scholarly journals Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

2019 ◽  
Vol 11 (3) ◽  
pp. 368 ◽  
Author(s):  
Zhi Zhang ◽  
Dagang Wang ◽  
Guiling Wang ◽  
Jianxiu Qiu ◽  
Weilin Liao
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Mainland China ◽  
Correction Method ◽  
Cumulative Distribution ◽  
Area Index ◽  
Soil Moisture Data ◽  
Precipitation Product ◽  
Antecedent Precipitation ◽  
Potential Factors

Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time.

scholarly journals Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach

2013 ◽  
Vol 10 (3) ◽  
pp. 3467-3500 ◽  
Author(s):  
J.-G. Liu ◽  
Z.-H. Xie
Keyword(s):  
Soil Moisture ◽  
Seasonal Variations ◽  
Land Surface ◽  
Bayesian Model Averaging ◽  
Mainland China ◽  
Model Averaging ◽  
Meteorological Forcing ◽  
Community Land Model ◽  
Soil Moisture Data ◽  
Ensemble Approach

Abstract. The quality of soil moisture simulation using land surface models depends largely on the accuracy of the meteorological forcing data. The present study investigated how to reduce the uncertainty arising from meteorological forcings in a simulation by adopting a multiple meteorological forcing ensemble approach. Simulations by the Community Land Model version 3.5 (CLM3.5) over mainland China were conducted using four different meteorological forcings, and the four sets of soil moisture data related to the simulations were then merged using simple arithmetical averaging and Bayesian model averaging (BMA) ensemble approaches. Compared to in situ observations, the four simulations captured the spatial and seasonal variations of soil moisture in most cases with some mean bias. They performed differently when simulating the seasonal phases in the annual cycle, the interannual variation and the magnitude of observed soil moisture over different subregions of mainland China, but no individual meteorological forcing performed best for all subregions. The simple arithmetical average ensemble product outperformed most, but not all, individual members over most of the subregions. The BMA ensemble product performed better than simple arithmetical averaging, and performed best for all fields over most of the subregions. The BMA ensemble approach applied to the ensemble simulation reproduced anomalies and seasonal variations in observed soil moisture values, and simulated the mean soil moisture. It is presented here as a promising way for reproducing long-term, high-resolution spatial and temporal soil moisture data.

The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models

2020 ◽  
Author(s):  
Daniel Aberer ◽  
Irene Himmelbauer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter Dorigo ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Measurement Techniques ◽  
European Space Agency ◽  
Web Portal ◽  
Soil Moisture Data ◽  
Spatial Coverage

<p>The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models. </p><p>In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.</p><p>The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.</p><p>Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community > 3200 registered users strong. Time series with hourly timestamps from 1952 – up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (> 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).</p><p>About 10’000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.</p><p>In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.</p>

scholarly journals Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data

2011 ◽  
Vol 15 (3) ◽  
pp. 999-1008 ◽  
Author(s):  
P. Meier ◽  
A. Frömelt ◽  
W. Kinzelbach
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
River Basin ◽  
Lead Time ◽  
Time Lag ◽  
Soil Storage ◽  
Soil Moisture Data ◽  
Modelling Framework ◽  
Zambezi River Basin ◽  
Zambezi River

Abstract. Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.

scholarly journals Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach

2013 ◽  
Vol 17 (9) ◽  
pp. 3355-3369 ◽  
Author(s):  
J.-G. Liu ◽  
Z.-H. Xie
Keyword(s):  
Soil Moisture ◽  
Seasonal Variations ◽  
Land Surface ◽  
Bayesian Model Averaging ◽  
Mainland China ◽  
Weighted Average ◽  
Meteorological Forcing ◽  
Community Land Model ◽  
Soil Moisture Data ◽  
Ensemble Approach

Abstract. The quality of soil-moisture simulation using land surface models depends largely on the accuracy of the meteorological forcing data. We investigated how to reduce the uncertainty arising from meteorological forcings in a simulation by adopting a multiple meteorological forcing ensemble approach. Simulations by the Community Land Model version 3.5 (CLM3.5) over mainland China were conducted using four different meteorological forcings, and the four sets of soil-moisture data related to the simulations were then merged using simple arithmetical averaging and Bayesian model averaging (BMA) ensemble approaches. BMA is a statistical post-processing procedure for producing calibrated and sharp predictive probability density functions (PDFs), which is a weighted average of PDFs centered on the bias-corrected forecasts from a set of individual ensemble members based on their probabilistic likelihood measures. Compared to in situ observations, the four simulations captured the spatial and seasonal variations of soil moisture in most cases with some mean bias. They performed differently when simulating the seasonal phases in the annual cycle, the interannual variation and the magnitude of observed soil moisture over different subregions of mainland China, but no individual meteorological forcing performed best for all subregions. The simple arithmetical average ensemble product outperformed most, but not all, individual members over most of the subregions. The BMA ensemble product performed better than simple arithmetical averaging, and performed best for all fields over most of the subregions. The BMA ensemble approach applied to the ensemble simulation reproduced anomalies and seasonal variations in observed soil-moisture values, and simulated the mean soil moisture. It is presented here as a promising way for reproducing long-term, high-resolution spatial and temporal soil-moisture data.

scholarly journals Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2

2021 ◽  
Vol 13 (23) ◽  
pp. 4729
Author(s):  
Veena Shashikant ◽  
Abdul Rashid Mohamed Shariff ◽  
Aimrun Wayayok ◽  
Md Rowshon Kamal ◽  
Yang Ping Lee ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Oil Palm ◽  
Vegetation Index ◽  
Cloud Model ◽  
Data Retrieval ◽  
Optical Data ◽  
Backscatter Coefficient ◽  
Area Index ◽  
Soil Moisture Data ◽  
Sar Data

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.

Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept

2021 ◽  
Vol 245 ◽  
pp. 106632
Author(s):  
Renkuan Liao ◽  
Shirui Zhang ◽  
Xin Zhang ◽  
Mingfei Wang ◽  
Huarui Wu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Proof Of Concept ◽  
Irrigation Systems ◽  
Soil Moisture Data

scholarly journals Ingestion of Simulated SMAP L3 Soil Moisture Data into Military Maneuver Planning

2015 ◽  
Vol 16 (1) ◽  
pp. 427-440 ◽  
Author(s):  
Susan Frankenstein ◽  
Maria Stevens ◽  
Constance Scott
Keyword(s):  
Soil Moisture ◽  
South Korea ◽  
High Mobility ◽  
Field Measurements ◽  
Soil Strength ◽  
Cumulative Distribution ◽  
Intelligence Agency ◽  
Soil Database ◽  
Soil Moisture Data ◽  
Moisture Sources

Abstract This paper uses simulated SMAP level-3 (L3) soil moisture data to calculate soil strength directly and compares the results against the current Noah Land Information System–based climatology approach. Based on the availability of data, three sites were chosen for the study: Cheorwon, South Korea; Laboue, Lebanon; and Asham, Nigeria. The simulated SMAP satellite data are representative of May conditions. For all three regions, this is best represented by the “average” soil moisture used in the current climatology approach. The cumulative distribution frequency of the two soil moisture sources indicates good agreement at Asham, Nigeria; mixed agreement at Cheorwon, South Korea; and no agreement at Laboue, Lebanon. Soil strengths and resulting vehicle speeds for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097 were calculated based on the Harmonized World Soil Database soil types used by the two soil moisture sources, as well as with a finer-resolution National Geospatial-Intelligence Agency product. Better agreement was found in soil strengths using the finer-resolution soil product. Finally, fairly large differences in soil moisture become muted in the speed calculations even when all factors except soil strength, slope, and vehicle performance are neglected. It is expected that the 0.04 volumetric uncertainty in the final SMAP L3 soil moisture product will have the greatest effect at low vehicle speeds. Field measurements of soil moisture and strength as well as soil type are needed to verify the results.

scholarly journals Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

2013 ◽  
Vol 10 (7) ◽  
pp. 9057-9103 ◽  
Author(s):  
A. L. Barbu ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
S. Lafont
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Cumulative Distribution ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Land Data Assimilation ◽  
The Impact

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from ASCAT backscatter data and the GEOV1 satellite-based LAI into the ISBA-A-gs land surface model within the SURFEX modelling platform of Meteo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the Extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the France domain at a spatial resolution of 8 km. Each model grid box is divided in a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and to distribute the root-zone soil moisture and LAI increments among the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study showing the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA stations shows improved anomaly correlations for eight stations.

scholarly journals Bias Correction in Monthly Records of Satellite Soil Moisture Using Nonuniform CDFs

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Shan Wang ◽  
Huiling Shan ◽  
Chi Zhang ◽  
Yuexing Wang ◽  
Chunxiang Shi
Keyword(s):  
Soil Moisture ◽  
Sampling Method ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Simple Method ◽  
Cumulative Distribution Functions ◽  
Soil Moisture Data ◽  
Curve Approximation ◽  
Systematic Biases ◽  
Temporal And Spatial

It is important to eliminate systematic biases in the field of soil moisture data assimilation. One simple method for bias removal is to match cumulative distribution functions (CDFs) of modeled soil moisture data to satellite soil moisture data. Traditional methods approximate numerical CDFs using 12 or 20 uniformly spaced samples. In this paper, we applied the Douglas–Peucker curve approximation algorithm to approximate the CDFs and found that three nonuniformly spaced samples can achieve the same reduction in standard deviation. Meanwhile, the matching results are always closely related to the temporal and spatial availability of soil moisture observed by automatic soil moisture station (ASM). We also applied the new nonuniformly spaced sampling method to a shorter time series. Instead of processing a whole year of data at once, we divided it into 12 datasets and used three nonuniformly spaced samples to approximate the model data’s CDF for each month. The matching results demonstrate that NU-CDF3 reduced the SD, improved R, and reduced the RMSD in over 70% of the stations, when compared with U-CDF12. Additionally, the SD and RMSD have been reduced by over 4% with R improved by more than 9%.

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