Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms

Karthikeyan, L.; Pan, Ming; Wanders, Niko; Kumar, Devendra; Wood, Eric F.

doi:10.1016/j.advwatres.2017.09.006

Cited by 132 publications

(94 citation statements)

References 226 publications

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“…For example, ECV products can reflect the changes in soil moisture caused by irrigated activities in irrigation regions such as north China, which is not captured by model simulations (Qiu et al, 2016). Because of substantial improvements in terms of daily coverage, retrieval accuracy, and temporal dynamics over the recent years (Karthikeyan et al, 2017a(Karthikeyan et al, , 2017b, soil moisture retrievals from satellites have been even used to improve large-scale hydrological modeling (López et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Chen

et al. 2019

JGR Atmospheres

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A large population of global soil moisture data sets generated by a variety of models is compared with the latest satellite‐based Essential Climate Variable (ECV) soil moisture product in a common framework. The model‐based surface soil moisture data sets include Global Land Data Assimilation System (GLDAS), reanalysis products, Coupled Model Intercomparison Project Phase 5 Global Climate Models (GCMs), and Inter‐Sectoral Impact Model Intercomparison Project (including observation‐driven outputs ISI‐MIP_OBS and GCM‐driven outputs ISI‐MIP_GCM). We evaluate the model‐based surface soil moisture against ECV with focuses on spatial patterns, temporal correlations, long‐term trends, and relationships with precipitation and Normalized Difference Vegetation Index. The results indicate that all data sets reach a good agreement on the spatial patterns of surface soil moisture, which are also consistent with that of precipitation. However, data sets produced by different techniques have considerable discrepancies in the absolute values of surface soil moisture. Specifically, GCMs tend to underestimate the absolute values of surface soil moisture relative to ECV. In comparisons that remove the influence of absolute values (e.g., unbiased root‐mean‐square error), all model‐based data sets show comparable performances against ECV. GLDAS, reanalysis, and ISI‐MIP_OBS data sets show significant positive temporal correlations with ECV. Model‐based data sets and ECV consistently indicate widespread drying trends during 1980–2005, but the regional trends vary in different data sets. Compared to ECV, GLDAS and reanalysis data sets exhibit more intensive drying trends, while Coupled Model Intercomparison Project Phase 5 and ISI‐MIP_GCM tend to underestimate the drying. In most of the regions, the wetting/drying trends are consistent with the increases/decreases in precipitation and Normalized Difference Vegetation Index.

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Section: Introductionmentioning

confidence: 99%

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Chen

et al. 2019

JGR Atmospheres

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“…In agricultural soil, apart from soil moisture, SAR imaging is also sensitive to several other surface parameters such as roughness, crop cover, and topography [13,14]. Therefore, the soil moisture retrieval model should account for the effects of these target parameters and minimize them from the backscattering coefficient values to get a full response from soil moisture [15].To take account of surface parameters and various sensor configurations in SAR based soil moisture estimation, many backscattering models have been developed over the past few decades. These models are generally classified into three main categories: a theoretical model [16][17][18], semi-empirical [19,20], and empirical models [21,22].…”

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confidence: 99%

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

et al. 2019

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In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

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“…The usage of microwave backscatter measurements for deducing biophysical characteristics of the land surface is a well known and widely accepted approach [1][2][3]. To distinguish contributions that originate from the soil-surface from those originating from the vegetation, a description of the scattering-behaviour of the soil-and vegetation composition within the illuminated area is necessary.…”

Section: Introductionmentioning

confidence: 99%

A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations

et al. 2019

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We present the application of a generic, semi-empirical first-order radiative transfer modelling approach for the retrieval of soil- and vegetation related parameters from coarse-resolution space-borne scatterometer measurements ( σ 0 ). It is shown that both angular- and temporal variabilities of ASCAT σ 0 measurements can be sufficiently represented by modelling the scattering characteristics of the soil-surface and the covering vegetation-layer via linear combinations of idealized distribution-functions. The temporal variations are modelled using only two dynamic variables, the vegetation optical depth ( τ ) and the nadir hemispherical reflectance (N) of the chosen soil-bidirectional reflectance distribution function ( B R D F ). The remaining spatial variabilities of the soil- and vegetation composition are accounted for via temporally constant parameters. The model was applied to series of 158 selected test-sites within France. Parameter estimates are obtained by using ASCAT σ 0 measurements together with auxiliary Leaf Area Index ( L A I ) and soil-moisture ( S M ) datasets provided by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land-surface model within the SURFEX modelling platform for a time-period from 2007–2009. The resulting parametrization was then used used to perform S M and τ retrievals both with and without the incorporation of auxiliary L A I and S M datasets for a subsequent time-period from 2010 to 2012.

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Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms

Cited by 132 publications

References 226 publications

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations

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