Filtration and assimilation of soil moisture satellite data

Bogoslovskiy, Nikolay N.; Erin, Sergei I.; Бородина, И. А.; Kizhner, Lubov I.

doi:10.1117/12.2205957

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Overall, these results suggest that data assimilation provides greater benefits when the precipitation forcing is more uncer-tain. Since rain gauge observations are not available over the large majority of the globe (Kidd et al, 2017), we expect data assimilation to provide significant added value at the global scale, as also concluded by Bolten et al (2010), Dong et al (2019), and Tian et al (2019). The lack of improvement for HBV-ERA5+SMAPL3E and HBV-MSWEP+SMAPL3E suggests that the gain parameter G (Eq.…”

Section: How Do the Models With Satellite Data Assimilation Perform?mentioning

confidence: 89%

“…3a). The improvements are probably mainly because the SWI filter reduces the impact of random errors and potential differences between ascending and descending overpasses (Su et al, 2015;Bogoslovskiy et al, 2015). Additionally, since the SWI filter simulates the slower variability of soil moisture at deeper layers (Wagner et al, 1999;Albergel et al, 2008;Brocca et al, 2010a), it improves the consistency between the in situ measurements at 5 cm depth and the microwave signals, which often have a penetration depth of just 1-2 cm depending on the observation frequency and the land surface conditions (Long and Ulaby, 2015; Shellito et al, 2016a;Rondinelli et al, 2015;Lv et al, 2018).…”

Section: Evaluation Approachmentioning

confidence: 99%

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

Beck¹,

Pan²,

Miralles

et al. 2021

Hydrol. Earth Syst. Sci.

201

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Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

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Section: How Do the Models With Satellite Data Assimilation Perform?mentioning

confidence: 89%

Section: Evaluation Approachmentioning

confidence: 99%

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

Beck¹,

Pan²,

Miralles

et al. 2021

Hydrol. Earth Syst. Sci.

201

View full text Add to dashboard Cite

show abstract

“…2a). The improvements are probably mainly because the SWI filter reduces the impact of random errors and potential differences between ascending and descending overpasses (Su et al, 2015;Bogoslovskiy et al, 2015).…”

Section: What Is the Impact Of The Soil Wetness Index (Swi) Smoothingmentioning

confidence: 99%

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

Beck¹,

Pan²,

Miralles³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as open-loop models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo and six estimates from the HBV model with three precipitation inputs (ERA5, IMERG, and MSWEP) and with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5-cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. The median R ± interquartile range across all sites and products in each category was 0.66 ± 0.30 for the satellite products, 0.69 ± 0.25 for the open-loop models, and 0.72 ± 0.22 for the models with satellite data assimilation. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E, SMOS, AMSR2, and ASCAT, with the L-band-based SMAPL3E (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI), MeMo performed better on average (median R of 0.72 versus 0.67), mainly due to the inclusion of SMAPL3E. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

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“…In recent decades, research on soil moisture has gained increasing attention, yielding abundant results in data acquisition and quality improvement, drought and flood monitoring, early warning, crop growth analysis, yield estimation, and the interplay with climate change [13][14][15][16]. Several scholars have elucidated the development of soil moisture research in detail, and most have provided reviews based on a methodological system from the perspective of the multidimensional scientific issues involved in soil moisture [17,18].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Hotspots and Trends in Soil Moisture Research since the 21st Century

Cai,

Yang,

Yue

et al. 2023

Atmosphere

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Soil moisture is a key factor in ecosystems that profoundly affects carbon, nitrogen, and water cycles on land surfaces, vegetation growth, and climate change. Consequently, numerous scholars have researched and authored scientific literature on soil moisture and related topics. Using the Web of Science database, we conducted a bibliometric analysis of 60,581 papers published in the field of soil moisture between 2000 and 2022. The findings revealed the following trends. (1) The number of publications on soil moisture has consistently increased in the 21st century at an increasing rate. For instance, although the annual increase was only 94 publications in 2005, it surged to 321 publications in 2020. (2) The United States (US), China, and developed European countries emerged as primary research institutions and authors. The US occupies a leading position in soil moisture research, boasting the highest number of publications and total citations in the field, whereas China ranks second in both publications and total citations. (3) Regarding international collaboration, the US has established close partnerships with numerous international research institutions. However, China’s international cooperation in this field requires improvement. (4) The Journal of Hydrology holds the top position in terms of both the total number of published articles and citations. Research on water resources ranked first in terms of its H-index. (5) Keyword analysis highlighted several current research hotspots, including the coupled covariance effect of soil moisture and land surface environmental factors in the context of climate change, soil moisture utilization rate, crop yield, influence mechanism of soil moisture on soil ecosystem structure, and development of high-precision soil moisture data products. In conclusion, this study provides a systematic review of the research hotspots and trends in soil moisture studies in the 21st century. The objective is to offer a comprehensive reference to aid in understanding the evolutionary patterns of soil moisture research in multiple dimensions.

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Filtration and assimilation of soil moisture satellite data

Cited by 3 publications

References 4 publications

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

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

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

Analysis of Hotspots and Trends in Soil Moisture Research since the 21st Century

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