A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula

Piles, María; Sánchez, Nilda; Vall-llossera, M.; Camps, Adriano; Martínez-Fernández, José; Martínez, Justino; González-Gambau, Verónica

doi:10.1109/jstars.2014.2325398

Cited by 168 publications

(130 citation statements)

References 40 publications

Supporting

Mentioning

118

Contrasting

Unclassified

Order By: Relevance

“…The mean annual rainfall and average temperature of this region are 385 mm and 12 • C, respectively. The REMEDHUS network has a long history of hydrological-related applications, including the validation of satellite soil moisture products [30,35], the evaluation of soil moisture downscaling algorithm [36,37], and parameterization of water balance models [38]. In our study, the precipitation data from the LWW network region we used were the in-situ measurements.…”

Section: Study Area and In-situ Soil Moisture Datamentioning

confidence: 99%

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

et al. 2017

View full text Add to dashboard Cite

Abstract:A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of~0.25 • , and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of~0.1 • . The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m 3 m −3 , whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m 3 m −3 . The JAXA product performed much better at 0.25 • than at 0.1 • , but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m 3 m −3 ). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m 3 m −3 . The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m 3 m −3 for LWW and 0.016 m 3 m −3 for REMEDHUS) and SMOS (−0.004 m 3 m −3 for LWW and −0.012 m 3 m −3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m 3 m −3 ), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the

show abstract

Section: Study Area and In-situ Soil Moisture Datamentioning

confidence: 99%

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Similar triangles are obtained at the afternoon passes and/or using Terra day (not shown). The triangle or trapezoid was also obtained when plotting LST-NDVI space, which is used to indirectly estimate SM from TIR observations [25][26][27][28], or to disagregate SM from the synergy of microwaves and TIR [32,34]. …”

Section: Sm-lst Relationship At the Daily Scalementioning

confidence: 99%

“…TIR retrievals, in turn, provide high resolutions (from tens of meters to several km), but are sensitive to atmospheric effects. To benefit from the advantages of both SM retrieval techniques, there are several SM downscaling or disaggregation algorithms based on the synergy of coarse resolution microwave and high resolution TIR observations [32][33][34]. A recent review describes these relationships and the synergistic use of passive microwave and optical Visible/InfraRed (VIS/IR) for multi-scale SM estimation [35].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations

et al. 2016

Self Cite

View full text Add to dashboard Cite

Soil moisture (SM) is an important component of the Earth's surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth's surface SM using L-band radiometers: ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA's Terra/Aqua MODIS or ESA & EUMETSAT's MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011-2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM-LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ≈ −0.6 to −0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ≈ − 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ≈ −0.5 to −0.7; satellite R ≈ −0.4 to −0.7) indicating SM-LST coupling, than in winter (in situ R ≈ +0.3; satellite R ≈ −0.3) indicating SM-LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ∼0.12 m 3 /m 3 , stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM-LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.

show abstract

“…The satellite operates in two orbits: ascendant and descendant. In the first, the satellite moves in the south-north direction and passes over the equator at 6:00 a.m., whereas the descendant orbit moves in the north-south direction and passes over the equator at 6:00 p.m. [2,18,20]. When a record was completed in both orbits on the same day, the two values are averaged.…”

Section: Smos Datamentioning

confidence: 99%

“…This microwave imaging radiometer using aperture synthesis (MIRAS), used in the present work, operates on the L-band at frequency 1.4 GHz and polarization horizontal-vertical (H-V) [2,6,13,18]. These products are available with a daily time resolution for three-day, nine-day, monthly, and yearly periods [19] and have an average ground resolution of 43 km [13], but can be better depending on the level of processing (40 km for L2, 25 km for L3, and 1 km for L4).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula

Cited by 168 publications

References 40 publications

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations

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

Contact Info

Product

Resources

About