Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product

2020 ◽  
Vol 34 (21) ◽  
pp. 4083-4096
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Hamid Moradkhani ◽  
Li Fang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Soil Moisture Data ◽  
Data Product

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.

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.

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 The Sodankylä in-situ soil moisture observation network: an example application to Earth Observation data product evaluation

2015 ◽  
Vol 5 (2) ◽  
pp. 599-629 ◽  
Author(s):  
J. Ikonen ◽  
J. Vehviläinen ◽  
K. Rautiainen ◽  
T. Smolander ◽  
J. Lemmetyinen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Reference Site ◽  
Weighted Average ◽  
Earth Observation ◽  
Research Station ◽  
Soil Moisture Data ◽  
Data Product ◽  
Observation Network ◽  
In Situ Observations

Abstract. Soil moisture is one of the main drivers in water, energy, and carbon cycles. Both latent and sensible heat fluxes, governing the air temperature and humidity boundary layer over land, are affected by variations in soil moisture. During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data needs to be validated by means of in-situ observations in different climatic regions. In that context, a comprehensive, distributed network of in-situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a (CAL-VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties. In this paper we present the Sodankylä CAL-VAL reference site soil moisture observation network. The procedures for choosing the representative sites for individual soil moisture network stations are discussed, as well as the development of a weighted average of top layer (5–10 cm) soil moisture over the study area. Comparisons of top layer soil moisture around the Sodankylä CAL-VAL site between the years 2012 and 2014 using ESA CCI soil moisture data against in-situ network observations were conducted. The comparisons were made against a single CCI data product pixel encapsulating the Sodankylä observation sites. Comparisons have been made against both daily CCI soil moisture estimates and against weekly running average values. Soil moisture comparisons are only conducted during snow free and thawed periods, as the presence of snow and soil frost interfere with Earth Observation (EO) data based soil moisture retrievals. While the overall achieved correlation between the CCI data product and in-situ observations was low (0.479), this was largely the result of a single year of observations (2014) with poor correlation metrics. The best values were achieved in 2012 and 2013 at 0.551 and 0.621. All years exhibit a negative (dry) bias ranging from 0.0346 to 0.046. Averaging CCI soil moisture data from daily to weekly estimates significantly improves both correlation and RMSE, but has little effect on bias. The average correlation between the CCI data product and weighted average in-situ observations improves from 0.479 to 0.637. The improvements in correlation are most pronounced in 2012 and 2013, with an improvement from 0.551 to 0.840 and from 0.621 to 0.813 respectively.

scholarly journals The Sodankylä in situ soil moisture observation network: an example application of ESA CCI soil moisture product evaluation

2016 ◽  
Vol 5 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Jaakko Ikonen ◽  
Juho Vehviläinen ◽  
Kimmo Rautiainen ◽  
Tuomo Smolander ◽  
Juha Lemmetyinen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Reference Site ◽  
Product Evaluation ◽  
Research Station ◽  
Soil Moisture Data ◽  
Data Product ◽  
Observation Network ◽  
Comparison Results ◽  
In Situ Observations

Abstract. During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data need to be validated by means of in situ observations in different climatic regions. In that context, a comprehensive, distributed network of in situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a calibration and validation (CAL–VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties. In this paper we present the Sodankylä CAL–VAL reference site soil moisture observation network, its instrumentation as well as its areal representativeness over the study area and the region in general as a whole. As an example of data utilization, comparisons of spatially weighted average top-layer soil moisture observations between the years 2012 and 2014 against ESA CCI soil moisture data product estimates are presented and discussed. The comparisons were made against a single ESA CCI data product pixel encapsulating most of the Sodankylä CAL–VAL network sites. Comparisons are made with daily averaged and running weekly averaged soil moisture data as well as through application of an exponential soil moisture filter. The overall achieved correlation between the ESA CCI data product and in situ observations varies considerably (from 0.479 to 0.637) depending on the applied comparison perspective. Similarly, depending on the comparison perspective used, inter-annual correlation comparison results exhibit even more pronounced variation, ranging from 0.166 to 0.840.

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