Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Beck, Hylke E.; Pan, Ming; Miralles, Diego G.; Reichle, Rolf H.; Dorigo, Wouter; Hahn, Sebastian; Sheffield, Justin; Karthikeyan, L.; Balsamo, Gianpaolo; Parinussa, Robert; Dijk, Albert I. J. M. van; Du, Jinyang; Kimball, J. S.; Vergopolan, Noemi; Wood, Eric F.

doi:10.5194/hess-25-17-2021

Cited by 201 publications

(138 citation statements)

References 187 publications

(241 reference statements)

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“…Overall, the merged SM products showed better performances than their source datasets; they also showed the ability to capture large-scale drought events, as well as reasonable spatiotemporal patterns and climatic response characteristics. The ranges of https://doi.org/10.5194/essd-2021-84 (Figures 2 and S5) were broadly within the estimates reported by previous SM evaluations, although making a strict comparison is difficult because of the widely different spatiotemporal coverages and resolutions (Beck et al, 2021;Karthikeyan et al, 2017;Li et al, 2020b;Wang et al, 2021a;Yuan and Quiring, 2017). These results (Figures 2 and S5) demonstrated that the merging procedures (unweighted averaging, OLC, EC) used were effective in creating relatively accurate long-term multi-layer SM data at the global scale.…”

Section: Discussionsupporting

confidence: 79%

“…Such vertical inconsistencies may be related to inconsistencies in the vertical discretization of the soil column (Tables S2-S4), soil properties in each layer, modeling of lateral flow and drainage, or other factors (e.g., Balsamo et al, 2009;Best et al, 2011;Melton et al, 2019). Previous regional or global SM evaluations (e.g., Beck et al, 2021;Karthikeyan et al, 2017;Li et al, 2020b;Wang et al, 2021a;Yuan and Quiring, 2017) rarely focused on https://doi.org/10.5194/essd-2021-84 The temporal continuity of the merged products was only examined visually in this study ( Figure 5). Although the applied concatenation procedure (Sect.…”

Section: Discussionmentioning

confidence: 99%

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Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

Wang¹,

Mao²,

Jin³

et al. 2021

Preprint

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Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

Wang¹,

Mao²,

Jin³

et al. 2021

Preprint

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“…shows the lowest performance of the three reanalyses datasets. Supporting these findings, ERA5-Land participated in a soil moisture intercomparison of 18 products against large number (826) of in situ stations and performed strongly (Beck et al, 2021).…”

mentioning

confidence: 67%

ERA5-Land: A state-of-the-art global reanalysis dataset for land applications

Muñoz‐Sabater¹,

Dutra²,

Agustı́-Panareda³

et al. 2021

Preprint

Self Cite

119

115

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Abstract. Framed within the Copernicus Climate Change Service of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the 5th generation of European ReAnalysis (ERA5), hereafter named as ERA5-Land. Once completed, the period covered will span from 1950 to present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, enabling the characterisation of trends and anomalies. This is achieved through global high resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parametrizations that guarantees the use of the state-of-the-art land surface modeling applied to Numerical Weather Prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed behaviour when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available extends from January 1981 to near present, with 2 to 3 months delay with respect to real-time. The segment prior to 1981 is in production, aiming to a release of the whole dataset in summer 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialise NWP and climate models, and to support diverse applications dealing with water resource, land and environmental management. The full ERA5-Land hourly and monthly averaged dataset presented in this paper are available through the Climate Data Store, https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

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“…ERA5 variables, including soil moisture, are provided in a regular grid of 0.1 × 0.1 • , with an hourly temporal resolution [37]. This database has been validated with in situ soil moisture networks worldwide, including the IP [40]. For this study, soil moisture at 12 am and 12 pm and the three depth layers provided by ERA5 were averaged.…”

Section: Era5-land Soil Moisturementioning

confidence: 99%

Agricultural Drought Trends on the Iberian Peninsula: An Analysis Using Modeled and Reanalysis Soil Moisture Products

et al. 2021

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Drought has a great impact on agriculture and food security, and climate change is increasing its frequency and exacerbating its intensity. Given the enormous interest in studying the recent drought evolution, in this work, agricultural drought trends over the past four decades on the Iberian Peninsula (IP) were analyzed. A trend analysis was performed with soil moisture based on the study of the evolution of anomalies and the Soil Water Deficit Index (SWDI). Two soil moisture databases (Lisflood and ERA5-Land) were used and the analysis was performed at daily and weekly temporal scales. Climate characteristics and soil properties were also considered to detect whether a trend spatial pattern could be identified. The results have shown a clear predominance of negative trends. A marked temporal pattern with negative trends was obtained over a 10-month period that coincided with the growing season of most of the crops on the IP, while a positive trend was observed over 2 months. No differences were found based on the climatic zone or soil characteristics. However, negative trends were observed to decrease as the clay content increased. These results can provide useful information for better water management and agriculture of the IP and other Mediterranean areas.

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Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Cited by 201 publications

References 187 publications

Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

ERA5-Land: A state-of-the-art global reanalysis dataset for land applications

Agricultural Drought Trends on the Iberian Peninsula: An Analysis Using Modeled and Reanalysis Soil Moisture Products

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