Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates

Sánchez-Ruíz, Sergio; Piles, María; Sánchez, Nilda; Martínez-Fernández, José; Vall-llossera, M.; Camps, Adriano

doi:10.1016/j.jhydrol.2013.12.047

Cited by 136 publications

(119 citation statements)

References 42 publications

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“…Despite the frequent precipitation events during November, all land cover types had low SM, due to its low intensity. Similar temporal patterns had been noted for the forested area adjacent to the east catchment for the previous hydrological year, when some extreme SM values had been noted during winter 2011 [8]. …”

Section: Temporal Dynamics Of Soil Moisturesupporting

confidence: 80%

Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes

Alexandridis

Cherif

Bilas

et al. 2016

Water

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Despite playing a critical role in the division of precipitation between runoff and infiltration, soil moisture (SM) is difficult to estimate at the catchment scale and at frequent time steps, as is required by many hydrological, erosion and flood simulation models. In this work, an integrated methodology is described to estimate SM at the root zone, based on the remotely-sensed evaporative fraction (Λ) and ancillary information on soil and meteorology. A time series of Terra MODIS satellite images was used to estimate SM maps with an eight-day time step at a 250-m spatial resolution for three diverse catchments in Europe. The study of the resulting SM maps shows that their spatial variability follows the pattern of land cover types and the main geomorphological features of the catchment, and their temporal pattern follows the distribution of rain events, with the exception of irrigated land. Field surveys provided in situ measurements to validate the SM maps' accuracy, which proved to be variable according to site and season. In addition, several factors were analyzed in order to explain the variation in the accuracy, and it was shown that the land cover type, the soil texture class, the temporal difference between the datasets' acquisition and the presence of rain events during the measurements played a significant role, rather than the often referred to scale difference between in situ and satellite observations. Therefore, the proposed methodology can be used operationally to estimate SM maps at the catchment scale, with a 250-m spatial resolution and an eight-day time step.

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Section: Temporal Dynamics Of Soil Moisturesupporting

confidence: 80%

Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes

Alexandridis

Cherif

Bilas

et al. 2016

Water

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“…REMEDHUS is a member of the International Soil Moisture Network (Dorigo et al, 2011, https://ismn.geo.tuwien.ac.at/). A detailed description of this network can be found in previous works (Sánchez-Ruiz et al, 2014).…”

Section: Remedhus Networkmentioning

confidence: 99%

“…To explore the comparisons at an improved spatial matching, a new spatial validation was performed (not shown) using a SMOS-derived product at a 1 km spatial resolution (Piles et al, 2014). Still, poor results were obtained.…”

Section: Spatial Validationmentioning

confidence: 99%

Validation of Smos L2 and L3 Soil Moisture Products Over the Duero Basin at Different Spatial Scales

González-Zamora

Sánchez

Gumuzzio

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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An increasing number of permanent soil moisture measurement networks are nowadays providing the means for validating new remotely sensed soil moisture estimates such as those provided by the ESA’s Soil Moisture and Ocean Salinity (SMOS) mission. Two types of in situ measurement networks can be found: small-scale (100–10000 km2), which provide multiple ground measurements within a single satellite footprint, and large-scale (>10000 km2), which contain a single point observation per satellite footprint. This work presents the results of a comprehensive spatial and temporal validation of a long-term (January, 2010 to June, 2014) dataset of SMOS-derived soil moisture estimates using two in situ networks within the Duero basin (Spain). The first one is the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), which has been extensively applied for validation of soil moisture remote sensing observations, including SMOS. REMEDHUS can be considered within the small-scale network group (1300 km2). The other network started from an existing meteorological network from the Castilla y León region, where soil moisture probes were incorporated in 2012. This network can be considered within the large-scale group (65000 km2). Results from comparison to in situ show that the new reprocessed L2 product (v5.51) improves the accuracy of former soil moisture retrievals, making them suitable for developing new L3 products. Validation based on comparisons between dense/sparse networks showed that temporal patterns on soil moisture are well reproduced, whereas spatial patterns are difficult to depict given the different spatial representativeness of ground and satellite observations.

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“…Choi and Hur [13] x x x x x x Das et al, [11] x x x Fang et al, [14] x x x x x Ines et al, [15] x x x Kim and Hogue [16] x x x x x x Merlin et al, [27] x x x x x x x Merlin et al, [18] x x x x x x x Parinussa et al, [19] x x x Piles et al, [20] x x x x x Sánchez-Ruiz et al, [21] x x x x Shin and Mohanty [22] x x x Song et al, [23] x x x Srivastava et al, [24] x x x x x Srivastava et al, [25] x x x x x Zhao and Li [26] x…”

Section: Reference Rmsd R B S Lr Space Time Spaceandtimementioning

confidence: 99%

“…Yet, to date, little work has focused on the strategy to assess soil moisture downscaling methods. Table 1 lists some recently published disaggregation methods [11,[13][14][15][16][17][18][19][20][21][22][23][24][25][26]. For each method, Table 1 reports the performance metrics that were used to assess the error statistics in downscaled data, whether such results were compared with those obtained at high resolution in the non-disaggregation case and the nature (spatial, temporal and/or spatio-temporal) of the comparison between disaggregated and reference (often in situ) measurements.…”

Section: Introductionmentioning

confidence: 99%

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

et al. 2015

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Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named G DOWN , is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2-3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between G DOWN and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good Remote Sens. 2015, 7 3784 indicator of the overall performance of DISPATCH. Especially, the sign of G DOWN (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The G DOWN approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables.

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Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates

Cited by 136 publications

References 42 publications

Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes

Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes

Validation of Smos L2 and L3 Soil Moisture Products Over the Duero Basin at Different Spatial Scales

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

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