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

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.

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

10.36227/techrxiv.14776353.v1 ◽

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.

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Evaluation of Smap Passive Soil Moisture Products Using In-Situ Data from a Dense Observation Network

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8517628 ◽

2018 ◽

Cited By ~ 1

Author(s):

X.M. Zheng ◽

Y. Bai ◽

T. Jiang ◽

W. Zhao ◽

J.W. Yang

Keyword(s):

Soil Moisture ◽

In Situ Data ◽

Observation Network

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.567.705 ◽

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.

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Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

Remote Sensing ◽

10.3390/rs12040650 ◽

2020 ◽

Vol 12 (4) ◽

pp. 650

Author(s):

Pablo Sánchez-Gámez ◽

Carolina Gabarro ◽

Antonio Turiel ◽

Marcos Portabella

Keyword(s):

Soil Moisture ◽

Sea Ice ◽

Brightness Temperature ◽

European Space Agency ◽

Ground Truth ◽

Ice Thickness ◽

Sea Ice Thickness ◽

In Situ Data ◽

L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

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Evaluation of SMAP L2/L3 Passive Soil Moisture Products using in-situ data from a dense observation network over Agricultural Area in Northeast China

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8898042 ◽

2019 ◽

Author(s):

X. M. Zheng ◽

Y. Bai ◽

T. Jiang ◽

X. Zhao ◽

K. Zhao

Keyword(s):

Soil Moisture ◽

Northeast China ◽

Agricultural Area ◽

In Situ Data ◽

Observation Network

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Improving Dryland Crop Simulation by Assimilating Soil Moisture and Vegetation Data

10.5194/egusphere-egu2020-13406 ◽

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

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.

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The International Soil Moisture Network: an open-source data hosting facility in support of hydrological research

10.5194/egusphere-egu2020-18547 ◽

2020 ◽

Author(s):

Irene Himmelbauer ◽

Daniel Aberer ◽

Lukas Schremmer ◽

Ivana Petrakovic ◽

Luca Zappa ◽

...

Keyword(s):

Quality Control ◽

Soil Moisture ◽

Open Source ◽

Measurement Techniques ◽

European Space Agency ◽

Web Portal ◽

Soil Moisture Data ◽

In Situ Data ◽

Spatial Coverage

The International Soil Moisture Network (ISMN, ) is an international cooperation to establish and maintain an open-source global data hosting facility, providing in-situ soil moisture data as well as accompanying soil variables. This database is an essential means for validating and improving global satellite soil moisture products as well as land surface -, climate- , and hydrological models.For hydrological validation, the quality of used in-situ data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collect 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.The ISMN was created to address the above-mentioned issues. 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. Since its establishment in 2009 and with continuous financial support through the European Space Agency (ESA), the ISMN evolved into a widely used in situ data source growing continuously (in terms of data volume and users). Historic measurements starting in 1952 up to near&#8211;real time are available through the ISMN web portal. Currently, the ISMN consists of 60 networks with more than 2500 stations spread all over the globe. With a steadily growing user community more than 3200 registered users strong the value of the ISMN as a well-established and rich source of in situ soil moisture observations is well recognized. In fact, the ISMN is widely used in variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.). Our partner networks range from networks with a handful of stations to networks that are composed of over 400 sites, are supported with half yearly provider reports on statistical data about their network (e.g.: data download statistic, flagging statistic, etc.). About 10&#8217;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 (GROW observatory ) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.In this session , we want to give an overview of the ISMN, its unique features and its support of data provider, who are willing to openly share their data, as well as hydrological researcher in need of freely available datasets.

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