Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States

Tavakol, Ameneh; Rahmani, Vahid; Quiring, Steven M.; Kumar, Sujay V.

doi:10.1016/j.rse.2019.05.006

Cited by 52 publications

(28 citation statements)

References 63 publications

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“…Based on the evaluation results shown above in Figures 6-9, it was found that comparing satellite-based SM retrievals to reference SMs (i.e., modeled ECMWF and in situ measurements) under different LAI classes or IGBP land cover types can impact the evaluation scores and the ranking of the two SMAP SM retrievals, due to different vegetation conditions. This is in line with previous evaluation studies on other satellite (e.g., SMOS (Soil Moisture and Ocean Salinity)) SM products [29,42,47] or other SMAP SM products [17,26,28]. On the one hand, considering different levels of LAI values compared to the reference SMs, SPL3SMP_E SM gave the best performance in terms of both correlations and ubRMSD, while on the other hand, MT-DCA SM performed better than SPL3SMP_E SM over "Mixed forests", "Evergreen needleleaf forests", and "Deciduous needleleaf forests" in terms of correlations when considering different IGBP land cover types compared to ECMWF modeled SM.…”

Section: Discussionsupporting

confidence: 92%

“…An assessment of the reliability of SMAP soil moisture (SM) retrievals is undeniably essential to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). A variety of methods or studies, for example, field campaigns, core validation stations, sparse ground networks, land surface model simulations, and inter-comparisons among satellites, have been used or conducted for extensive validation/assessment of SMAP SM retrievals since the relevant products were published in April 2015 [17,[21][22][23][24][25][26][27][28]. Recently, the soil moisture information from the SMAP-enhanced level three and modeled level four, spanning from April 2015 to November 2017, have been assessed against model-based SPoRT-LIS and in situ observations via statistical metrics over the United States [28].…”

Section: Introductionmentioning

confidence: 99%

“…A variety of methods or studies, for example, field campaigns, core validation stations, sparse ground networks, land surface model simulations, and inter-comparisons among satellites, have been used or conducted for extensive validation/assessment of SMAP SM retrievals since the relevant products were published in April 2015 [17,[21][22][23][24][25][26][27][28]. Recently, the soil moisture information from the SMAP-enhanced level three and modeled level four, spanning from April 2015 to November 2017, have been assessed against model-based SPoRT-LIS and in situ observations via statistical metrics over the United States [28]. Zhang et al (2019) have assessed the accuracy of SMAP-enhanced level three soil moisture retrieval from April 2015 to March 2018 using extensive in situ observations from sparse networks covering a wide range of climates and land cover types [17].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bai

2019

Remote Sensing

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“…Pronounced differences in spatiotemporal dynamics and accuracy were found among the products, even among those derived from the same data source. However, most studies evaluated only one specific product or a small subset (≤ 3) of the available products (e.g., Martens et al, 2017;Liu et al, 2019;Zhang et al, 2019;Tavakol et al, 2019). Additionally, many had a regional (subcontinental) focus (e.g., Albergel et al, 2009;Gruhier et al, 2010;Griesfeller et al, 2016), potentially leading to conclusions with limited generalizability.…”

Section: Introductionmentioning

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.

200

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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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“…The spatiotemporal variation in SWC refers to the obvious difference and diversity of soil moisture characteristics in different regions, at different times, locations, and soil layers [6,7]. Traditionally, SWC can be measured by in-situ observations, remote sensing, and laboratory measurements [3,8]. In recent years, the method of satellites [9,10], proximal neutron [11], and gamma radiation stations [12] have been greatly improved, which promoted the development of soil moisture estimation.…”

Section: Introductionmentioning

confidence: 99%

Soil Water Content and Temperature Dynamics under Grassland Degradation: A Multi-Depth Continuous Measurement from the Agricultural Pastoral Ecotone in Northwest China

Yang

Wang

et al. 2019

Sustainability

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The agricultural pastoral ecotone (APE) in Northwest China is an ecological transition zone in the arid area with a very fragile ecosystem. In recent years, the ecosystem has deteriorated sharply, and increasing desertification has made the regional ecosystem more vulnerable and sensitive. In this study, we analyzed (using classical statistical methods) spatial and temporal variations in soil water content (SWC) from 14 September 2016 to 22 April 2019 for high and low vegetation in two grassland sites in Yanchi County, Ningxia. The results showed that the largest average seasonal SWC occurred in autumn. The SWC of the first three layers (0 ÷ 15 cm) of the soil profile responded strongly to precipitation, whereas the SWC in deeper soil (30 ÷ 50 cm) could only be recharged markedly after continuous precipitation. Additionally, the growing process of plants proved to be a cause of variability in soil moisture profiles. Vegetation degradation sped up the course of desertification and decreased soil organic carbon content. These changes left the soil increasingly desiccated and enhanced soil variability. Meanwhile, vegetation degradation also prompted changes in soil temperature and shortened the soil’s frozen time in winter. With the acceleration of global warming, if the process of vegetation degeneration continues and soil temperatures keep rising, the ecosystem is likely to undergo irreversible degradation.

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Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States

Cited by 52 publications

References 63 publications

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

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

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

Soil Water Content and Temperature Dynamics under Grassland Degradation: A Multi-Depth Continuous Measurement from the Agricultural Pastoral Ecotone in Northwest China

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