scholarly journals First evaluation of SMOS L2 soil moisture products using in situ observation data of MAVEX on the Mongolian Plateau in 2010 and 2011

2013 ◽  
Vol 7 (2) ◽  
pp. 30-35 ◽  
Author(s):  
Ichirow Kaihotsu ◽  
Keiji Imaoka ◽  
Hideyuki Fujii ◽  
Dambaravjaa Oyunbaatar ◽  
Tsutomu Yamanaka ◽  
...  
Keyword(s):  
Soil Moisture ◽  
In Situ Observation ◽  
Observation Data ◽  
Mongolian Plateau

scholarly journals Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012–2018)

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Ichirow Kaihotsu ◽  
Jun Asanuma ◽  
Kentaro Aida ◽  
Dambaravjaa Oyunbaatar
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Mongolian Plateau ◽  
Moisture Measurement ◽  
Rainfall Condition ◽  
Soil Moisture Measurement

Abstract This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

scholarly journals Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1872 ◽  
Author(s):  
Lu Yi ◽  
Wanchang Zhang ◽  
Xiangyang Li
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Eastern China ◽  
Hydrological Modelling ◽  
Heavy Rainfall Event ◽  
In Situ Observation ◽  
Rainfall Runoff ◽  
Study Case

To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.

scholarly journals Validation of satellite-based soil moisture retrievals from SMAP with in situ observation in the Simineh-Zarrineh (Bokan) Catchment, NW of Iran

2019 ◽  
Vol 8 (4) ◽  
pp. 340-350
Author(s):  
Khaled Haji Maleki ◽  
Ali Reza Vaezi ◽  
Fereydoon Sarmadian ◽  
Wade T. Crow
Keyword(s):  
Soil Moisture ◽  
In Situ Observation

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

2021 ◽  
Vol 25 (8) ◽  
pp. 4567-4584
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Robert C. Pipunic ◽  
Julien Lerat ◽  
Wendy Sharples ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Time Step ◽  
Australian Water

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.

Design of a Cabled Seafloor Observatory for Marine Ecological Environment Monitoring

2021 ◽  
Vol 55 (2) ◽  
pp. 17-24
Author(s):  
Chao Li ◽  
Yan Li ◽  
Rui Zhu ◽  
Yu-ze Song ◽  
Lei Yang
Keyword(s):  
Current System ◽  
Ecological Environment ◽  
Observation System ◽  
In Situ Observation ◽  
Observation Data ◽  
Environment Monitoring ◽  
State Monitoring ◽  
Seafloor Observation ◽  
Shore Station

Abstract Cabled seafloor in-situ observation systems have drawn much attention in recent years for their capability of facilitating long-term all-weather deep-sea data-intense marine observations. The Penglai in-situ seafloor observation system for ecological environment monitoring is proposed in this paper. The current system consists of an on-shore station, a primary node, and two secondary nodes, but more nodes can be hosted due to its scalability. A looped backbone network connects the on-shore station and primary nodes. Each primary node can host up to four secondary nodes, and each secondary node can host up to eight different sensors. Marine observation data and system work state data are collected and backed up by the on-shore station in a real-time manner. Users can access the ocean observation data via a web page interface. The proposed system has been deployed for more than half a year and will continue to work after that. The field experiment showed that the proposed system worked smoothly in system state monitoring and marine data acquisition. A large amount of oceanographic data with videos has been achieved for future studies.

scholarly journals Thermal Inertia Approach Using a Heat Budget Model to Estimate the Spatial Distribution of Surface Soil Moisture over a Semiarid Grassland in Central Mongolia

2018 ◽  
Vol 19 (1) ◽  
pp. 245-265 ◽  
Author(s):  
Dai Matsushima ◽  
Jun Asanuma ◽  
Ichirow Kaihotsu
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Surface Soil ◽  
Heat Budget ◽  
Atmospheric Stability ◽  
Thermal Inertia ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Budget Model

Abstract Thermal inertia is a physical parameter that evaluates soil thermal properties with an emphasis on the stability of the temperature when the soil is affected by heating/cooling. Thermal inertia can be retrieved from a heat budget formulation as a parameter when the time series of Earth surface temperature and forcing variables, such as insolation and air temperature, are given. In this study, a two-layer, linearized heat budget model was employed for the retrieval of thermal inertia over a grassland in a semiarid region. Application of different formulations to the aerodynamic conductance with respect to atmospheric stability significantly improved the accuracy of the thermal inertia retrieval. The retrieved values of thermal inertia were well correlated with in situ surface soil moisture at multiple ground stations. The daily time series of thermal inertia–derived soil moisture qualitatively agreed well with in situ soil moisture after antecedent rainfalls, which was found after fitting the time series to an exponentially decaying function. On the contrary, AMSR2 soil moisture mostly did not agree with in situ soil moisture. The results of the estimation showed high accuracy: the root-mean-square error was 0.038 m3 m−3 compared to the in situ data and was applied to an area of 2° × 2° in which the in situ observation locations were included. The spatiotemporal distribution of surface soil moisture was mapped at a 0.03° × 0.03° spatial resolution in the study area as 10- or 11-day averages over a vegetation growth period of 2012.

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

2020 ◽  
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Robert C. Pipunic ◽  
Julien Lerat ◽  
Wendy Sharples ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Time Step ◽  
Australian Water

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 the 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 in a post-analysis adjustment after the state updating at each time step. In this study, we apply the data assimilation framework to the Australian Water Resources Assessment Landscape model (AWRA-L) 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 vegetation time series across cropland areas results in significant improvements of 0.11 correlation units. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

scholarly journals Comparative Study on Interface Elements, Thin-Layer Elements, and Contact Analysis Methods in the Analysis of High Concrete-Faced Rockfill Dams

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xiao-xiang Qian ◽  
Hui-na Yuan ◽  
Quan-ming Li ◽  
Bing-yin Zhang
Keyword(s):  
Thin Layer ◽  
Contact Stress ◽  
Contact Analysis ◽  
Interface Element ◽  
In Situ Observation ◽  
Observation Data ◽  
Analysis Methods ◽  
Dam Deformation ◽  
Concrete Face

This paper presents a study on the numerical performance of three contact simulation methods, namely, the interface element, thin-layer element, and contact analysis methods, through the analysis of the contact behavior between the concrete face slab and the dam body of a high concrete-faced rockfill dam named Tianshengqiao-I in China. To investigate the accuracy and limitations of each method, the simulation results are compared in terms of the dam deformation, contact stress along the interface, stresses in the concrete face slab, and separation of the concrete face slab from the cushion layer. In particular, the predicted dam deformation and slab separation are compared with the in-situ observation data to classify these methods according to their agreement with the in-situ observations. It is revealed that the interface element and thin-layer element methods have their limitations in predicting contact stress, slab separation, and stresses in the concrete face slab if a large slip occurs. The contact analysis method seems to be the best choice whether the separation is finite or not.

Deep multi-modal satellite and in-situ observation fusion for Soil Moisture retrieval

2021 ◽  
Author(s):  
Grigorios Tsagkatakis ◽  
Mahta Moghaddam ◽  
Panagiotis Tsakalides
Keyword(s):  
Soil Moisture ◽  
In Situ Observation

Numerical Simulation on the Sinking Process of Open Caisson with Particle Flow Code (PFC)

2013 ◽  
Vol 838-841 ◽  
pp. 831-834 ◽  
Author(s):  
Jian Wang ◽  
Li Sha Chai ◽  
Hao Wu
Keyword(s):  
Soil Structure ◽  
Fem Simulation ◽  
Particle Flow ◽  
Particle Flow Code ◽  
Solid Boundary ◽  
In Situ Observation ◽  
Observation Data ◽  
Servo Mechanism ◽  
Soil Flow

Sidewall pressure is important to open caisson design. However, FEM simulation for the sinking process of open caisson is quite difficult due to extremely large deformation including soil flow and solid boundary movement. In order to overcome this problem, Particle Flow Code (PFC), which is based upon discrete element method (DEM), was applied to simulate the whole sinking process of an open caisson using proposed load servo mechanism, and the variations of the sidewall pressure with sinking depth and elevation were investigated. The outcomes agree well with the in-situ observation data and the indoor experiment data, which verifies the feasibility of modeling the sinking process of open caissons using PFC and further studying their soil-structure interaction mechanisms from the microscopic prospect.

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