Simulated Surface Soil Moisture Research Articles

Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model

This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts.

The Benefits of Using State‐Of‐The‐Art Digital Soil Properties Maps to Improve the Modeling of Soil Moisture in Land Surface Models

AbstractThis study assesses the added value of using emerging maps of soil properties to improve surface soil moisture simulations using the HydroBlocks land surface model with different soil hydraulic parameterization schemes. Simulations were run at an hourly 30‐m resolution between 2012 and 2019 and evaluated against U.S. Climate Reference Network measurements. The results show that state‐of‐the‐art soil properties maps (POLARIS and SoilGrids250m V2.0) improve the accuracy of simulated surface soil moisture when compared to the STATSGO‐derived CONUS‐SOIL map. Contemporary pedotransfer functions (multi‐linear regression and Artificial Neural Networks‐based) also improve model performance in comparison to the lookup table‐derived soil parameterization schemes. The addition of vertical heterogeneity to the soil properties further improves the mean Kling‐Gupta efficiency by 0.04 and lowers the mean Root mean square error by 0.003 over the CONUS. This study demonstrates that land surface modeling can be improved by using state‐of‐the‐art maps of soil properties, accounting for the vertical heterogeneity of soils, and advancing the use of contemporary pedotransfer functions.

Comparative assessment of surface soil moisture simulations by the coupled wcm-iem vs. data-driven models using the Sentinel 1 and 2 satellite images

Comparative assessment of surface soil moisture simulations by the coupled wcm-iem vs. data-driven models using the Sentinel 1 and 2 satellite images

Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture

The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs.

Bias correction of satellite soil moisture through data assimilation

Soil moisture exhibits great spatio-temporal heterogeneity and plays a critical part in land surface energy and water cycles, being identified as a terrestrial essential climate variable. Thus, it is urgently needed in a wide variety of environmental processes such as hydrology, meteorology, agriculture, and ecology. Microwave remote sensing has the potential to provide near real-time soil moisture estimates on large spatial scales according to the distinctive contrast between dielectric properties of water and dry soils. Thus, many space-borne microwave sensors have been launched for retrieving soil moisture. Especially, SMOS and SMAP at L-band frequency (1.4 GHz) supply an unprecedented opportunity for retrieving surface soil moisture due to their deeper penetration than instruments at other bands. However, these satellite soil moisture products need bias correction before application such as data assimilation. Two common correction methods require reliable land surface soil moisture simulations. However, the quality of these simulations relies heavily on model parameters, such as soil porosity and texture, which are almost unavailable in remote regions such as the Tibetan Plateau. In this study, a dual-cycle assimilation algorithm is taken to make on-line bias correction when assimilating SMAP soil moisture products. During the assimilation, a linear bias correction scheme is regarded as the observation operator to link the simulated soil moisture values and the satellite retrievals. In the inner cycle, a sequentially based assimilation algorithm is run with both model parameters and bias correction coefficients, which are provided by the outer cycle. At the same time, both the analyzed soil moisture and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of both model parameters and correction coefficients through an optimization algorithm. A series of numerical experiments are designed and conducted, indicating that the soil moisture estimates by the presented algorithm are superior to those with the existing bias correction schemes.

Uncertainty Estimation for SMAP Level-1 Brightness Temperature Assimilation at Different Timescales

Soil Moisture Active Passive (SMAP) mission brightness temperature ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{b}$</tex-math></inline-formula> ) observations are assimilated into NASA's Catchment Land Surface Model using an ensemble Kalman filter to update simulations of surface and root-zone soil moisture. Different time-series components of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{b}$</tex-math></inline-formula> observations are assimilated, including anomalies, interannual variations, and high-frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the uncertainties of modeled and observed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{b}$</tex-math></inline-formula> is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. In a benchmark experiment, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{b}$</tex-math></inline-formula> observations are assimilated using a spatially constant 4-K observation uncertainty, as in the operational SMAP Level-4 algorithm. All the DA experiments exhibit notable skill improvements in most regions. Improvements are largest for the interannual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (–) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for surface soil moisture simulations (0.06). No notable difference in skill—neither improvement nor deterioration—is observed between the experiments that use optimized observation uncertainty parameters and the 4-K benchmark experiment. This may be explained by the presence of large observation operator errors, which are analytically shown to have the potential to render postupdate uncertainty insensitive to inaccuracies in estimates of the Kalman gain. These results have important implications for the design of soil moisture DA systems, in particular for parameterizing model and observation uncertainties.

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (∼1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active–Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.

Satellite soil moisture data assimilation for improved operational continental water balance prediction

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is a Kalman-filter-type sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model using tangent linear modelling theory in a post-analysis adjustment after the state updating at each time step. In this study, we assimilate satellite soil moisture retrievals from both Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions simultaneously into the Australian Water Resources Assessment Landscape model (AWRA-L) using the proposed framework and evaluate its impact on the model's accuracy against in situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in situ observation increases from 0.54 (open loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed Enhanced Vegetation Index (EVI) time series across cropland areas results in significant improvements from 0.52 to 0.64 in correlation. The improvements gained from data assimilation can persist for more than 1 week in surface soil moisture estimates and 1 month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters

Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Improving surface soil moisture estimates in West Africa through GRACE data assimilation

Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management.

A Monte Carlo-based multi-objective optimization approach to merge different precipitation estimates for land surface modeling

Precipitation is a fundamental forcing variable in land surface modeling, controlling several hydrological and biogeochemical processes (e.g., runoff, carbon cycling, evaporation, transpiration, groundwater recharge, and soil moisture). However, precipitation estimates from rain gauges, ground-based radars, satellite sensors, and numerical models are affected by significant uncertainties, which can be amplified when exposed to highly non-linear land model physics. This work tests the hypothesis that precipitation data from different sources can be optimally merged to minimize the hydrologic response error in surface soil moisture simulations and maximize their correlation with ground observations (multi-objective optimization problem). This hypothesis is tested by merging three precipitation products (one satellite product, a ground-based dataset, and model-base estimates) that force a land surface model trained to minimize soil moisture anomalies. A Monte Carlo-based algorithm is developed to generate weights to linearly combine these precipitation datasets. Optimal combinations of weights are identified by minimizing the errors and maximizing the correlation between the model simulated soil moisture and the satellite-based SMOS soil moisture product. The proposed methodology has been tested over Oklahoma where high-quality, high-resolution (independent) ground-based soil moisture observations are available for validation purposes. Results show that there exist optimal combinations of these precipitation datasets that provide smaller errors and larger correlation coefficients between modeled soil moisture estimates and ground-based data with respect to forcing the land surface model with single precipitation datasets. Specifically, combining three precipitation products from different sources provides the largest correlation coefficient and the lowest root mean square error at several locations across Oklahoma.

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

Abstract. Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25–75.5∘ N, 11.5∘ W–62.5∘ E, at 0.5∘ resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter–spring high flows and underestimate summer–autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes.

Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM) and the Soil Moisture Operational Products System (SMOPS) observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb) using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM) maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS) using the Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS) SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD) of 0.0303 m3/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb) at an incidence angle of 55° using the Community Microwave Emission Model (CMEM) Radiative transfer Model (RTM). This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA) brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.

Calibration of Noah Soil Hydraulic Property Parameters Using Surface Soil Moisture from SMOS and Basinwide In Situ Observations

AbstractSoil hydraulic properties (SHPs) control infiltration and redistribution of moisture in a soil column. The Noah land surface model (LSM) default simulation selects SHPs according to a location’s mapped soil texture class. SHPs are instead estimated at seven sites in North America through calibration. A single-objective algorithm minimizes the root-mean-square difference (RMSD) between simulated surface soil moisture and observations from 1) a dense network of in situ probes, 2) Soil Moisture Ocean Salinity (SMOS) satellite retrievals, and 3) SMOS retrievals adjusted such that their mean equals that of the in situ network. Parameters are optimized in 2012 and validated in 2013 against the in situ network. RMSD and unbiased RMSD (ubRMSD) assess resulting surface soil moisture behavior. At all sites, assigning SHP parameters from a different soil texture than the one that is mapped decreases the RMSD by an average of 0.029 cm3 cm−3. Similar improvements result from calibrating parameters using in situ network data (0.031 cm3 cm−3). Calibrations using remotely sensed data show comparable success (0.029 cm3 cm−3) if the SMOS product has no bias. Calibrated simulations are superior to texture-based simulations in their ability to decrease ubRMSD at times of year when the default simulation is worst. Changes to both RMSD and ubRMSD are small when the default simulation is already good. Most calibrated simulations have higher runoff ratios than do texture-based simulations, a change that warrants further evaluation. Overall, parameter selection using SMOS data shows good potential where biases are low.

Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations

Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m3/m3 for AMSR-reg and 0.084 m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations.

Evaluation of hydrologic components of community land model 4 and bias identification

Runoff and soil moisture are two key components of the global hydrologic cycle that should be validated at local to global scales in Earth System Models (ESMs) used for climate projection. We have evaluated the runoff and surface soil moisture output by the Community Climate System Model (CCSM) along with 8 other models from the Coupled Model Intercomparison Project (CMIP5) repository using satellite soil moisture observations and stream gauge corrected runoff products. A series of Community Land Model (CLM) runs forced by reanalysis and coupled model outputs was also performed to identify atmospheric drivers of biases and uncertainties in the CCSM. Results indicate that surface soil moisture simulations tend to be positively biased in high latitude areas by most selected CMIP5 models except CCSM, FGOALS, and BCC, which share similar land surface model code. With the exception of GISS, runoff simulations by all selected CMIP5 models were overestimated in mountain ranges and in most of the Arctic region. In general, positive biases in CCSM soil moisture and runoff due to precipitation input error were offset by negative biases induced by temperature input error. Excluding the impact from atmosphere modeling, the global mean of seasonal surface moisture oscillation was out of phase compared to observations in many years during 1985–2004. The CLM also underestimated runoff in the Amazon, central Africa, and south Asia, where soils all have high clay content. We hypothesize that lack of a macropore flow mechanism is partially responsible for this underestimation. However, runoff was overestimated in the areas covered by volcanic ash soils (i.e., Andisols), which might be associated with poor soil porosity representation in CLM. Our results indicate that CCSM predictability of hydrology could be improved by addressing the compensating errors associated with precipitation and temperature and updating the CLM soil representation.

Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts

Abstract. Two new remotely sensed leaf area index (LAI) and surface soil moisture (SSM) satellite-derived products are compared with two sets of simulations of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) land surface models. We analyse the interannual variability over the period 1991–2008. The leaf onset and the length of the vegetation growing period (LGP) are derived from both the satellite-derived LAI and modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI and LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. In order to investigate how recent droughts affected vegetation over the Euro-Mediterranean area, a case study addressing the summer 2003 drought is presented. It shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-A-gs only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite-derived product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of the Ukraine and southern Russia.

SMOS soil moisture data validation in the Aurajoki watershed, Finland

Soil Moisture and Ocean Salinity (SMOS) level 2 soil moisture observations were validated by the soil moisture simulations of the Watershed Simulation and Forecasting System (WSFS) in the Aurajoki watershed located in Southwest Finland. The validation period included summer seasons 2010–2012. A new 2-layer soil moisture model was developed to simulate soil moisture in two layers, 10 cm thick surface layer and 80 cm thick sub-surface layer, due to low penetration depth of the satellite observations. Both layers of the model were divided into six classes of soil types to characterize the soil properties of the study catchment. The soil parameters for different soil types were estimated using the soil moisture observation network and other model parameters were calibrated against discharge observations. The WSFS with the 2-layer soil moisture model was found appropriate in reproducing observed discharges in the test site. Despite slight dry bias, the SMOS observations were consistent with the simulated surface soil moisture content, giving Nash–Sutcliffe efficiency coefficient of 0.77 and root-mean-square deviation of 0.076. The analysis of the SMOS level 2 data presents promising results for the satellite observations to be used as an indicator of the filling of the soil moisture storage in the WSFS.

Evaluation of the visible and shortwave infrared drought index in China

In this article, the performance of the Visible and Shortwave infrared Drought Index (VSDI), a drought index recently developed and validated in Oklahoma, United States, is further explored and validated in China. The in-situ measured soil moisture from 585 weather stations across China are used as ground-truth data, and five commonly used drought indices are compared with VSDI for surface drought monitoring. The results reveal that VSDI is robust and reliable in the estimation of surface dryness-it has the highest correlation with soil moisture among the six indices when computed using both the original and cloud removed data. All six indices show the highest correlation with soil moisture at the 10 cm layer and the averaged 10–50 cm layer. The spatiotemporal patterns of surface moisture indicated by the MODIS-based VSDI are further compared with the precipitation-based drought maps and the Global Land Data Assimilation System (GLDAS) simulated surface soil moisture maps over five provinces located in the Middle-Lower Yangtze Plain of China. The results indicate that despite the difference between the spatial and temporal resolutions of the three products, the VSDI maps still show good agreement with the other two drought products through the rapidly alternating drought and flood events in 2011 in this region. Therefore, VSDI can be used as an effective surface wetness indicator at both the provincial and the national scales in China.