To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM. © 2012 Springer-Verlag

Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data: Case study in SMEX03 Little River Region, Georgia, U.S.

Optimal soil moisture estimation may be characterized by intercomparisons among remotely sensed measurements, ground‐based measurements, and land surface models. In this study, we compared soil moisture from Advanced Microwave Scanning Radiometer E (AMSR‐E), ground‐based measurements, and a Soil‐Vegetation‐Atmosphere Transfer (SVAT) model for the Soil Moisture Experiments in 2003 (SMEX03) Little River region, Georgia. The Common Land Model (CLM) reasonably replicated soil moisture patterns in dry down and wetting after rainfall though it had modest wet biases (0.001–0.054 m3/m3) as compared to AMSR‐E and ground data. While the AMSR‐E average soil moisture agreed well with the other data sources, it had extremely low temporal variability, especially during the growing season from May to October. The comparison results showed that highest mean absolute error (MAE) and root mean squared error (RMSE) were 0.054 and 0.059 m3/m3 for short and long periods, respectively. Even if CLM and AMSR‐E had complementary strengths, low MAE (0.018–0.054 m3/m3) and RMSE (0.023–0.059 m3/m3) soil moisture errors for CLM and soil moisture low biases (0.003–0.031 m3/m3) for AMSR‐E, care should be taken prior to employing AMSR‐E retrieved soil moisture products directly for hydrological application due to its failure to replicate temporal variability. AMSR‐E error characteristics identified in this study should be used to guide enhancement of retrieval algorithms and improve satellite observations for hydrological sciences

Influence of irrigation on land hydrological processes over California

In this study, a regional climate model (RCM) is employed to investigate the effect of irrigation on hydrology over California through implementing a “realistic irrigation” scheme. Our results indicate that the RCM with a realistic irrigation scheme commonly practiced in California can capture the soil moisture and evapotranspiration (ET) variation very well in comparison with the available in situ and remote sensing data. The RCM results show significant improvement in comparison with those outputs from the default run and the commonly used runs with fixed soil moisture at field capacity. Furthermore, the model reproduces the observed decreasing trends of the reference ET (i.e., ET0) from the California Irrigation Management Information System (CIMIS). The observed decreasing trend is most likely due to the decreasing trend of downward solar radiation shown by models and CIMIS observations. This issue is fundamental in projecting future irrigation water demand. The deep soil percolation rate changes depending on the irrigation method and irrigation duration. Finally, the model results show that precipitation change due to irrigation in California is relatively small in amount and mainly occurs along the midlatitudes in the western United States

Evaluation of a global soil moisture product from finer spatial resolution sar data and ground measurements at Irish sites

In the framework of the European Space Agency Climate Change Initiative, a global, almost daily, soil moisture (SM) product is being developed from passive and active satellite microwave sensors, at a coarse spatial resolution. This study contributes to its validation by using finer spatial resolution ASAR Wide Swath and in situ soil moisture data taken over three sites in Ireland, from 2007 to 2009. This is the first time a comparison has been carried out between three sets of independent observations from different sensors at very different spatial resolutions for such a long time series. Furthermore, the SM spatial distribution has been investigated at the ASAR scale within each Essential Climate Variable (ECV) pixel, without adopting any particular model or using a densely distributed network of in situ stations. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values in temperate grasslands. Temporal and spatial variability analysis provided high levels of correlation (p < 0.025) and low errors between the three datasets, leading to confidence in the new ECV SM global product, despite limitations in its ability to track the driest and wettest conditions

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Satellite Microwave Remote Sensing of Boreal-Arctic Land Surface State and Meteorology from AMSR-E

High latitude regions are undergoing significant climate-related change and represent an integral component of the Earth’s climate system. Near-surface vapor pressure deficit, soil temperature, and soil moisture are essential state variables for monitoring high latitude climate and estimating the response of terrestrial ecosystems to climate change. Methods are developed and evaluated to retrieve surface soil temperature, daily maximum/minimum air temperature, and land surface wetness information from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite for eight Boreal forest and Arctic tundra biophysical monitoring sites across Alaska and northern Canada. Daily vapor pressure deficit is determined by employing AMSR-E daily maximum/minimum air temperature retrievals. The seasonal pattern of microwave emission and relative accuracy of the estimated land surface state are influenced strongly by landscape properties including the presence of open water, vegetation type and seasonal phenology, snow cover and freeze-thaw transitions. Daily maximum/minimum air temperature is retrieved with RMSEs of 2.88 K and 2.31 K, respectively. Soil temperature is retrieved with RMSE of 3.1 K. Vapor pressure deficit (VPD) is retrieved to within 427.9 Pa using thermal information from AMSR-E. AMSR-E thermal information imparted 27% of the overall error in VPD estimation with the remaining error attributable to underlying algorithm assumptions. Land surface wetness information derived from AMSR-E corresponded with soil moisture observations and simple soil moisture models at locations with tundra, grassland, and mixed -forest/cropland land covers (r = 0.49 to r = 0.76). AMSR-E 6.9 GHz land surface wetness showed little correspondence to soil moisture observation or model estimates at locations with \u3e 20% open water and \u3e 5 m2 m-2 Leaf Area Index, despite efforts to remove the impact of open water and vegetation biomass. Additional information on open water fraction and vegetation phenology derived from AMSR-E 6.9 GHz corresponds well with independent satellite observations from MODIS, Sea-Winds, and JERS-1. The techniques and interpretations of high-latitude terrestrial brightness temperature signatures presented in this investigation will likely prove useful for future passive microwave missions and ecosystem modeling

Evaluation of MERRA Land Surface Estimates in Preparation for the Soil Moisture Active Passive Mission

The authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (Tmax) and minimum (Tmin) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SWrad), and surface soil moisture. The MERRA results were evaluated against in situ measurements, similar global products derived from satellite microwave [the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E)] remote sensing and earlier generation atmospheric analysis [Goddard Earth Observing System version 4 (GEOS-4)] products. Relative to GEOS-4, MERRA is generally warmer (~0.5°C for Tmin and Tmax) and drier (~50 Pa for VPD) for low- and middle-latitude regions (\u3c50°N) associated with reduced cloudiness and increased SWrad. MERRA and AMSR-E temperatures show relatively large differences (\u3e3°C) in mountainous areas, tropical forest, and desert regions. Surface soil moisture estimates from MERRA (0–2-cm depth) and two AMSR-E products (~0–1-cm depth) are moderately correlated (R ~ 0.4) for middle-latitude regions with low to moderate vegetation biomass. The MERRA derived surface soil moisture also corresponds favorably with in situ observations (R = 0.53 ± 0.01, p \u3c 0.001) in the midlatitudes, where its accuracy is directly proportional to the quality of MERRA precipitation. In the high latitudes, MERRA shows inconsistent soil moisture seasonal dynamics relative to in situ observations. The study’s results suggest that satellite microwave remote sensing may contribute to improved reanalysis accuracy where surface meteorological observations are sparse and in cold land regions subject to seasonal freeze–thaw transitions. The upcoming NASA Soil Moisture Active Passive (SMAP) mission is expected to improve MERRA-type reanalysis accuracy by providing accurate global mapping of freeze–thaw state and surface soil moisture with 2–3-day temporal fidelity and enhanced (≤9 km) spatial resolution

Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station

In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses the use of the PERSIANN-CCS<sup>1</sup>database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km<sup>2</sup>). The study focuses on the Valencia Anchor Station (VAS) experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe. <br><br> A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km<sup>2</sup>) requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66%) but the precipitation occurrences are in general retrieved (about 67%). <br><br> To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT) model – ISBA (Interactions between Soil Biosphere Atmosphere) is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN – CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E). We show that the PERSIANN database provides useful information at temporal and spatial scales in the context of soil moisture retrieval. <br><br> <br><br> <sup>1</sup>Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System – <a href="http://chrs.web.uci.edu/persiann"target="_blank">http://chrs.web.uci.edu/persiann</a&gt