Multi‐Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints

Molero, Beatriz; Leroux, Delphine; Richaume, Philippe; Kerr, Yann H.; Merlin, Olivier; Cosh, Mh; Bindlish, Rajat

doi:10.1002/2017jd027478

Cited by 30 publications

(17 citation statements)

References 77 publications

(100 reference statements)

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“…These observations are used to constrain the atmospheric model, land-surface and snow analyses together. Large spatial gaps and representativity issues [50] continue to affect soil moisture and snow observations. In addition, most variables are retrieved in the atmosphere so that the constraint on the land surface is only indirect.…”

Section: Ground-based Networkmentioning

confidence: 99%

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

et al. 2018

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In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

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Section: Ground-based Networkmentioning

confidence: 99%

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

et al. 2018

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“…The generalization of soil moisture time-space dynamics (i.e., dry stays dry and wet stays wet) underlying this result is obviously a simplification of the complex time-space variability in soil moisture fields [see, e.g., Molero et al (2018) for a more complete analysis]. However, it is consistent with the assumptions underlying the application of temporal stability approaches for upscaling point-scale soil moisture (Cosh et al 2006(Cosh et al , 2008, as well as a study that showed a large fraction of soil moisture time-space variability can be explained using a small set of empirical orthogonal functions (Joshi and Mohanty 2010).…”

Section: Discussionmentioning

confidence: 99%

Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites

Chen

Crow

Cosh

et al. 2019

Journal of Hydrometeorology

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Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

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“…One way of achieving this is to compare the in-situ measurements on the ground with the integrated remote sensing data (Rodríguez-Fernández et al, 2017). Even though ground-based measurements are sparse and not necessarily representative of large-scale satellite soil moisture (Molero et al, 2018), such evaluation is important to understand the variations and diversity of conditions between the point measurements and the SMOS/SMAP data.…”

Section: Validation Strategiesmentioning

confidence: 99%

A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

Sadri

Pan

Wada

et al. 2020

Remote Sensing of Environment

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Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015-2018) of SMAP datasets in combination with two approaches-Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process-and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at~36 km resolution for the period from 2010 to 2018. We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04.4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics. Furthermore, we visually compare our global SM-based index maps with those produced by other organizations. Our Global SM Index Monitor (GSMIM) performed, in many tests, similarly to the CCI's product SM index but with the advantage of being a near-real-time tool, which has applications for identifying evolving drought for food security conditions, insurance, policymaking, and crop planning especially for the remote parts of the globe.

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Multi‐Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints

Cited by 30 publications

References 77 publications

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites

A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

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