SMAP soil moisture drying more rapid than observed in situ following rainfall events

Shellito, Peter J.; Small, E. E.; Colliander, Andreas; Bindlish, Rajat; Cosh, Michael H.; Berg, Aaron; Bosch, David D.; Caldwell, Todd; Goodrich, David C.; McNairn, Heather; Prueger, John H.; Starks, Patrick J.; Velde, R. van der; Walker, Jeffrey P.

doi:10.1002/2016gl069946

Cited by 107 publications

(106 citation statements)

References 39 publications

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“…Instead we use calculations of changes in surface soil moisture through time, which Shellito et al (2016b) showed to provide a similar depiction of soil drying as the exponential approach. Importantly, by analyzing simple soil drying rates we avoid introducing new uncertainties into our analysis associated with fitting an exponential decay model.…”

Section: P J Shellito Et Al: Controls On Surface Soil Drying Ratesmentioning

confidence: 99%

“…However, continental-scale evaporation from soil is notoriously difficult to measure directly. Lysimeters and chamber measurements provide information over extremely small areas (∼ 10 m 2 or less) (e.g., Herbst et al, 1996;Stannard and Weltz, 2006). Soil drying rates determined from satellite-based observations can provide an estimate of surface evaporation rates on a large scale (McColl et al, 2017a).…”

Section: P J Shellito Et Al: Controls On Surface Soil Drying Ratesmentioning

confidence: 99%

“…Alternative methods of analyzing drydowns, such as the exponential model used in McColl et al (2017a) and Shellito et al (2016b), must cope with the exact same noise in SMAP observations. In those cases, the noise just contributes to misfit in the exponential curve rather than negative evaporation values from individual observations.…”

Section: Calculation Of Drying Ratesmentioning

confidence: 99%

“…Following Shellito et al (2016b), a drydown is defined by a dry period that follows a soil wetting event. We automate this selection process for all pixels according to the following logic: (1) the event precipitation depth must surpass 5 mm in a 24 h period; (2) the dry period must begin after the event precipitation stops and end a day before 2 mm or more additional precipitation accumulates; and (3) the dry period must be at least 3 days long (Fig.…”

Section: Drydown Periodsmentioning

confidence: 99%

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Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito

Small

Livneh

2018

Hydrol. Earth Syst. Sci.

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Abstract. Drydown periods that follow precipitation events provide an opportunity to assess controls on soil evaporation on a continental scale. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates, and our work suggests that SMAP-observed drying is also predominantly affected by direct soil evaporation. Data cover the domain of the North American Land Data Assimilation System Phase 2 and span the first 1.8 years of SMAP's operation.Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

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Section: P J Shellito Et Al: Controls On Surface Soil Drying Ratesmentioning

confidence: 99%

Section: P J Shellito Et Al: Controls On Surface Soil Drying Ratesmentioning

confidence: 99%

Section: Calculation Of Drying Ratesmentioning

confidence: 99%

Section: Drydown Periodsmentioning

confidence: 99%

See 2 more Smart Citations

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Shellito

Small

Livneh

2018

Hydrol. Earth Syst. Sci.

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“…Several processes have been developed by the scientific community to validate soil moisture remote sensing products from satellites, such as the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) [10,12,25], SMAP [26][27][28], and SMOS [2,6,16,17,24,29]. These remotely sensed data have been used to obtain drought indices with monitoring goals [10,12], including SMOS soil moisture data [1,5,8,30].…”

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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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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Assessment of SMAP soil moisture for global simulation of gross primary production

Chen

Liu

et al. 2017

J. Geophys. Res. Biogeosci.

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In this study, high‐quality soil moisture data derived from the Soil Moisture Active Passive (SMAP) satellite measurements are evaluated from a perspective of improving the estimation of the global gross primary production (GPP) using a process‐based ecosystem model, namely, the Boreal Ecosystem Productivity Simulator (BEPS). The SMAP soil moisture data are assimilated into BEPS using an ensemble Kalman filter. The correlation coefficient (r) between simulated GPP from the sunlit leaves and Sun‐induced chlorophyll fluorescence (SIF) measured by Global Ozone Monitoring Experiment‐2 is used as an indicator to evaluate the performance of the GPP simulation. Areas with SMAP data in low quality (i.e., forests), or with SIF in low magnitude (e.g., deserts), or both are excluded from the analysis. With the assimilated SMAP data, the r value is enhanced for Africa, Asia, and North America by 0.016, 0.013, and 0.013, respectively (p < 0.05). Significant improvement in r appears in single‐cropping agricultural land where the irrigation is not considered in the model but well captured by SMAP (e.g., 0.09 in North America, p < 0.05). With the assimilation of SMAP, areas with weak model performances are identified in double or triple cropping cropland (e.g., part of North China Plain) and/or mountainous area (e.g., Spain and Turkey). The correlation coefficient is enhanced by 0.01 in global average for shrub, grass, and cropland. This enhancement is small and insignificant because nonwater‐stressed areas are included.

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SMAP soil moisture drying more rapid than observed in situ following rainfall events

Cited by 107 publications

References 39 publications

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

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

Assessment of SMAP soil moisture for global simulation of gross primary production

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