Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation

Paloscia, S.; Pettinato, S.; Santi, Emanuele; Notarnicola, Claudia; Pasolli, Luca; Reppucci, A.

doi:10.1016/j.rse.2013.02.027

Cited by 355 publications

(238 citation statements)

References 40 publications

Supporting

Mentioning

228

Contrasting

Unclassified

Order By: Relevance

“…Also, the SVR model is easy to handle and quick to train, and shows a good compromise between processing time and inversion accuracy. Thus, it is suitable for timeliness requirements [22,51]. Moreover, the SVR model is suitable for small training samples.…”

Section: Empirical Model Experimentsmentioning

confidence: 99%

The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

et al. 2015

View full text Add to dashboard Cite

Abstract:The potential use of TerraSAR-X and Radarsat-2 data for soil moisture retrieval over bare agricultural areas was investigated using both empirical and semi-empirical approaches. For the empirical approach, the Support Vector Regression (SVR) model was used with two cases: (1) using only one C-band or X-band image; and (2) using a pair of C-band and X-band images jointly. For the semi-empirical approach, the modified Dubois model based on C-band and X-band SAR data was developed to estimate soil moisture content. The experiments were implemented over two bare agricultural areas, and in-situ measurements were carried out to assess the methods. The results showed that the TerraSAR-X and Radarsat-2 are suitable remote sensing tools for the estimation of surface soil moisture, with an accuracy of about 3 vol % (root mean square error, RMSE) over bare agricultural areas. Compared with the results obtained by Radarsat-2 data, TerraSAR-X data gives a slight improvement in estimating soil moisture. The accuracy of the soil moisture estimation was improved further when the two bands SAR data were used (RMSE of about 2.2 vol %) instead of only one. Moreover, the modified Dubois model showed comparable accuracy to the empirical model independent of the surface roughness.

show abstract

Section: Empirical Model Experimentsmentioning

confidence: 99%

The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In particular, the research on microwave remote sensing focused on soil moisture retrieval, forest and crop biomass estimation, and ice and snow pack parameter investigation [e.g. Kasischke et al, 2009;Balenzano et al, 2011;Paloscia et al, 2013;Pettinato et al, 2013a]. Soil moisture and its temporal and spatial variations are influential parameters in both climatic and hydrologic models.…”

Section: Introductionmentioning

confidence: 99%

HydroCosmo: The Monitoring of Hydrological Parameters on Agricultural Areas by using Cosmo-SkyMed Images

Fontanelli¹,

Paloscia²,

Pampaloni³

et al. 2013

European Journal of Remote Sensing

View full text Add to dashboard Cite

In this paper, the results of an experiment carried out for exploiting the capabilities of X-band Cosmo-SkyMed data in the monitoring of soil and vegetation characteristics are summarized. SAR data have been collected in two agricultural areas in Italy and compared with ground truth measurements of soil and vegetation parameters. A rather good sensitivity to vegetation features, biomass and the moisture of bare or slightly vegetated soils has been confirmed. On bare surfaces the effect of surface roughness was significant, as expected, due to the high observation frequency. Model simulations have also been performed for better explaining the backscattering response to soil moisture at this frequency. The different backscattering response according to vegetation types, which has already been observed at C-band, has been confirmed at X-band, too. The absorption due to thin vertical stems was observed on wheat crops, whereas sunflower showed a prevalent scattering behavior due to the large circular leaves.

show abstract

“…en, the applicability of Alpha model was further assessed in combination 6 Advances in Meteorology with time series SAR data and field measurements. e multitemporal Radarsat-2 data and measured soil moisture of the same sampling sites were employed for the theoretical analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Synthetic aperture radar (SAR) is one of the most promising techniques for measuring surface soil moisture at moderate-to-high spatial resolution required by hydrological, meteorological, ecological, and agricultural applications [1][2][3]. However, accurate soil moisture retrieval from SAR data is still a challenging task due to the fact that the radar backscatter is influenced by multiple parameters such as soil dielectric constant (related to soil moisture), surface roughness, and vegetation conditions [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Zhang

Tang

Gao

et al. 2018

Advances in Meteorology

View full text Add to dashboard Cite

e objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. e Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. e solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. is research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. e disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. e optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. e agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm 3 /cm 3 for both methods. e optimized Alpha model shows an obvious improvement for soil moisture retrieval. e results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

show abstract

Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation

Cited by 355 publications

References 40 publications

The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

HydroCosmo: The Monitoring of Hydrological Parameters on Agricultural Areas by using Cosmo-SkyMed Images

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

Contact Info

Product

Resources

About