On the identification of representative in situ soil moisture monitoring stations for the validation of SMAP soil moisture products in Australia

Yee, Mei Sun; Walker, Jeffrey P.; Monerris, A.; Rüdiger, Christoph; Jackson, Thomas J.

doi:10.1016/j.jhydrol.2016.03.060

Cited by 58 publications

(34 citation statements)

References 67 publications

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“…Such complexity will likely result in relatively low agreement between the in situ soil moisture observations (which are dominated by local hydrological processes) and the remotely sensed product that represent the average conditions over a very large area. It should be noted that specific approaches exist for determining whether point scale observations have the capacity to represent large areas [30]. However, it was decided to use all stations that satisfied the described selection criteria, since an evaluation over the widest possible range of climate conditions is required, as the quality of the remotely sensed products is known to vary accordingly [7,9,10,27].…”

Section: In Situ Soil Moisture Observationsmentioning

confidence: 99%

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Parinussa

Schalie

Crow

et al. 2016

Climate

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Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called R value metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the R value technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the R value technique. Results indicate an average increasing R value of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore pr...

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Section: In Situ Soil Moisture Observationsmentioning

confidence: 99%

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Parinussa

Schalie

Crow

et al. 2016

Climate

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“…Several algorithms are available for soil moisture retrievals using passive microwave instruments, including the single-channel algorithm [Jackson, 1993;Jackson et al, 1994Jackson et al, , 1996, the multifrequency-polarization iterative algorithm [Njoku and Li, 1999;Njoku et al, 2003;Koike et al, 2000], the look-up table algorithm [Fujii et al, 2009], the polarization index algorithm [Paloscia et al, 2001], and the Dual-Channel Algorithm based on the Qp model (QDCA) [Shi et al, 2005[Shi et al, , 2006. Recently, with these algorithms considerable satellite soil moisture validation work has been conducted in the United States [Collow et al, 2012;Leroux et al, 2013;Ford et al, 2014;Kornelsen et al, 2015;Kim et al, 2015;Chan et al, 2016;Zhang et al, 2017] [Panciera et al, 2009;Draper et al, 2009;Su et al, 2014;Kim et al, 2015;O'Neill et al, 2016;Yee et al, 2016;Cho et al, 2017], and the Tibetan Plateau, China [Su et al, 2011[Su et al, , 2013Liu et al, 2013;Chen et al, 2013;Bi et al, 2016]. However, limited comparative work has been performed in northeast China.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

Cui

Jiang

et al. 2017

JGR Atmospheres

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High‐precision soil moisture products play an important role in estimating forest carbon storage and carbon emissions in Genhe, China. In this paper, we evaluated the Soil Moisture and Ocean Salinity (SMOS) L3 product, the Soil Moisture Active Passive (SMAP) L3 product, and four soil moisture products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR‐2), i.e., the Dual Channel Algorithm based on the Qp model (QDCA) product, the Japan Aerospace Exploration Agency (JAXA) L3 product, and the Land Parameter Retrieval Model (LPRM) C band and X band products in the Genhe area of China. The results indicated that the root‐mean‐square error (RMSE) and bias of the QDCA product were lower than those of the other AMSR‐2 products, although the QDCA still fell outside of the acceptable range with a volumetric error of no greater than 6%. The JAXA product underestimated the soil moisture and had a constant bias of 0.089–0.099 m3 m−3. The LPRM C‐band and X‐band products had a constant variable season bias of 0.261–0.576 m3 m−3. The quality of the SMOS was better than that of the AMSR‐2 products; however, the results were noisy and unstable. The SMAP was closest to the ground measurements and presented a low RMSE (0.039–0.063 m3 m−3) and bias (0.022–0.050 m3 m−3). Finally, an assessment was performed on the parameters in these soil moisture algorithms.

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“…TStab exhibits the largest disagreements with respect to the other methods. In agricultural sites, human decisions (cropping and irrigation) undermine TStab performances because they affect the temporal stability of the spatial distribution of SM (Yee et al, ).…”

Section: Resultsmentioning

confidence: 99%

“…Temporal stability analysis (Vachaud et al, ) selects the station that exhibits the smallest difference, in terms of mean and dispersion (Cosh et al, , ; Kornelsen & Coulibaly, ). It is based on the assumption that spatial SM fields are stable in time, which is not always true (Yee et al, ). Triple collocation (TC) can also be used to estimate the representativeness of ground stations (Chen et al, ; Gruber et al, ; Miralles et al, ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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We conduct a novel comprehensive investigation that seeks to prove the connection between spatial scales and timescales in surface soil moisture (SM) within the satellite footprint (~50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at timescales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per‐timescale basis by comparison to large spatial scale data sets (the in situ spatial average, SMOS, AMSR2, and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), percentage of correlated areas (CArea), and a new proposed approach that uses wavelet‐based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the timescale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero‐crossings, and TC is suitable for week and month scales but not for other scales where data set cross‐correlations are found low. In contrast, WCor and CArea give consistent results at all timescales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (one station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.

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On the identification of representative in situ soil moisture monitoring stations for the validation of SMAP soil moisture products in Australia

Cited by 58 publications

References 67 publications

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

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

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