surface temperature, and solar radiation with respect to plant energy and water processes are used for the analysis. The proposed model is utilized using piecewise linear regression of data obtained from 16 rainfed wheat parcels distributed across Turkey, under different climatic and topographic conditions. It is shown that the proposed context-aware data clustering process enables the nonlinear plant behavior to be analyzed linearly. The correlation value of the whole season increased from 21% to a range between 78% and 95% for different clusters. The outliers became relevant and the parameters became significant after the proposed context-aware data clustering.

The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture

AbstractThis study examines the impact of variation in root‐zone soil moisture (RZSM), a key component of the Earth's hydrologic cycle and climate system, on regional carbon fluxes across seven North American ecosystems. P‐band synthetic aperture radar‐derived RZSM estimates were incorporated into the ecosystem demography (ED2) terrestrial biosphere model through a model‐data blending approach. Analysis shows that the model qualitatively captures inter‐daily and seasonal variability of observed RZSM at seven flux tower sites (r = 0.59 ± 0.26 and r = 0.70 ± 0.22 for 0–10 and 10–40 cm of soil layers, respectively; P < 0.001). Incorporating the remotely sensed RSZM estimates increases the accuracy (root‐mean‐square deviations decrease from 0.10 ± 0.07 and 0.09 ± 0.06 m3·m−3 to 0.08 ± 0.05 and 0.07 ± 0.03 m3 ·m−3 for 0–10 and 10–40 cm of soil layers, respectively) of the model's RZSM predictions. The regional carbon fluxes predicted by the native and RZSM‐constrained model were used to quantify sensitivities of gross primary productivity, autotrophic respiration (Ra), heterotrophic respiration (Rh), and net ecosystem exchange to variation in RZSM. Gross primary productivity exhibited the largest sensitivity (6.6 ± 10.7 kg·cm−2·year·θ−1) followed by Ra (2.9 ± 7.3 kg·cm−2·year−1·θ−1), Rh (2.6 ± 3.1 kg·cm−2·year−1·θ−1), and net ecosystem exchange (−1.7 ± 7.8 kg·cm−2·year−1·θ−1). Analysis shows that these carbon flux sensitivities varied considerably across regions, reflecting influences of canopy structure, soil properties, and the ecophysiological properties of different plant functional types. This study highlights (1) the importance of improved terrestrial biosphere model predictions of RZSM to improve predictions of terrestrial carbon fluxes, (2) a need for improved pedotransfer functions, and (3) improved understanding of how soil characteristics, climate, and vegetation composition interact to govern the responses of different ecosystems to changing hydrological conditions.

Validation of a New Root-Zone Soil Moisture Product: Soil MERGE

Soil MERGE (SMERGE) is a 0.125°, root-zone soil moisture (RZSM) product (0–40-cm depth) generated within the contiguous United States (CONUS). This product is developed by merging RZSM output from the North American land data assimilation system (NLDAS) with surface satellite retrievals from the European Space Agency Climate Change Initiative. SMERGE, at present, spans four decades (1979–2016). Here, we introduce the SMERGE approach and describe the validation of SMERGE RZSM estimates using three geophysical observations: 1) comparison with sparse in situ soil moisture data acquired from the soil climate analysis network (SCAN) and the U.S. Climate Reference Network (USCRN); 2) ranked correlation analysis against normalized difference vegetation index (NDVI) datasets; and 3) ranked correlation analysis of antecedent RZSM with storm-event streamflow across a range of precipitation intensities (5–45 mm/day). Relative to in situ SCAN and USCRN observations, SMERGE has an average daily correlation of 0.7–0.8 and unbiased root-mean square error close to 0.04 m3/m3—a level that is commonly applied as a validation target for large-scale soil moisture datasets. NDVI benchmarking allows us to indirectly evaluate SMERGE across CONUS and reveals it can predict near-term vegetation health anomalies with skill comparable to that of RZSM products generated by more complex data assimilation methods. In addition, streamflow-based evaluation results demonstrate that SMERGE antecedent RZSM can be used as a reliable predicator of storm-event runoff efficiency for rainfall events >25 mm/day.

Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model

Root zone soil moisture (RZSM) affects many natural processes and is an important component of environmental modeling, but it is expensive and challenging to monitor for relatively small spatial extents. Satellite datasets offer ample spatial coverage of near-surface soil moisture content at up to a daily time-step, but satellite-derived data products are currently too coarse in spatial resolution to use directly for many environmental applications, such as those for small catchments. This study investigated the use of passive microwave satellite soil moisture data products in a simple hydrologic model to provide root zone soil moisture estimates across a small catchment over a two year time period and the Eastern U.S. (EUS) at a 1 km resolution over a decadal time-scale. The physically based soil moisture analytical relationship (SMAR) was calibrated and tested with the Advanced Microwave Scanning Radiometer (AMSRE), Soil Moisture Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) data products. The SMAR spatial model relies on maps of soil physical properties and was first tested at the Shale Hills experimental catchment in central Pennsylvania. The model met a root mean square error (RMSE) benchmark of 0.06 cm3 cm−3 at 66% of the locations throughout the catchment. Then, the SMAR spatial model was calibrated at up to 68 sites (SCAN and AMERIFLUX network sites) that monitor soil moisture across the EUS region, and maps of SMAR parameters were generated for each satellite data product. The average RMSE for RZSM estimates from each satellite data product is <0.06 cm3 cm−3. Lastly, the 1 km EUS regional RZSM maps were tested with data from the Shale Hills, which was set aside for validating the regional SMAR, and the RMSE between the RZSM predictions and the catchment average is 0.042 cm3 cm−3. This study offers a promising approach for generating long time-series of regional RZSM maps with the same spatial resolution of soil property maps.

State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management

Combining metamodels with data assimilation schemes allows the incorporation of up-to-date information in metamodels, offering new opportunities for operational water resources management. We developed a data assimilation scheme for the unsaturated zone metamodel MetaSWAP using OpenDA, which is an open source data assimilation framework. A twin experiment showed the feasibility of applying an Ensemble Kalman filter as a data assimilation method for updating metamodels. Furthermore, we assessed the accuracy of root zone soil moisture model estimates when assimilating the regional SMAP L3 Enhanced surface soil moisture product. The model accuracy is assessed using in situ soil moisture measurements collected at 12 locations in the Twente region, the Netherlands. Although the accuracy of the model estimates does not improve in terms of correlation coefficient, the accuracy does improve in terms of Root Mean Square Error and bias. Therefore, the assimilation of surface soil moisture observations has value for updating root zone soil moisture model estimates. In addition, the accuracy of the model estimates improves on both regional and local spatial scales. The increasing availability of remotely sensed soil moisture data will lead to new possibilities for integrating metamodelling and data assimilation in operational water resources management. However, we expect that significant investments in computational capacities are necessary for effective implementation in decision-making.

Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response

Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. The GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column. However, root-zone soil moisture cannot be separated from GRACE-observed total water storage anomalies without ancillary information on surface water and groundwater changes. In this study, GRACE total water storage anomalies and SMOS near-surface soil moisture observations were jointly assimilated into a hydrological model globally to better estimate the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture estimates through the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For example, the correlation between modelled and in situ measurements of root-zone moisture increased by 0.1 (from 0.48 to 0.58) and 0.12 (from 0.53 to 0.65) on average for grasslands and croplands, respectively. Improved correlations were found between vegetation greenness and soil water storage on both seasonal variability and anomalies over water-limited regions. Joint assimilation results show a more severe deficit in soil water anomalies in eastern Australia, southern India and eastern Brazil over the period of 2010 to 2016 than the open-loop, consistent with the satellite-observed vegetation greenness anomalies. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation conditions.

Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors

Accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for hydro-meteorological and agricultural management. The cosmic-ray neutron probe (CRNP) is an innovative technique for field-scale soil moisture observation. However, it is limited at deeper depths, and requires suitable methods for scaling the CRNP effective depth to represent the root-zone layer (up to 100 cm depth). A merging framework was developed in this study for improving field-scale RZSM with the CRNP via coupling the representative ancillary RZSM information with cosmic-ray soil moisture. By using ancillary RZSM retrieved at the most time stable location, this approach alleviates the problems associated with lack of independent datasets, while maintaining scale representativeness. A linear autoregressive model was adopted to forecast the errors between two input datasets (the cosmic-ray soil moisture and ancillary RZSM) and a reference product, which was computed by spatially weighting the deepest in-situ soil moisture measurements apart from the most time stable location. The variances of estimated errors were then used to compute a suitable weight for each single product by following the original linear combination of forecasts, and subsequently merging the ancillary RZSM and cosmic-ray soil moisture. Performance of the merged RZSM in comparison to input datasets and exponential filter-based RZSM over three different environments was evaluated against the reference RZSM product. The results indicated that differences in vegetation coverages related to total aboveground and belowground biomass accumulation, root water uptake rate and canopy density are the major factors controlling the temporal variation in merged RZSM, and they can be partly interpreted via the framework procedure. Superior performance achieved by the merging framework demonstrated its robustness in improving field-scale RZSM measurement compared to other products. This study also underlines the strong relationship between input data quality and the performance of the selected merging method, with respect to variations of CRNP effective depths. Overall, the merging framework is simple to apply, enables unrestricted-use in different environments, and is flexible to combine further standalone data sources.

Estimating root zone soil moisture using multilayer feedforward neural networks based on Levenberg-Maquardt and scaled conjugate gradient algorithms

This paper presents the use of multilayer feedforward neural networks for root zone soil moisture estimation based on Levenberg-Maquardt (LM) and scaled conjugate gradient (SCG) algorithms. Data driven approaches such as artificial neural networks overcome some limitations of remote sensing, conceptual and semi-analytical models in root zone soil moisture estimation. This creates the need to compare neural networks training algorithms to determine those with best estimation accuracy. Observed volumetric soil moisture at 80 cm depth, rainfall and evaporation data were used to estimate soil moisture at 120 and 180 cm depths, within MATLAB. SCG trained network underestimated and/or overestimated soil moisture as compared to LM trained network. LM trained network has better performance and estimation accuracy. Estimated soil moisture is useful for irrigation scheduling, hydrological modelling, and groundwater recharge estimation in the study area.

Estimating root zone soil moisture using multilayer feedforward neural networks based on Levenberg-Maquardt and scaled conjugate gradient algorithms

Estimating root zone soil moisture using multilayer feedforward neural networks based on Levenberg-Maquardt and scaled conjugate gradient algorithms

Spatial and temporal variability of root-zone soil moisture acquired from hydrologic modeling and AirMOSS P-band radar.

The accurate estimation of grid-scale fluxes of water, energy, and carbon requires consideration of sub-grid spatial variability in root-zone soil moisture (RZSM). The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission represents the first systematic attempt to repeatedly map high-resolution RZSM fields using airborne remote sensing across a range of biomes. Here we compare 3-arc-sec (~100-m) spatial resolution AirMOSS RZSM retrievals from P-band radar acquisitions over 9 separate North American study sites with analogous RZSM estimates generated by the Flux-Penn State Hydrology Model (Flux-PIHM). The two products demonstrate comparable levels of accuracy when evaluated against ground-based soil moisture products and a significant level of temporal cross-correlation. However, relative to the AirMOSS RZSM retrievals, Flux-PIHM RZSM estimates generally demonstrate much lower levels of spatial and temporal variability, and the spatial patterns captured by both products are poorly correlated. Nevertheless, based on a discussion of likely error sources affecting both products, it is argued that the spatial analysis of AirMOSS and Flux-PIHM RZSM fields provide meaningful upper and lower bounds on the potential range of RZSM spatial variability encountered across a range of natural biomes.

Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations

In this study, six satellite-based root zone soil moisture (RZSM) estimates from March 2015 to December 2016 were evaluated both temporally and spatially. The first two were the Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) L4 RZSM products. The other four were obtained through the Soil Water Index (SWI) approach, which embedded surface soil moisture (SSM). The SMOS-Barcelona Expert Center (BEC) L4 SSM product and the apparent thermal inertia (ATI)-derived SSM from the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used as SSM datasets. In the temporal analysis, the RZSM estimates were compared to in situ RZSM from 14 stations of the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS). Regarding the spatial assessment, the resulting RZSM maps of the Iberian Peninsula were compared between them. All RZSM values followed the temporal evolution of the ground-based measurements well, although SMOS and MODIS showed underestimation while SMAP displayed overestimation. The good results obtained from MODIS ATI are notable, notwithstanding they were not estimated through microwave radiometry. A very high agreement was found in terms of spatial patterns for the whole Iberian Peninsula except for the extreme north area, which is dominated by high mountains and dense forests.

Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data

The FAO-56 dual crop coefficient (FAO-2Kc) model has been extensively used at the field scale to estimate the crop water requirements by means of the simulated evapotranspiration (ET) and its two components evaporation (E) and transpiration (T). Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68 mm day-1 compared to 0.84 mm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67 mm) is correctly estimated and is very close to the actual effective irrigation (69.8 mm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally-sparse thermal data.