Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data

Ma, Can; Li, Xiaoqiong; Long, Wuqiang; Wang, Weizhen

doi:10.3390/rs9040327

Cited by 56 publications

(20 citation statements)

References 39 publications

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“…The thiessen polygon method adopted for SMAP SM validation [41] was used to upscale point-based field SM to pixel-based SM, to reduce the uncertainty of validation caused by the spatial mismatch between ground observations and satellite results. The thiessen polygon method was carried out based on a Matlab code by combining spatial location of SM observing points and the boundary of satellite footprint.…”

Section: E Error Metricsmentioning

confidence: 99%

Performance of Four Passive Microwave Soil Moisture Products in Maize Cultivation Areas of Northeast China

Zheng

Feng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Passive microwave remote sensing is an effective way to obtain global soil moisture (SM) measurements, and many studies have explored the uncertainty inherent in microwave-based SM products. However, SM product accuracy has not been evaluated in northeast China, a national and global production base for commodity grain. In this study, a ground-based wireless sensor network with 28 observation nodes that were spatially distributed within 36 × 36 km was established to achieve satellite-scale "true" SM values through sensor calibration for specific soil types, senor consistency testing, and spatial scale transformation. The uncertainties of four passive microwave SM products (SMAP L3, SMOS L3, the Japan Aerospace Exploration Agency/JAXA AMSR2, and FY3C) were investigated and the following conclusions were obtained: 1) SMAP SM accuracy was very close to the expected application accuracy of 0.04 cm 3 /cm 3 , followed by SMOS, FY3C, and AMSR2; 2) for SMOS and SMAP, there were no significant temporal changes in SM errors, except for the larger error of descending SMOS SM and June SM values for descending SMAP and ascending SMOS. AMSR2 SM generally underestimated field SM, while FY3C SM values under low vegetation conditions were more consistent with field data, with an error of about 0.06 cm 3 /cm 3 ; 3) agricultural activities and rainfall caused the soil surface roughness to increase or decrease within a growing season, which may have been an important source of satellite-scale SM error indicated by high bias values in July for both SMAP and SMOS; and 4) the standard deviation of field SM (0.06 cm 3 /cm 3) produced a SMAP SM error of about 0.06 cm 3 /cm 3 in low vegetation water content conditions,

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Section: E Error Metricsmentioning

confidence: 99%

Performance of Four Passive Microwave Soil Moisture Products in Maize Cultivation Areas of Northeast China

Zheng

Feng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…These in situ networks are located in different climates and environments, which largely compensate for the lack of SM observations in typical polar climates. Based on these in situ measurements, satellite-derived SM-validation studies have been conducted over the QTP and its surrounding areas [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [24] evaluated the SMAP and SMOS SM products by using observations from the Naqu and Pali networks over the QTP, and the results showed that SMAP adequately captured the temporal variations and amplitudes of in situ SM, while SMOS failed to capture temporal variations in SM, especially in an in situ network in a semiarid area of the southern QTP. Ma et al [25] conducted a multiscale validation of SMAP SM products in the Heihe River basin and found that the vegetation effect may have been a major factor that caused slightly unsatisfactory performance, but SMAP maintained consistent spatial-temporal variations with the in situ measurements and typical precipitation events. Recently, Zheng et al [26] evaluated SMAP products in the Maqu and Ngari networks of the QTP and assessed the effect of vegetation opacity and other factors on SM retrieval.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

et al. 2019

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High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

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“…The median value of RMSE decrease from GPM to SMP is more than 20% for LAI ranging from 1 to 2, and this improvement becomes less effective as LAI increases. Although L-band measurements used in SMAP are less sensitive to vegetation than C-band measurements due to the low noise produced with SMAP [66], the SMAP soil moisture is still substantially biased over vegetated areas [67,68]. The lower quality of SMAP soil moisture over vegetated areas may lead to less improvement from GPM estimates to SM corrected precipitation.…”

Section: Discussionmentioning

confidence: 99%

Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

Zhang

Wang

et al. 2019

Remote Sensing

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Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time.

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Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data

Cited by 56 publications

References 39 publications

Performance of Four Passive Microwave Soil Moisture Products in Maize Cultivation Areas of Northeast China

Performance of Four Passive Microwave Soil Moisture Products in Maize Cultivation Areas of Northeast China

Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

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