Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

Kornelsen, Kurt C.; Coulibaly, Paulin

doi:10.1016/j.jhydrol.2012.10.044

Cited by 258 publications

(171 citation statements)

References 255 publications

(658 reference statements)

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“…Soil moisture content (SMC) retrieval is attracting widespread interest due to its role in regulating the water cycle and energy balance [1]. This influence makes the SMC a key variable in the fields of climatology, meteorology, hydrology, and agriculture.…”

Section: Introductionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

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This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel-and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R 2 ), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

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Section: Introductionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

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“…Active microwave in the L and C bands have proven to be particularly interesting for the estimation of soil moisture in the first centimeters [53] for bare soils. With a revisit time of one to three days and a ground resolution of 5 × 20 m, Sentinel-1 data would be a good candidate to help a reflectance-based crop coefficient approach in the early part of the season.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

A Life-Size and Near Real-Time Test of Irrigation Scheduling with a Sentinel-2 Like Time Series (SPOT4-Take5) in Morocco

et al. 2014

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This paper describes the setting and results of a real-time experiment of irrigation scheduling by a time series of optical satellite images under real conditions, which was carried out on durum wheat in the Haouz plain (Marrakech, Morocco), during the 2012/13 agricultural season. For the purpose of this experiment, the irrigation of a reference plot was driven by the farmer according to, mainly empirical, irrigation scheduling while test plot irrigations were being managed following the FAO-56 method, driven by remote sensing. Images were issued from the SPOT4 (Take5) data set, which aimed at delivering image time series at a decametric resolution with less than five-day satellite overpass similar to the time OPEN ACCESSRemote Sens. 2014, 6 11183 series ESA Sentinel-2 satellites will produce in the coming years. With a Root Mean Square Error (RMSE) of 0.91mm per day, the comparison between daily actual evapotranspiration measured by eddy-covariance and the simulated one is satisfactory, but even better at a five-day integration (0.59mm per day). Finally, despite a chaotic beginning of the experiment-the experimental plot had not been irrigated to get rid of a slaking crust, which prevented good emergence-our plot caught up and yielded almost the same grain crop with 14% less irrigation water. This experiment opens up interesting opportunities for operational scheduling of flooding irrigation sectors that dominate in the semi-arid Mediterranean area.

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“…Consequently, this translates to habitation of the wetlands with increased influx occurring in the dry season when there is low water availability [1]. Soil moisture content has been reported as being an important variable with respect to influencing climate change dynamics and other hydrological applications [2] [3]. While soil moisture can be observed at single point using gravimetric or Time Domain Reflectometry (TDR) probing, it is difficult to cover wide areas with this method.…”

Section: Introductionmentioning

confidence: 99%

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Kirimi¹,

Kuria²,

Thonfeld³

et al. 2016

ARS

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Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

Cited by 258 publications

References 255 publications

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

A Life-Size and Near Real-Time Test of Irrigation Scheduling with a Sentinel-2 Like Time Series (SPOT4-Take5) in Morocco

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

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