Application of Triple Collocation in Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products

Chen, Fan; Crow, Wade T.; Colliander, Andreas; Cosh, Michael H.; Jackson, Thomas J.; Bindlish, Rajat; Reichle, Rolf H.; Chan, S.; Bosch, David D.; Starks, Patrick J.; Goodrich, David C.; Seyfried, M. S.

doi:10.1109/jstars.2016.2569998

Cited by 132 publications

(133 citation statements)

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“…In addition, compared to the true values of soil moisture at a point scale, the point-based in situ observations include errors [29,31]. To date, some studies have shown that the triple collocation method and other upscaling methods can limit the errors due to the mismatch of the spatial scale and vertical depth, leading to a precise evaluation of the quality of satellite-based soil moisture products [44,46,47]. Therefore, we should be cautious regarding the interpretation of the comparison between the SMAP product and in situ observations.…”

Section: Discussionmentioning

confidence: 99%

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

Sun

Huang

et al. 2017

Remote Sensing

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Abstract:The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely used in scientific research and applications. This study represents the first evaluation of the SMAP radiometer soil moisture product over China. In this paper, a preliminary evaluation was performed using sparse in situ measurements from 655 China Meteorological Administration (CMA) monitoring stations between 1 April 2015 and 31 August 2016. The SMAP radiometer-derived soil moisture product was evaluated against two schemes of original soil moisture and the soil moisture anomaly in different geographical zones and land cover types. Four performance metrics, i.e., bias, root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the correlation coefficient (R), were used in the accuracy evaluation. The results indicated that the SMAP radiometer-derived soil moisture product agreed relatively well with the in situ measurements, with ubRMSE values of 0.058 cm 3 ·cm −3 and 0.039 cm 3 ·cm −3 based on original data and anomaly data, respectively. The values of the SMAP radiometer-based soil moisture product were overestimated in wet areas, especially in the Southwest China, South China, Southeast China, East China, and Central China zones. The accuracies over croplands and in Northeast China were the worst. Soil moisture, surface roughness, and vegetation are crucial factors contributing to the error in the soil moisture product. Moreover, radio frequency interference contributes to the overestimation over the northern portion of the East China zone. This study provides guidelines for the application of the SMAP-derived soil moisture product in China and acts as a reference for improving the retrieval algorithm.

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Section: Discussionmentioning

confidence: 99%

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

Sun

Huang

et al. 2017

Remote Sensing

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“…Sparse network measurements can be used as additional resources to expand the spatial and temporal scopes of the validation [12,23]. In this study, the sparse in situ soil moisture network was established in June 2013 by authors' team over the upper reach of the Heihe River Watershed.…”

Section: Sparse In Situ Networkmentioning

confidence: 99%

“…However, so far, only a few validations of the SMAP products have been conducted, and almost all of them have been carried out at the core validation sites (CVS) [12,[16][17][18][19][20][21][22]. Because observations from the CVS have been used to refine and validate retrieval algorithms, it is important to validate the SMAP products over other regions beyond the CVS [12,14,21,23].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Zhang

2017

Remote Sensing

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Abstract:As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperature T Bp and effective temperature T eff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m 3 /m 3 in mountainous area. These uncertainties also result in the "dry bias" of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m 3 /m 3 and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient b p result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature T Bp and effective temperature T eff ; determine coefficient b p and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future.

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“…For a random perturbation of inputs, it is important to make a correct attribution of error sources, and identify error ranges. This could be sometimes helped with the use of the uncertainty quantification analysis [65,66]. In this review, we focus on this approach.…”

Section: The Concept and Type Of Retrieval Ensemblesmentioning

confidence: 99%

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

2017

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Abstract:To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a conceptual difference between error and uncertainty in basic metrological terms of the International Organization for Standardization (ISO), and briefly summarize how current retrieval algorithms deal with a challenge of land surface heterogeneity. As compared to relative approaches such as Triple Collocation, or cumulative distribution function (CDF) matching that aim for climatology stationary errors at time-scale of years, we address a stochastic approach for reducing instantaneous retrieval errors at time-scale of several hours to days. The stochastic approach has a potential as a global scheme to resolve systematic errors introducing from instrumental measurements, geo-physical parameters, and surface heterogeneity across the globe, because it does not rely on the ground measurements or reference data to be compared with.

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Application of Triple Collocation in Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products

Cited by 132 publications

References 31 publications

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

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