SMOS Level-2 Soil Moisture Product Evaluation in Rain-Fed Croplands of the Pampean Region of Argentina

Niclòs, Raquel; Rivas, Raúl Eduardo; García-Santos, Vicente; Doña, Carolina; Valor, E.; Holzman, Mauro; Bayala, Martín Ignacio; Carmona, Facundo; Ocampo, Dora; Soldano, Alvaro; Thibeault, M.; Caselles, Vicente; Sánchez, Juan Manuel

doi:10.1109/tgrs.2015.2460332

Cited by 15 publications

(10 citation statements)

References 25 publications

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“…SMOS soil moisture products show an underestimation of soil moisture with respect to both SMAP retrievals and SASMAS in-situ measurements. This observation is in agreement with several studies showing a general underestimation of SMOS soil moisture retrievals (Niclòs et al, 2016).…”

Section: Discussionsupporting

confidence: 93%

Downscaling SMAP and SMOS soil moisture retrievals over the Goulburn River Catchment, Australia

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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Soil moisture is an important variable in a number of environmental processesspecifically the hydrological cycle, in the water-limited environments. Therefore, soil moisture data is important as an input variable in hydrologic, climatic modelling and agricultural applications. Many of these applications require high-resolution soil moisture data. However, most of the available soil moisture measurements are rarely available at high resolution, therefore unable to capture the spatial heterogeneity of soil moisture with required accuracy levels. Thus, upscaling or downscaling of soil moisture observations to higher spatial resolution is an essential requirement for these multidisciplinary applications. A long-term high-resolution soil moisture dataset is useful for planning and decision making in agriculture, climatology and hydrology. Developing a historic soil moisture dataset at high spatial resolution over a long period requires the use of different satellite soil moisture products. However, the use of different satellite products results in incompatibilities among each other due to discrepancies in overpass times, the wavelengths used, retrieval algorithms, orbital parameters and sensor errors. Therefore, validation and comparison of soil moisture retrievals from different satellite sensors and their downscaled products is important in evaluating the consistency of a long-term time series dataset of high-resolution soil moisture. This study focusses on a downscaling algorithm based on the thermal inertia theory at two sub-catchments of the Goulburn River in southeastern Australia, Krui and Merriwa River catchments. The goal is to downscale the radiometric soil moisture retrievals of Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions along with validation using established in-situ observation networks. A linear regression model was developed between the daily surface temperature difference and daily mean soil moisture values from the in-situ observations of the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) project. This relationship is modulated by the vegetation cover and soil attributes. The MODerateresolution Imaging Spectroradiometer (MODIS) derived land surface temperature difference values were fitted into the lookup algorithms to estimate surface soil moisture at fine spatial resolution at 1 km. The coarseresolution SMAP (36 km) and SMOS (25 km) radiometric soil moisture products were downscaled to 1 km. The coarse-resolution SMAP and SMOS soil moisture datasets were compared with each other, and then against the SASMAS in-situ measurements. SMAP 36 km datasets show a reasonable agreement with the insitu data with RMSEs of 0.07 and 0.05 cm 3 /cm 3 over two SMAP pixels. However, SMOS 25 km soil moisture products show a general underestimation as compared to SMAP and SASMAS datasets. Therefore, the SMOS data were calibrated with SMAP data. Subsequently, the SMAP, SMOS and adjusted SMOS datasets over the Krui and Merriwa River catchments for the year...

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Section: Discussionsupporting

confidence: 93%

Downscaling SMAP and SMOS soil moisture retrievals over the Goulburn River Catchment, Australia

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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“…Fig. 2 includes the four described samples, but other samples showed similar results (please see also [6] ). The lowest coefficient of determination value was observed in sample 3.…”

Section: Methodsmentioning

confidence: 54%

“…However, efforts should be done to optimize field techniques to ensure high quality data. In [6] a campaign for a SMOS soil moisture product validation in Pampean Region of Argentina was carried out. The field/laboratory techniques were briefly described and the more detailed process included in this work should be useful for different studies to understand soil-vegetation-atmosphere processes.…”

Section: Methods Detailsmentioning

confidence: 99%

A method for soil moisture probes calibration and validation of satellite estimates

et al. 2017

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“…Remote sensing is currently a unique tool able to retrieve geophysical information from the Earth and the surface of other planets at local and global scales, through radiance measurements in different ranges of the electromagnetic spectrum. In the specific case of the sensors orbiting the Earth that measure the surface radiance in the thermal infrared (TIR) region (8–14 µm), the main objective is to obtain the land surface temperature (LST), which is a key variable controlling numerous physical, chemical, and biological land processes, and which is useful in several disciplines like agrometeorology [ Sánchez et al , ], climatology [ Qin et al , ], and hydrology [ Niclòs et al , ]. To determine accurate LSTs, measurements must be corrected for atmospheric and emissivity effects, and thus, the atmosphere must be well characterized, and accurate land surface emissivity (LSE) values must be used.…”

Section: Introductionmentioning

confidence: 99%

Validation and comparison of two models based on the Mie theory to predict 8–14 µm emissivity spectra of mineral surfaces

et al. 2016

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Remote sensing is a powerful tool for studying the planetary regolith surfaces through emission in the thermal infrared region (TIR, 8–14 µm). Theoretical emissivity models are needed to interpret the measured data and eventually to get surface characteristics (such as the refraction index) through model inversion. A new era of orbiting satellites carrying Hyperspectral TIR sensors is coming, and the necessity of understanding the thermal emission of Earth and other planet surfaces at all wavelengths of the electromagnetic spectrum is of prime interest. In this paper we review most of the existing analytical models for predicting the emissivity spectra of minerals for different viewing angles, which are based on the Mie theory, and validated and compared two of them: the Hapke model with two compactness correction methods not tested yet and a model based on the δ‐Eddington approximation, which has not been validated for mineral surfaces. The validation was performed using measurements over two samples rich in quartz and gypsum, respectively. The Hapke model showed the best results when compared with the Warren‐Wiscombe‐Dozier (WWD) model with respect to measured data, showing a RMSE of ±0.04 in emissivity for particle diameter size of a quartz sample greater than 75 µm. This model also showed improvements with regard to results of past published works, after applying to Mie solutions the compactness correction proposed for the WWD model. These results were confirmed for a gypsum sample, a mineral different to the widely used quartz. Finally, the results showed the deficiencies of both models in simulating mineral emissivity around 8.7 µm, probably due to the underestimation of multiple scattering for large values of the imaginary part of the refractive index.

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SMOS Level-2 Soil Moisture Product Evaluation in Rain-Fed Croplands of the Pampean Region of Argentina

Cited by 15 publications

References 25 publications

Downscaling SMAP and SMOS soil moisture retrievals over the Goulburn River Catchment, Australia

Downscaling SMAP and SMOS soil moisture retrievals over the Goulburn River Catchment, Australia

A method for soil moisture probes calibration and validation of satellite estimates

Validation and comparison of two models based on the Mie theory to predict 8–14 µm emissivity spectra of mineral surfaces

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