Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach

Ambrosone, Mariapaola; Matese, Alessandro; Gennaro, Salvatore Filippo Di; Gioli, Beniamino; Tudoroiu, Marin; Genesio, L.; Miglietta, Franco; Baronti, Silvia; Maienza, Anita; Ungaro, Fabrizio; Toscano, Piero

doi:10.1016/j.jag.2020.102113

Cited by 38 publications

(32 citation statements)

References 53 publications

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“…N =Normal: 40% ≤ 60%) and in which recorded precipitation was around the median. Sentinel-2 enabled a combination of bands (EOS DATA ANALYTICS, 2017) at different resolutions (10 .m - Near-Infrared bands- and 20 m, Red Edge and shortwave infrared bands), which facilitated discrimination of the different levels of surface soil moisture (near-infrared -12-; Red Edge 8 -8A- and Red -4-) applied to agriculture [26] , [27] , [28] , [29] , [30] , to irrigated fields [31] , [32] , [33] and, more precisely, to pasture [34 , 35] . The scenes (a total of four) were mosaicked and contrasted with the use of the nearest neighbour method to obtain a final image in GRID format.…”

Section: Methods Detailsmentioning

confidence: 99%

Classification of Mediterranean hedgerows: A methodological approximation

et al. 2021

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Highlights Hedgerow network mapping in a continental Mediterranean mountain using GIS and Remote Sensing. Identify the different levels of surface soil moisture provided by images from the satellite Sentinel-2. Analyse of the evolution of hedgerow area from 1956 to the present time from aerial pictures and satellite images and their correlation with surface soil moisture and slope. A classification of hedgerow networks in the Mediterranean region (Central System, Spain).

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Section: Methods Detailsmentioning

confidence: 99%

Classification of Mediterranean hedgerows: A methodological approximation

et al. 2021

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“…As a key variable in regulating the Earth water cycle, surface soil moisture (SSM) has a significant role in detecting, monitoring, and predicting drought [146][147][148][149][150][151]. Soil moisture characteristics are directly associated with the weather conditions and geographical characteristics of an area [152,153].…”

Section: Remote Sensing Of Soil Moisture In Droughtmentioning

confidence: 99%

“…The most commonly used model is the OPtical TRApezoid Model (OPTRAM) which has been used for estimating soil moisture requiring only the optical remote sensing data. The estimation of the soil moisture is based on shortwave infrared transformed reflectance (STR)-NDVI trapezoidal space [146,[161][162][163]. Sadeghi et al [161] showed that in comparing estimated SSM from OPTRAM Sentinel-2 and TOTRAM Landsat-8, scientifically acceptable and comparable predictive accuracy was found.…”

Section: Remote Sensing Of Soil Moisture In Droughtmentioning

confidence: 99%

Reviewing the Potential of Sentinel-2 in Assessing the Drought

et al. 2021

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This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

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“…Compared with ground single-point measurement methods, remote sensing technology has been gradually applied due to its wide coverage, strong timeliness, and low costs over the last three decades [9]. As the earliest and most mature technology of earth observation, optical remote sensing has always been playing an important role [10][11][12]. However, the optical features have reduced sensitivity to the water content of the observed target because the SMC retrieval is only based on indirect relationships, and the accuracy is in general low.…”

Section: Of 21mentioning

confidence: 99%

Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

Liu

et al. 2021

Sensors

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As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

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Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach

Cited by 38 publications

References 53 publications

Classification of Mediterranean hedgerows: A methodological approximation

Classification of Mediterranean hedgerows: A methodological approximation

Reviewing the Potential of Sentinel-2 in Assessing the Drought

Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

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