Validation of SMAP Soil Moisture Products Using Ground-Based Observations for the Paddy Dominated Tropical Region of India

Singh, Gurjeet; Das, Narendra N.; Panda, Rutuparna; Colliander, Andreas; Jackson, Thomas J.; Mohanty, Binayak P.; Entekhabi, Dara; Yueh, Simon

doi:10.1109/tgrs.2019.2921333

Cited by 34 publications

(26 citation statements)

References 27 publications

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“…In tropical forest regions passive products often do not provide soil moisture retrievals, and when they do, the retrievals are typically less reliable than those from active products due to the dense vegetation cover (Al-Yaari et al, 2014;Chen et al, 2018;Miyaoka et al, 2017;Kim et al, 2018). Shedding more light on the strengths and weaknesses of soil moisture products in regions without dense measurement networks -for example using independent soil moisture products (Chen et al, 2018;Dong et al, 2019) or by expanding measurement networks (Kang et al, 2016;Singh et al, 2019) -should be a key priority for future research (Ochsner et al, 2013;Myeni et al, 2019).…”

Section: 9mentioning

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

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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 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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Section: 9mentioning

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

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“…The present research meets this suggestion, by testing SMAP products in Wales, U.K., and highlights a way to calibrate SMAP products and overcoming possible internal algorithmic issues by introducing a regression model. Finally, it must be stated that the work of [57] once more proves the deviations between field data and SMAP products in growing and non-growing seasons, as this research work did too. Another factor that must be taken under consideration in the attempt to justify the differences in correlation between SMAP SM products and in situ measurements and the necessity of this study's proposed model is the specific environment and climate of each test site.…”

Section: Discussionmentioning

confidence: 53%

“…This supports the application of the proposed model in the attempt to overcome the base environmentclimate obstacles. In their recent study, [57] tested SMAP products in India, in an area of extreme seasonal variability fluctuating from very wet to dry soil mainly for the paddy regions, with field measurements. They found that SMAP products worked well during the non-growing season but underperformed in the paddy growing season.…”

Section: Discussionmentioning

confidence: 99%

SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network

et al. 2021

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Soil moisture (SM) is the primary variable regulating the soil temperature (ST) differences between daytime and night-time, providing protection to crop rooting systems against sharp and sudden changes. It also has a number of practical applications in a range of disciplines. This study presents an approach to incorporating the effect of ST for the accurate estimation of SM using Earth Observation (EO) data from NASA’s SMAP sensor, one of the most sophisticated satellites currently in orbit. Linear regression analysis was carried out between the SMAP-retrieved SM and ground-measured SM. Subsequently, SMAP-derived ST was incorporated with SMAP-derived SM in multiple regression analysis to improve the SM retrieval accuracy. The ability of the proposed method to estimate SM under different seasonal conditions for the year 2016 was evaluated using ground observations from the Wales Soil Moisture Network (WSMN), located in Wales, United Kingdom, as a reference. Results showed reduced retrieval accuracy of SM between the SMAP and ground measurements. The R2 between the SMAP SM and ground-observed data from WSMN was found to be 0.247, 0.183, and 0.490 for annual, growing and non-growing seasons, respectively. The values of RMSE between SMAP SM and WSMN observed SM are reported as 0.080 m3m−3, 0.078 m3m−3 and 0.010 m3m−3, with almost zero bias values for annual, growing and non-growing seasons, respectively. Implementation of the proposed scheme resulted in a noticeable improvement in SSM prediction in both R2 (0.558, 0.440 and 0.613) and RMSE (0.045 m3m−3, 0.041 m3m−3 and 0.007 m3m−3), with almost zero bias values for annual, growing and non-growing seasons, respectively. The proposed algorithm retrieval accuracy was closely matched with the SMAP target accuracy 0.04 m3m−3. In overall, use of the new methodology was found to help reducing the SM difference between SMAP and ground-measured SM, using only satellite data. This can provide important assistance in improving cases where the SMAP product can be used in practical and research applications.

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“…The role of seasonality in the performance of SMAP L4 SM product has been already foregrounded by Reichle et al [63]. Climate and background environment played a vital role in satellite SM product performance particularly in the regions with extreme seasonal variability and fluctuating vegetation and surface conditions during growing and nongrowing seasons such as in India [64]. In this investigation, we found better performance of the SMAP SM product in the tropical climate of USA and in the subtropical steppe climate at Australian sites with oceanic influence, than in the humid subtropical climate of India and maritime temperate climate of Europe.…”

Section: Discussionmentioning

confidence: 99%

“…However, for Indian sites (Figures 7 and 8), frequent SM variations were noticed during summer (March-May), monsoon (June-September) and post-monsoon (October-November) than during the winter (December−February) months. In a study by Singh et al [64], they investigated the SMAP product performance in the paddy sown fields. The author also suggested that one of the ways to improve satellite SM retrieval globally could be by assessing the satellite product performance in different parts of the world with varying seasonal and soil conditions.…”

Section: Discussionmentioning

confidence: 99%

Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective

et al. 2020

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Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km × 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0–5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications.

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Validation of SMAP Soil Moisture Products Using Ground-Based Observations for the Paddy Dominated Tropical Region of India

Cited by 34 publications

References 27 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

SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network

Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective

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