Remote Sensing of Soil Moisture

ISRN Soil Science ◽

10.1155/2013/424178 ◽

2013 ◽

Vol 2013 ◽

pp. 1-33 ◽

Cited By ~ 48

Author(s):

Venkat Lakshmi

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Microwave Frequency ◽

Important Variable ◽

Catchment Hydrology ◽

Ancillary Data ◽

Surface Hydrology ◽

Satellite Sensors ◽

Table Versus

Soil moisture is an important variable in land surface hydrology as it controls the amount of water that infiltrates into the soil and replenishes the water table versus the amount that contributes to surface runoff and to channel flow. However observations of soil moisture at a point scale are very sparse and observing networks are expensive to maintain. Satellite sensors can observe large areas but the spatial resolution of these is dependent on microwave frequency, antenna dimensions, and height above the earth’s surface. The higher the sensor, the lower the spatial resolution and at low elevations the spacecraft would use more fuel. Higher spatial resolution requires larger diameter antennas that in turn require more fuel to maintain in space. Given these competing issues most passive radiometers have spatial resolutions in 10s of kilometers that are too coarse for catchment hydrology applications. Most local applications require higher-spatial-resolution soil moisture data. Downscaling of the data requires ancillary data and model products, all of which are used here to develop high-spatial-resolution soil moisture for catchment applications in hydrology. In this paper the author will outline and explain the methodology for downscaling passive microwave estimation of soil moisture.

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatiotemporal Scales ◽

Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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Global Downscaling of Remotely-Sensed Soil Moisture using Neural Networks

10.5194/hess-2017-680 ◽

2018 ◽

Cited By ~ 3

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Neural Networks ◽

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatio Temporal

Abstract. Characterizing soil moisture at spatio-temporal scales relevant to land surface processes (i.e. of the order of a kilometer) is necessary in order to quantify its role in regional feedbacks between land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3 days repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite estimates soil moisture at two different spatial scales of 36 km and 9 km since April 2015. In this study, we develop a neural networks-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses mean monthly Normalized Differenced Vegetation Index (NDVI) as an ancillary data to quantify sub-pixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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

Remote Sensing ◽

10.3390/rs12172861 ◽

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.

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HAPEX—MOBILHY: A Hydrologic Atmospheric Experiment the Study of Water Budget and Evaporation Flux at the Climatic Scale

Bulletin of the American Meteorological Society ◽

10.1175/1520-0477-67.2.138 ◽

1986 ◽

Vol 67 (2) ◽

pp. 138-144 ◽

Cited By ~ 7

Author(s):

Jean-Claude André ◽

Jean-Paul Goutorbe ◽

Alain Perrier

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Water Budget ◽

General Circulation ◽

Circulation Model ◽

Surface Energy Budget ◽

Surface Hydrology ◽

Land Surface Water ◽

Evaporation Flux

The HAPEX-MOBILHY program is aimed at studying the hydrological budget and evaporation flux at the scale of a GCM (general circulation model) grid square, i.e., 104 km2. Different surface and subsurface networks will be operated during the year 1986, to measure and monitor soil moisture, surface-energy budget and surface hydrology, as well as atmospheric properties. A two-and-a-half-month special observing period will allow for detailed measurements of atmospheric fluxes and for intensive remote sensing of surface properties using well-instrumented aircraft. The main objective of the program, for which guest investigations are strongly encouraged, is to provide a data base against which parameterization schemes for the land-surface water budget will be tested and developed.

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Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

Hydrology and Earth System Sciences ◽

10.5194/hess-16-2547-2012 ◽

2012 ◽

Vol 16 (8) ◽

pp. 2547-2565 ◽

Cited By ~ 4

Author(s):

G. Tang ◽

P. J. Bartlein

Keyword(s):

Soil Moisture ◽

Surface Water ◽

Surface Runoff ◽

Land Surface ◽

Surface Hydrology ◽

Large Spatial Scale ◽

Land Surface Water ◽

Land Surface Hydrology ◽

Water Balances ◽

Global Vegetation

Abstract. Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM) simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i) modify a DGVM for simulating land surface water balances; (ii) evaluate the modified model in simulating actual evapotranspiration (ET), soil moisture, and surface runoff at regional or watershed scales; and (iii) gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH) model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ) DGVM. To evaluate the model we ran LH using historical (1981–2006) climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981–2006 (R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52). The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15%) with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt enabled LH to better simulate monthly stream flow in winter and early spring for rivers located at mid-to-high latitudes. In addition, LH-modeled monthly soil moisture for the state of Illinois (US) agreed well (R2 = 0.79, p < 0.01) with observed data for the years 1984–2001. Overall, this study justifies both the feasibility of incorporating satellite-based land covers into a DGVM and the reliability of LH to simulate land-surface water balances. To better estimate surface/river runoff at mid-to-high latitudes, we recommended that LPJ-DGVM considers the effects of solar radiation on snowmelt.

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Investigating soil moisture-climate interactions with prescribed soil moisture experiments: an assessment with the Community Earth System Model (version 1.2)

10.5194/gmd-2016-209 ◽

2016 ◽

Author(s):

Mathias Hauser ◽

René Orth ◽

Sonia I. Seneviratne

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Land Surface ◽

Seasonal Cycle ◽

Deep Soil ◽

Surface Hydrology ◽

Climate Interactions ◽

Set Up ◽

Moisture Conditions ◽

The Impact

Abstract. Land surface hydrology is an important control of surface weather and climate. A valuable technique to investigate this link is the prescription of soil moisture in land surface models, which leads to a decoupling of the interaction between the atmosphere and land processes. Diverse approaches to prescribe soil moisture, as well as different prescribed soil moisture conditions can be envisaged. Here, we compare and assess three methodologies to prescribe soil moisture and investigate the impact of two estimates of the climatological seasonal cycle to prescribe soil moisture. This can help to guide the set up of future experiments prescribing soil moisture, as for instance planned within the "Land Surface, Snow and Soil Moisture Model Intercomparison Project" (LS3MIP). Our analysis shows that, though in appearance similar, the different approaches require substantially different long-term moisture inputs and lead to different temperature signals. The smallest influence on temperature and the water balance is found when prescribing the median seasonal cycle of deep soil liquid water, whereas the strongest signal is found when prescribing soil liquid and soil ice using the mean seasonal cycle. These results indicate that induced net water-balance perturbations in experiments investigating soil moisture-climate coupling are important contributors to the climate response, in addition to the intended impact of the decoupling.

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Impact of Scale and Aggregation on the Terrestrial Water Exchange: Integrating Land Surface Models and Rhône Catchment Observations

Journal of Hydrometeorology ◽

10.1175/jhm613.1 ◽

2007 ◽

Vol 8 (5) ◽

pp. 1002-1015 ◽

Cited By ~ 17

Author(s):

Reto Stöckli ◽

Pier Luigi Vidale ◽

Aaron Boone ◽

Christoph Schär

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Climate Model ◽

Good Representation ◽

Subgrid Scale ◽

Land Surface Models ◽

Catchment Scale ◽

Surface Models ◽

Terrestrial Water

Abstract Land surface models (LSMs) used in climate modeling include detailed above-ground biophysics but usually lack a good representation of runoff. Both processes are closely linked through soil moisture. Soil moisture however has a high spatial variability that is unresolved at climate model grid scales. Physically based vertical and horizontal aggregation methods exist to account for this scaling problem. Effects of scaling and aggregation have been evaluated in this study by performing catchment-scale LSM simulations for the Rhône catchment. It is found that evapotranspiration is not sensitive to soil moisture over the Rhône but it largely controls total runoff as a residual of the terrestrial water balance. Runoff magnitude is better simulated when the vertical soil moisture fluxes are resolved at a finer vertical resolution. The use of subgrid-scale topography significantly improves both the timing of runoff on the daily time scale (response to rainfall events) and the magnitude of summer baseflow (from seasonal groundwater recharge). Explicitly accounting for soil moisture as a subgrid-scale process in LSMs allows one to better resolve the seasonal course of the terrestrial water storage and makes runoff insensitive to the used grid scale. However, scale dependency of runoff to above-ground hydrology cannot be ignored: snowmelt runoff from the Alpine part of the Rhône is sensitive to the spatial resolution of the snow scheme, and autumnal runoff from the Mediterranean part of the Rhône is sensitive to the spatial resolution of precipitation.

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Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates

10.5194/egusphere-egu2020-10419 ◽

2020 ◽

Author(s):

Amol Patil ◽

Benjamin Fersch ◽

Harrie-Jan Hendricks-Franssen ◽

Harald Kunstmann

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Spatial Resolution ◽

Land Surface ◽

Root Zone ◽

Cosmic Ray ◽

Soil Parameters ◽

Surface Model ◽

Field Scale ◽

Spatio Temporal

<p>Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.</p>

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Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

10.5194/hess-2020-665 ◽

2021 ◽

Author(s):

Natthachet Tangdamrongsub ◽

Michael F. Jasinski ◽

Peter Shellito

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Water Storage ◽

Land Surface Model ◽

Surface Model ◽

Model Parameters ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

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