Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements

Al-Yaari, Amen; Wigneron, Jean‐Pierre; Dorigo, Wouter; Colliander, Andreas; Pellarin, Thierry; Hahn, Sebastian; Mialon, Arnaud; Richaume, Philippe; Fernandez-Morán, Roberto; Fan, Lei; Kerr, Yann H.; Lannoy, Gabriëlle De

doi:10.1016/j.rse.2019.02.008

Cited by 167 publications

(104 citation statements)

References 65 publications

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“…For a better calibration and understanding the main limitations of satellite soil moisture measurements across multiple environmental conditions, there is an increasing number of studies reporting validation performances across multiple scales (spatial and temporal) of available soil moisture satellite datasets (An et al, 2016;Colliander et al, 2017b;Dorigo et al, 2011b;Minet et al, 2012;Mohanty et al, 2017;Yee et al, 2016). Several reports have shown similar prediction bias (e.g., similar RMSE values) (Al-Yaari et al, 2019;Colliander et al, 2017a) as the results of this study. Thus, we contribute with a modeling framework supported by the direct correlation between topography https://doi.org/10.5194/essd-2019-191 and satellite soil moisture (Mason et al, 2016) that brings positive implications to increase the accuracy of satellite soil moisture measurements.…”

Section: Discussionsupporting

confidence: 77%

“…The ESA-CCI soil moisture product is a synthesis from multiple soil moisture sources and it covers four decades (from the 1978 to 2018) of accurate soil moisture values at the global scale. The ESA-CCI soil moisture product contains uncertainty of measurements and it covers a longer period of time (from 1978 to 2018) compared with other satellite-derived soil moisture products (Al-Yaari et al, 2019), and is suitable for applications in long-term ecological and hydrological studies (Dorigo et al, 2017).…”

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confidence: 99%

“…Across large areas of the world, the ESA-CCI soil moisture product is being validated and calibrated against ground truth (i.e., field) soil moisture measurements (Al-Yaari et al, 2019;Dorigo et al, 2011a). Previous work has included a quality control framework to remove potentially wrong measurements from the ESA-CCI soil moisture product, with the ultimate goal of improving its spatial representativeness and reliability (Gruber et al, 2017).…”

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confidence: 99%

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Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Guevara

Taufer

Vargas

2019

Preprint

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Abstract. Soil moisture is key for quantifying soil-atmosphere interactions and the ESA-CCI (European Space Agency Climate Change Initiative) provides historical (> 30 years) satellite soil moisture gridded data at the global scale. We evaluate an alternative approach to increase the spatial resolution of the original ESA-CCI soil moisture measurements from 27 km to 15 km grids by coupling machine learning (ML) algorithms with information from digital terrain analysis at the global scale. We modeled mean annual ESA-CCI soil moisture values across 26 years of available data (1991–2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n = 13376) for evaluating soil moisture predictions. We provide gap-free mean annual soil moisture predictions, which increase by nearly 50 % the spatial resolution of ESA-CCI soil moisture product. Our predictions showed a statistical accuracy varying 0.69–0.87 % and 0.04 m3/m3 of cross-validated explained variance and root mean squared error (RMSE). We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. We found a negative bias (−0.01 to −0.08 m3/m3) between the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years. A temporal analysis, using a robust trend detection strategy (i.e., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded datasets and field measurements of soil moisture varying from −0.7[−0.77, −0.62] % in the ESA-CCI product, −0.9[−1.01, −0.8] % in the downscaled predictions, and −1.6 [−1.7, −1.5] % in the ISMN. The soil moisture predictions provided here (http://www.hydroshare.org/resource/b940b704429244a99f902ff7cb30a31f) could be useful for quantifying soil moisture spatial and temporal dynamics across areas with low availability of soil moisture information in the original ESA-CCI database.

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Section: Discussionsupporting

confidence: 77%

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confidence: 99%

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confidence: 99%

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Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Guevara

Taufer

Vargas

2019

Preprint

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“…Given that the number of available ground-measurements is not sufficient to separate them between training and validation datasets, 10-fold cross-validation methods [40][41][42] were used to evaluate the upscaled SM by comparing them against the in situ measurements from the sixteen WSN sites. In addition, the upscaled SM estimates were also evaluated by comparing them against the SM data measured by two AMSs.…”

Section: Validationmentioning

confidence: 99%

Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures

et al. 2019

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Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution.The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy-the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m 3 /m 3 , respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m 3 /m 3 ). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.

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“…However, it should be noted that SM retrieval algorithms are rapidly developing, and work on their improvement is still being carried out using new concepts or implementing new calibration parameters [20,29]. Therefore, some newly developed SM products have not been fully assessed and inter-compared.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements

Bai

2019

Remote Sensing

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A comprehensive evaluation of the performance of satellite-based soil moisture (SM) retrievals is undoubtedly very important to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). Since the release of the SMAP (Soil Moisture Active Passive) mission data in April 2015, the associated SM retrieval algorithms have developed rapidly, and their improvement work is still in progress. However, some newly developed SM retrievals have not been fully assessed and inter-compared. One such product is the new multi-temporal dual-channel retrieval algorithm (MT-DCA) SM retrievals, which was recently retrieved using the so-called MT-DCA algorithm. To solve this, we aim to assess the MT-DCA SM retrievals along with the SMAP-enhanced level three SM products (SPL3SMP_E, version 2). More specifically, in this paper we evaluated and inter-compared the two SMAP SM retrievals with the ECMWF (European Centre for Medium-Range Weather Forecasts) modeled SM and ISMN (International Soil Moisture Network) in situ observations by applying four statistical scores: Pearson correlation coefficient (R), root mean square difference (RMSD), bias, and unbiased RMSD (ubRMSD). It was found that both SMAP SM retrievals can better capture the seasonal variations of ECMWF-modeled SM and ground-based measurements according to correlations, and MT-DCA SM was drier than SPL3SMP_E SM by ~0.018 m3/m3 on average on a global scale. With respect to the ISMN ground-based measurements, the performance of SPL3SMP_E SM compared better than the MT-DCA SM. The median ubRMSD of SPL3SMP_E SM and MT-DCA SM with ground measurements computed over all selected ISMN sites were 0.058 m3/m3 and 0.070 m3/m3, respectively.

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Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements

Cited by 167 publications

References 65 publications

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures

Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements

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