A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes

Zhang, Runze; Kim, Seokhyeon; Sharma, Ashish

doi:10.1016/j.rse.2019.01.015

Cited by 91 publications

(65 citation statements)

References 63 publications

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“…5). SMAP satellite soil moisture has an average unbiased root mean square error (ubRMSE) in a range from 0.04-0.055 (m 3 /m 3 ) (Chan et al, 2018;Colliander et al, 2018;Zhang et al, 2019) which is close to SMAP mission's ubRMSE requirement. While SMAP soil moisture has shown a better performance compared to other satellite soil moisture products (Stillman and Zeng, 2018), however, it is also found that SMOS and SMAP soil moisture dry-downs are more rapid than in-situ observations following rainfall events (Rondinelli et al, 2015;Shellito et al, 2016).…”

Section: Discussionmentioning

confidence: 95%

Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

Jadidoleslam

Mantilla

Goska

2019

Journal of Hydrology

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Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495-5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments with drainage areas ranging from 80-1000 km 2 in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture

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Section: Discussionmentioning

confidence: 95%

Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

Jadidoleslam

Mantilla

Goska

2019

Journal of Hydrology

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“…The 9 km SM data can provide a more detailed SM distribution and more adequate SM values than the 36 km SMAP SM data [80][81][82]. Although the SMAP SM products with the unbiased root mean square error (ubRMSE) requirement of the SMAP mission (0.04 cm 3 /cm 3 ) have a satisfactory accuracy, the products are still affected by many complex factors (e.g., topography, vegetation, surface temperature,' and RFI).…”

Section: Rf-based Downscaling Methodsmentioning

confidence: 99%

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

et al. 2019

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A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1-RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1-RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active-passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical-vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions. 2 of 20 salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. However, coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; therefore, many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17][18][19][20][21][22][23].Some downscaling methods are based on active or passive microwave data including downscaling coarse-resolution microwave brightness temperature (TB) data or coarse-resolution SM data based on microwave backscatter data [22,[24][25][26][27][28][29][30], and downscaling low-frequency passive microwave data based on high-frequency passive microwave data [31][32][33]. Active microwave techniques offer higher spatial resolutions than passive microwave techniques. Downscaling methods based on active microwaves usually leverage the linear or near-linear relationship between microwave TB or SM data and microwave backscatter data in a time series. Vegetation and surface roughness are generally considered time-invariant during long-time series [26]. However, active microwaves are greatly affected by vegetation and surface roughness [34]; therefore, their applications in most areas are limited [35]. Another assumption is that invariance of vegetation and surface roughness occurs in adjacent observations (several days); thus, these techniques req...

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“…Active and passive microwave satellite-based remote sensing has been considered an effective instrument for monitoring SM on a global scale because of its unique, strong relationship with the soil dielectric constant [17]. Passive L-band microwave remote sensing with frequent revisiting time is recognized as the most promising tool for mapping the regional and global SM distribution [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…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]. By using ground-based observations from the core validation station, Colliander et al (2018) have evaluated the accuracy of SMAP-enhanced level two descending (06:00 local time) retrievals, which were generated by the Backus-Gilbert interpolation with a spatial resolution of 9 km [24].…”

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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A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes

Cited by 91 publications

References 63 publications

Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

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

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