The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission

Panciera, Rocco; Walker, Jeffrey P.; Jackson, Thomas J.; Gray, Douglas A.; Tanase, Mihai A.; Ryu, Dongryeol; Monerris, A.; Yardley, Heath; Rüdiger, Christoph; Wu, Xiaoling; Gao, Ying; Hacker, Jörg

doi:10.1109/tgrs.2013.2241774

Cited by 173 publications

(104 citation statements)

References 42 publications

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“…Both sensors use the L-band to collect soil moisture. These missions have conducted campaigns to assess the remotely-obtained products compared with in situ data in different climates in order to study drought effects [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Some of these networks include: Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), Spain [5,6]; Wales Soil Moisture Network (WSMN), UK [21]; and OzNet in Australia [14,20]. South America is an example of a region that lacks in situ data, mainly due to its large territory where data collection is often unviable.…”

Section: Introductionmentioning

confidence: 99%

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Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

et al. 2018

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Abstract:The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m 3 ·m −3 and 0.04 m 3 ·m −3 , respectively. Those values are near to the expected 0.04 m 3 ·m −3 accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved −50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

et al. 2018

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“…al., 1999) and Soil Moisture Experiments (SMEX) (Jacobs et al, 2004;Sano et al, 2004;Jackson et al, 2008), and Polarimtric L-band Multi-beam Radiometer (PLMR) was used in National Airborne Field Experiment (NAFE) Merlin et al, 2008) and Soil Moisture Active and Passive Experiments (SMAPEx) (Panciera et al, 2014). PSR provide C-and X-band radiometer observations which are sensitive to SM but also to vegetation.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Estimation Based on Probabilistic Inversion over Heterogeneous Vegetated Fields Using Airborne PLMR Brightness Temperature

Wang

2017

Preprint

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The L-band radiometer has demonstrated its strong ability of estimating soil moisture (SM) over vegetated surface. 15However, the present SM products derived from satellite radiometers are hardly directly used in heterogeneous oasis. This may be attributed to the coarse spatial resolution of the satellite-based observations and the defects of the point-estimation algorithms which cannot quantify the uncertainty in SM inversion. This paper presents a SM estimation based on the combination of Bayesian probabilistic inversion with high resolution airborne radiometer observations. The overall objective is to quantify the uncertainty in SM estimation and to provide a desirable SM 20 3) The Bayesian PI improves the simulation performance of forward model via maximum likelihood estimation. 4) The 3P and 2P strategies result in different SM inversion uncertainties. Overall, the present Bayesian PI combining multi-channel L-band observations has obtained a desirable SM estimation and uncertainty quantification, which may offer an insight into the future SM inversion based on passive microwave remote sensing. 30

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“…At larger scales (e.g. regional and global scales), remote sensing techniques can provide surface soil moisture data at fine spatial or temporal resolution (Brocca et al, 2011;Panciera et al, 2014). However, these instruments generally measure soil moisture at relatively shallow layers.…”

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confidence: 99%

Depth scaling of soil moisture content from surface to profile: multistation testing of observation operators

Gao¹,

Brocca²,

Zhao³

et al. 2017

Preprint

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Abstract. The accurate assessment of profile soil moisture for spatial domains is usually difficult due to the associated costs, strong spatial-temporal variability, and nonlinear relationship between surface and profile moisture. Here we attempted to use observation operators built by Cumulative Distribution Frequency (CDF) matching method to directly predict profile soil moisture from surface measurements based on multi-station in situ observations from the Soil and Climate Analysis Network (SCAN). We first analyzed the effects of temporal resolution (hourly, daily and weekly) and data length (half year in nongrowing season, half year in growing season, one year, two years and four years) on the performance of observation operators. The results showed that temporal resolution had a negligible influence on the performance of observation operators. However, data length significantly changed the prediction accuracy of observation operators, and a two-year interval was identified as the optimal data length in building observation operators. A dataset with a two-year duration was therefore used to test the robustness of observation operators in three primary climates (humid continental, humid subtropical and semiarid) of the continental USA, with the popular exponential filter employed as a reference approach. The results indicated that observation operators reliably predicted profile soil moisture for the majority of stations in both calibration and validation periods and performed almost equally well with the exponential filter method. This suggests that observation operators are a feasible statistical tool for depth scaling of soil moisture. The findings here have the potential to be applied in profile soil moisture prediction from surface measurements at a range of environments if the target site has long enough (two years) soil moisture observations even with coarse temporal resolutions. Continuous and accurate measurement of profile soil moisture, however, is difficult because of expensive field Hydrol. Earth Syst. Sci. Discuss., https://doi

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The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission

Cited by 173 publications

References 42 publications

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

Soil Moisture Estimation Based on Probabilistic Inversion over Heterogeneous Vegetated Fields Using Airborne PLMR Brightness Temperature

Depth scaling of soil moisture content from surface to profile: multistation testing of observation operators

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