A multi-sensor (SMOS, AMSR-E and ASCAT) satellite-based soil moisture products inter-comparison

Lacava, Teodosio; Brocca, Luca; Faruolo, Mariapia; Matgen, Patrick; Moramarco, Tommaso; Pergola, Nicola; Tramutoli, Valerio

doi:10.1109/igarss.2012.6351348

Cited by 11 publications

(12 citation statements)

References 17 publications

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“…These studies showed that the variation range of the AMSR-E/NASA SM time series is significantly narrower than in-situ measurements and does not reflect the SM change due to rainfall events. The accuracy of this product was even lower for the Tibetan Plateau [11,13,14]. Under dry conditions, the NASA SM was overestimated and underestimated under wet conditions [12,15].…”

Section: Introductionmentioning

confidence: 89%

Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Plateau

Xie

Menenti

2019

Remote Sensing

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The daily AMSR-E/NASA (the Advanced Microwave Scanning Radiometer-Earth Observing System/the National Aeronautics and Space Administration) and JAXA (the Japan Aerospace Exploration Agency) soil moisture (SM) products from 2002 to 2011 at 25 km resolution were developed and distributed by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) and JAXA archives, respectively. This study analyzed and evaluated the temporal changes and accuracy of the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM product. The accuracy of both AMSR-E/NASA and JAXA SM was low, with RMSE (root mean square error) > 0.1 cm3 cm−3 against the in-situ SM measurements, especially the AMSR-E/NASA SM. Compared with the AMSR-E/JAXA SM, the dynamic range of AMSR-E/NASA SM is very narrow in many regions and does not reflect the intra- and inter-annual variability of soil moisture. We evaluated both data products by building a linear relationship between the SM and the Microwave Polarization Difference Index (MPDI) to simplify the AMSR-E/NASA SM retrieval algorithm on the basis of the observed relationship between samples extracted from the MPDI and SM data. We obtained the coefficients of this linear relationship (i.e., A0 and A1) using in-situ measurements of SM and brightness temperature (TB) data simulated with the same radiative transfer model applied to develop the AMSR-E/NASA SM algorithm. Finally, the linear relationships between the SM and MPDI were used to retrieve the SM monthly from AMSR-E TB data, and the estimated SM was validated using the in-situ SM measurements in the Naqu area on the Tibetan Plateau of China. We obtained a steeper slope, i.e., A1 = 8, with the in-situ SM measurements against A1 = 1, when using the NASA SM retrievals. The low A1 value is a measure of the low sensitivity of the NASA SM retrievals to MPDI and its narrow dynamic range. These results were confirmed by analyzing a data set collected in Poland. In the case of the Tibetan Plateau, the higher value A1 = 8 gave more accurate monthly AMSR-E SM retrievals with RMSE = 0.065 cm3 cm−3. The dynamic range of the improved retrievals was more consistent with the in-situ SM measurements than with both the AMSR-E/NASA and JAXA SM products in the Naqu area of the Tibetan Plateau in 2011.

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Section: Introductionmentioning

confidence: 89%

Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Plateau

Xie

Menenti

2019

Remote Sensing

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“…air temperature below 0). This is because SMOS is incapable of retrieving good quality soil moisture measurements under frozen weather (Lacava et al, 2012a;Al-Yaari et al, 2014;Wagner et al, 2014;Zhuo and Han, 2015). It has been further averaged into one catchmentscale dataset by applying the weighted average method.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Zhuo

Dai

Islam

et al. 2016

Hydrological Processes

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Satellite-based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimise the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in-situ measurements. This study attempts to build the first error distribution model with additional higher order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using SMOS (the Soil Moisture and Ocean Salinity) satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling which is more relevant to the intended data use than the in-situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General Extreme Value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage which ignores spatial and temporal correlations, and nonstationarity. Further 2 improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models.

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“…Estudios recientes emplean los sistemas de microondas activos y pasivos, junto con datos de la humedad del suelo medidos en campo, para la estimación de la humedad del suelo. Algunos ejemplos de estos trabajos son: Albergel et al 2012 empleando SMOS) con ASCAT; Lacava et al (2012b), utilizando SMOS, AMSR y ASCAT; Brocca et al (2011b), utilizando los sensores ASCAT y AMSR-E y Santi et al (2018) utilizando SMAP, AMSR2 y SENTINEL-1 con el modelo SWBM-GA (Brocca et al, 2008 o Petropoulus et al, (2009) con aplicaciones de SAR para modelos de transferencia suelo-vegetaciónatmosfera (SVAT).…”

Section: Estimaciones Por Teledetección De La Humedad Del Suelounclassified

“…Frecuentemente, en esos trabajos se producen diferencias sistemáticas entre los datos derivados por detección remota y las observaciones in situ, a pesar de que la dinámica temporal sea muy similar. Para solucionar este problema, se han desarrollado varias técnicas de comparación (matching) de la variabilidad de los datos de satélite con la obtenida in situ como la corrección por regresión lineal (Jackson et al, 2010;Brocca et al, 2011b) y mediante funciones de densidad acumulada (CDF matching) (Reichle y Koster 2004;Drusch et al 2005;Lacava et al 2012aLacava et al , 2012b. En este sentido, los trabajos de Lacava et al, (2010Lacava et al, ( , 2012a tienen particular interés, ya que en ellos se emplean, junto con datos in situ y datos del producto de humedad del suelo SMOS, datos modelados con el SWBM-GA (Brocca et al, 2008).…”

Section: La Misión Smos Para La Estimación De La Humedad Superficial unclassified

“…Son varios los modelos que se han aplicado al estudio de la humedad del suelo, entre ellos destacan el modelo Soil Water Atmosphere Plant model, SWAP Van Dam, 2000) exitosamente utilizado en varios estudios (Baroni et al, 2010;Bhuiyan, 2017;Wanders et al, 2014) y el modelo Soil Water Balance Model Green-Ampt (SWBM-GA) desarrollado por Brocca et al, (2008). Este último modelo se ha aplicado en estudios de la humedad del suelo en diversas zonas de Europa (Brocca et al, 2011a;Brocca et al, 2013a;Brocca et al, 2013d;Lacava et al, 2012a;Lacava et al, 2012b) y en la zona de estudio de esta tesis (González Zamora et al, 2015b;Gumuzzio et al, 2016) con resultados satisfactorios, y ha demostrado ser una herramienta muy útil para la validación tanto de la humedad del suelo procedente de SMOS (Lacava et al, 2012a;Lacava et al, 2012b;González-Zamora et al, 2015b;Gumuzzio et al, 2016), como para la procedente de otros satélites (Brocca et al, 2011b;Santi et al, 2016;Santi et al, 2016;Santi, et al, 2018).…”

Section: Introductionunclassified

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Estudio de la humedad superficial del suelo en la cuenca del Duero mediante la integración multiescala de técnicas basadas en teledetección, modelización y observaciones in situ

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A multi-sensor (SMOS, AMSR-E and ASCAT) satellite-based soil moisture products inter-comparison

Cited by 11 publications

References 17 publications

Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Plateau

Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Plateau

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Estudio de la humedad superficial del suelo en la cuenca del Duero mediante la integración multiescala de técnicas basadas en teledetección, modelización y observaciones in situ

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