scholarly journals Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

2021 ◽  
Author(s):  
Navid Jadidoleslam ◽  
Brian K Hornbuckle ◽  
Witold F. Krajewski ◽  
Ricardo Mantilla ◽  
Michael H. Cosh
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Vegetation Indices ◽  
Hydrologic Model ◽  
Corn Belt ◽  
Streamflow Prediction ◽  
Row Crops ◽  
Row Crop ◽  
Crop Agriculture

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

scholarly journals Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

2021 ◽  
Author(s):  
Navid Jadidoleslam ◽  
Brian K Hornbuckle ◽  
Witold F. Krajewski ◽  
Ricardo Mantilla ◽  
Michael H. Cosh
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Vegetation Indices ◽  
Hydrologic Model ◽  
Corn Belt ◽  
Streamflow Prediction ◽  
Row Crops ◽  
Row Crop ◽  
Crop Agriculture

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

scholarly journals Analyzing Effects of Crops on SMAP Satellite-based Soil Moisture using a Rainfall-Runoff Model in the U.S. Corn Belt

2021 ◽  
Author(s):  
Navid Jadidoleslam ◽  
Brian K Hornbuckle ◽  
Witold F. Krajewski ◽  
Ricardo Mantilla ◽  
Michael H. Cosh
Keyword(s):  
Soil Moisture ◽  
Vegetation Indices ◽  
Hydrologic Model ◽  
Corn Belt ◽  
Row Crops ◽  
In Situ Data ◽  
Row Crop ◽  
In Situ Sensors ◽  
Crop Agriculture

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

Effect of Soil Depth on the Quantification of Soil Moisture Content Value Estimated from NOAA Satellite Images

2014 ◽  
Vol 567 ◽  
pp. 705-710
Author(s):  
Abdalhaleem A. Hassaballa ◽  
Abdul Nasir Matori ◽  
Helmi Z.M. Shafri
Keyword(s):  
Soil Moisture ◽  
Critical Parameter ◽  
Satellite Images ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Spatial Estimation ◽  
Optical Images

Soil moisture (MC) is considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes especially over humid areas. However, MC is very critical parameter to measure because of its variability in both space and time. The fluctuation of MC along the soil depth in turn, makes it so difficult to assess from optical satellite techniques. The study aims to produce a rectified satellite’s surface temperature (Ts) in order to enhance the spatial estimation of MC. The study also aims to produce MC estimates from three variable depths of the soil using optical images from NOAA 17 in order to examine the potential of satellite techniques in assessing the MC along the soil depths. The universal triangle (UT) algorithm was used for MC assessment based on Ts, vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths. The study area was divided into three classes according to the nature of surface cover. The resultant MC extracted from the UT method with rectified Ts, produced accuracies of MC ranging from 0.65 to 0.89 when validated with in-situ measured MC at depths 5cm and 10 cm respectively.

Improving Dryland Crop Simulation by Assimilating Soil Moisture and Vegetation Data

2020 ◽  
Author(s):  
Yang Lu ◽  
Justin Sheffield
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Management Practices ◽  
Water Productivity ◽  
Vegetation Indices ◽  
Screening Method ◽  
Model Performance ◽  
State Parameter ◽  
Aquacrop Model

<p>Global population is projected to keep increasing rapidly in the next 3 decades, particularly in dryland regions of the developing world, making it a global imperative to enhance crop production. However, improving current crop production in these regions is hampered by yield gaps due to poor soils, lack of irrigation and other management practices. Here we develop a crop modelling capability to help understand gaps, and apply to dryland regions where data for parametrizing and testing models is generally lacking. We present a data assimilation framework to improve simulation capability by assimilating in-situ soil moisture and vegetation data into the FAO AquaCrop model. AquaCrop is a water-driven model that simulates canopy growth, biomass and crop yield as a function of water productivity. The key strength of AquaCrop lies in the low requirement for input data thanks to its simple structure. A global sensitivity analysis is first performed using the Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method to identify the key influential parameters on the model outputs. We begin with state-only updates by assimilating different combinations of soil moisture and vegetation data (vegetation indices, biomass, etc.), and different filtering/smoothing assimilation strategies are tested. Based on the state-only assimilation results, we further evaluate the utility of joint state-parameter (augmented-states) assimilation in improving the model performance. The framework will eventually be extended to assimilate remote sensing estimates of soil moisture and vegetation data to overcome the lack of in-situ data more generally in dryland regions.</p>

scholarly journals The Capability of Integrating Optical and Microwave Data for Detecting Soil Moisture in an Oasis Region

2020 ◽  
Vol 12 (9) ◽  
pp. 1358 ◽  
Author(s):  
Shuai Huang ◽  
Jianli Ding ◽  
Bohua Liu ◽  
Xiangyu Ge ◽  
Jinjie Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Ratio Model ◽  
Retrieval Model ◽  
Backscattering Coefficient ◽  
Strong Control

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.

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>

From hillslope to catchment scale hydrologic prediction in a semi-arid region with in-situ observations, satellite soil moisture products, and a distributed catchment model

2020 ◽  
Author(s):  
In-Young Yeo ◽  
Ali Binesh ◽  
Garry Willgoose ◽  
Greg Hancock ◽  
Omer Yeteman
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Limited Region ◽  
Semi Arid Region ◽  
Catchment Model ◽  
Semi Arid

<p>The water-limited region frequently experiences extreme climate variability.  This region, however, has relatively little hydrological information to characterize the catchment dynamics and its feedback to the climate system. This study assesses the relative benefits of using remotely sensed soil moisture, in addition to sparsely available in-situ soil moisture and stream flow observations, to improve the hydrologic understanding and prediction.  We propose a multi-variable approach to calibrate a hydrologic model, Soil and Water Assessment Tool (SWAT), a semi-distributed, continuous catchment model, with observed streamflow and in-situ soil moisture.  The satellite<span> soil moisture products (~ 5 cm top soil) from the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) are then used to evaluate the model estimates of soil moisture over the spatial scales through time.  The results show the model calibrated against streamflow only could provide misleading prediction for soil moisture.  Long term in-situ soil moisture observations, albeit limited availability, are crucial to constrain model parameters leading to improved soil moisture prediction at the given site.  </span><span>Satellite soil moisture products </span><span>provide useful information to assess simulated soil moisture results across the spatial domains, filling the gap on the soil moisture information at landscape scales.</span> <span>The preliminary results from this study suggest the potential to produce robust soil moisture and streamflow estimates across scales for a semi-arid region, using a distributed catchment model with in-situ soil network and remotely sensed observations and enhance the overall water budget estimations for multiple hydrologic variables across scales.  </span>This research is conducted on Merriwa catchment, a semi-arid region located in the Upper Hunter Region of NSW, Australia.</p>

scholarly journals Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

2008 ◽  
Vol 8 (2) ◽  
pp. 349-357 ◽  
Author(s):  
J. Schmidt ◽  
G. Turek ◽  
M. P. Clark ◽  
M. Uddstrom ◽  
J. R. Dymond
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Hydrologic Model ◽  
Local Scale ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Infinite Slope Stability Model

Abstract. A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

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