Evaluation of the potential of Sentinel-1 and Sentinel-1 data for clay content mapping

Soil texture is a key parameter in agricultural processes and an important measure for agricultural prediction, water cycle, filtering of pollutants and carbon storage. Besides, its estimation is essential for agronomists, hydrologists, geologists and environmentalists and for modeling in these application areas. Several studies have been based on understanding and modeling the biological, physical and chemical processes in the soil. Regarding the texture of the soil, few researches propose soil texture spatialization, and are generally based on ground measurements. Among other things, field observations or laboratory analyzes are very expensive and are not very representative. Indeed, the soil texture presents a strong heterogeneity even at the scale of a field. It is then necessary to use precise and spatialized information on soils.These methods are generally based on remote sensing data and particularly optical data to restore soil component. However, these techniques are strongly affected by atmospheric conditions. This constraint is not valid for Radar sensors (Radio Detection And Ranging). Radar data are mainly sensitive to soil moisture and soil roughness, and has also been evaluated for its ability to perform texture measurements.The aim of this study is evaluate the potential of these techniques based on optical and radar data for soil texture estimation. By its composition, its structure, its texture and its porosity, soil moisture is strongly influenced by the soil nature. With the arrival of Sentinel-1 (S-1) and Sentinel-2 (S-2) ESA spatial missions, data are acquired with high spatial and temporal resolution between July and early December 2017, on a semi-arid area in central Tunisia. This study is therefore conducted using S-2 SWIR (Short-Wave Infrared) bands (B11 and B12, most sensitive to clay) and soil moisture products derived from radar data. And algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content.In order to evaluate the approach and determine the adequate data (between optical and radar data) allowing to precisely characterize the clay content, a cross-validation was used. The SWIR bands lead&#160;to less satisfactory outcomes compared to soil moisture. With an overall accuracy of approximately 65%, soil moisture achieved the best performance for estimating soil texture. The results also showed that RF and SVM are robust classifiers for texture estimation despite the small number of training data. However, RF displays greater accuracy and speed of simulation compared to SVM.

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Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2

Remote Sensing ◽

10.3390/rs11131520 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1520 ◽

Cited By ~ 11

Author(s):

Safa Bousbih ◽

Mehrez Zribi ◽

Charlotte Pelletier ◽

Azza Gorrab ◽

Zohra Lili-Chabaane ◽

...

Keyword(s):

Soil Texture ◽

Clay Content ◽

Radar Data ◽

Short Wave ◽

Optical Data ◽

Support Vector ◽

Texture Measurement ◽

Combined Use ◽

Bare Soils ◽

Sentinel 2

This paper discusses the combined use of remotely sensed optical and radar data for the estimation and mapping of soil texture. The study is based on Sentinel-1 (S-1) and Sentinel-2 (S-2) data acquired between July and early December 2017, on a semi-arid area about 3000 km2 in central Tunisia. In addition to satellite acquisitions, texture measurement samples were taken in several agricultural fields, characterized by a large range of clay contents (between 13% and 60%). For the period between July and August, various optical indicators of clay content Short-Wave Infrared (SWIR) bands and soil indices) were tested over bare soils. Satellite moisture products, derived from combined S-1 and S-2 data, were also tested as an indicator of soil texture. Algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content and a three-fold cross-validation is used to evaluate both approaches. The classifications with the best performance are achieved using the soil moisture indicator derived from combined S-1 and S-2 data, with overall accuracy (OA) of 63% and 65% for the SVM and RF classifications, respectively.

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Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

Remote Sensing ◽

10.3390/rs13020243 ◽

2021 ◽

Vol 13 (2) ◽

pp. 243

Author(s):

Amal Chakhar ◽

David Hernández-López ◽

Rocío Ballesteros ◽

Miguel A. Moreno

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Remote Sensing Data ◽

Radar Data ◽

Optical Data ◽

Support Vector ◽

Sensing Data ◽

Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

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

Sensors ◽

10.3390/s21030877 ◽

2021 ◽

Vol 21 (3) ◽

pp. 877

Author(s):

Jian Liu ◽

Youshuan Xu ◽

Henghui Li ◽

Jiao Guo

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Remote Sensing Data ◽

Optimal Combination ◽

Optical Data ◽

Support Vector ◽

Sensing Data

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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A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

Remote Sensing ◽

10.3390/rs12060961 ◽

2020 ◽

Vol 12 (6) ◽

pp. 961 ◽

Cited By ~ 1

Author(s):

Marinalva Dias Soares ◽

Luciano Vieira Dutra ◽

Gilson Alexandre Ostwald Pedro da Costa ◽

Raul Queiroz Feitosa ◽

Rogério Galante Negri ◽

...

Keyword(s):

Remote Sensing Data ◽

Data Classification ◽

Test Site ◽

Radar Data ◽

Input Image ◽

Optical Data ◽

Land Use Classification ◽

Object Based ◽

Sar Imagery ◽

Small Change

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.

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

Remote Sensing ◽

10.3390/rs10081285 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1285 ◽

Cited By ~ 20

Author(s):

Reza Attarzadeh ◽

Jalal Amini ◽

Claudia Notarnicola ◽

Felix Greifeneder

Keyword(s):

Soil Moisture ◽

Optical Data ◽

Estimation Accuracy ◽

Support Vector ◽

Practical Applications ◽

Initial Stage ◽

Object Based ◽

Single Polarization ◽

Selection Of

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 R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

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Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

Water ◽

10.3390/w12030866 ◽

2020 ◽

Vol 12 (3) ◽

pp. 866 ◽

Cited By ~ 5

Author(s):

Myriam Foucras ◽

Mehrez Zribi ◽

Clément Albergel ◽

Nicolas Baghdadi ◽

Jean-Christophe Calvet ◽

...

Keyword(s):

Soil Moisture ◽

Optical Sensors ◽

Surface Soil ◽

Remote Sensing Data ◽

Climatic Conditions ◽

Optical Data ◽

Surface Soil Moisture ◽

Moisture Index ◽

Wide Range ◽

Moderate Resolution Imaging Spectroradiometer

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

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Mapping surface soil moisture over wheat crops in southern Mediterranean regions using the backscattering coefficient and the interferometric coherence derived from Sentinel-1

10.5194/egusphere-egu2020-7408 ◽

2020 ◽

Author(s):

Nadia Ouaadi ◽

Lionel Jarlan ◽

Jamal Ezzahar ◽

Saïd Khabba ◽

Mehrez Zribi ◽

...

Keyword(s):

Soil Moisture ◽

Temporal Resolution ◽

Surface Soil ◽

Cloud Model ◽

Microwave Remote Sensing ◽

Radar Data ◽

Optical Data ◽

Surface Soil Moisture ◽

Backscattering Coefficient ◽

New Approach

High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2&#176; for VV, with R=0.82, RMSE=0.05m3/m3 and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m3/m3 and 0.03 m3/m3, respectively even that the incidence angle is higher (40&#176;). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.

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DEPTH ESTIMATION OF SHALLOW WATER USING MULTISPECTRAL SATELLITE IMAGERY SENTINEL-2A

Jurnal Segara ◽

10.15578/segara.v16i3.8562 ◽

2020 ◽

Vol 16 (3) ◽

Author(s):

Arip Rahman

Keyword(s):

Shallow Water ◽

Test Data ◽

Remote Sensing Data ◽

Depth Estimation ◽

Training Data ◽

Coefficient Of Determination ◽

Support Vector ◽

Data Set ◽

Svm Algorithm ◽

Sentinel 2A

Shallow water bathymetry estimation from remote sensing data has been increasing widespread, as an alternative to traditional bathymetry measurement that has disturbed by technical and logistic problem. Deriving bathymetry data from Sentinel 2A images, at visible wavelength (blue, green and red) 10 meter spatial resolution was carried out around the waters of the Kemujan Island Karimunjawa National Park Central Java. Amount of 1280 points data are used as training data sets and 854 points data as test data set produced from sounding. Dark Object Substraction (DOS) has been to correct atmospherically the Sentinel-2A images. Several algorithm has been applied to derive bathymetry data, including: linear transform, ratio transform and support vector machine (SVM). The highest correlation between depth prediction and observe resulted from SVM algorithm with a coefficient of determination (R2) 0.71 (training data) and 0.56 (test data). The assessment of the accuracy of the three methods using RMSE and MAE values, the SVM algorithm has the smallest value (< 1 m). This indicates that the SVM algorithm has a high accuracy compared to the other two methods. The bathymetry map derived from Sentinel 2A imagery cannot be used as a reference for navigation.

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POSSIBILITIES OF THE JOINT USE OF OPTICAL AND RADAR DATA IN FLOOD SPACE MONITORING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w4-67-2018 ◽

2018 ◽

Vol XLII-3/W4 ◽

pp. 67-73

Author(s):

O. P. Arkhipkin ◽

G. N. Sagatdinova

Keyword(s):

Spatial Resolution ◽

Complex Analysis ◽

Remote Sensing Data ◽

Weather Conditions ◽

High Water ◽

Radar Data ◽

Time Of Day ◽

Optical Data ◽

Additional Information ◽

Good Spatial Resolution

Abstract. The article gives a brief description of the system of space monitoring of high water and floods. Its main tasks are the operational dynamics of snow and ice cover melting and the passage of flood waters. The solution of these tasks is carried out in three levels corresponding to the low, medium and high resolution of remote sensing data. An important role in monitoring is given to radar data. This is due to the features of the radar survey: independence from weather conditions and time of day, regularity, good spatial resolution, the possibility of using polarimetric properties (including phase information). The use of radar data also provides additional information, including the allocation of wet soils, flooded vegetation and infrastructure. The presence of large time periods of repeated survey, interference (cloudiness, haze, noise, etc.), different spatial resolution necessitates a complex analysis of optical and radar data in flood space monitoring. Such analysis makes it possible to better observe the flood dynamics, more precisely identify of flooding zones and determine their structure. Features of radar survey (transparency of dry snow and change of reflected signal during snowmelt) allow using them to determine the beginning of snow melt and determine the degree of water content in it. Optical data are also used to determine the area and structure of the snow cover. Method of detecting the beginning of the snowmelt period consists in the comparison of the current radar image with a base image created as an average image from the winter images with dry snow.

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Discovering Potential Settlement Areas around Archaeological Tells Using the Integration between Historic Topographic Maps, Optical, and Radar Data in the Northern Nile Delta, Egypt

Remote Sensing ◽

10.3390/rs11243039 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3039 ◽

Cited By ~ 2

Author(s):

Abdelaziz Elfadaly ◽

Mohamed A. R. Abouarab ◽

Radwa R. M. El Shabrawy ◽

Wael Mostafa ◽

Penelope Wilson ◽

...

Keyword(s):

Nile Delta ◽

Remote Sensing Data ◽

Radar Data ◽

Primary Objective ◽

Topographic Maps ◽

Optical Data ◽

History Of ◽

The Creation ◽

Settlement Studies ◽

Settlement Areas

The primary objective of this study is to leverage the integration of surface mapping data derived from optical, radar, and historic topographical studies with archaeological sampling to identify ancient settlement areas in the Northern Nile Delta, Egypt. This study employed the following methods: digitization of topographic maps, band indices techniques on optical data, the creation of a 3D model from SRTM data, and Sentinel-1 interferometric wide swath (IW) analysis. This type of study is particularly relevant to the search for evidence of otherwise hidden ancient settlements. Due to its geographical situation and the fertility of the Nile, Egypt witnessed the autochthonous development of predynastic and dynastic civilizations, as well as an extensive history of external influences due to Greek, Roman, Coptic, Islamic, and Colonial-era interventions. Excavation work at Buto (Tell el-Fara’in) in 2017–18, carried out by the Kafrelsheikh University (KFS) in cooperation with the Ministry of Antiquities, demonstrated that remote sensing data offers considerable promise as a tool for developing regional settlement studies and excavation strategies. This study integrates the mission work in Buto with the satellite imagery in and around the area of the excavation. The results of the initial Buto area research serve as a methodological model to expand the study area to the North Delta with the goal of detecting the extent of the ancient kingdoms of Buto and Sakha. The results of this research include the creation of a composite historical database using ancient references and early topographical maps (1722, 1941, 1950, and 1997), Optical Corona (1965), Landsat MSS (Multispectral Scanner System) (1973, 1978, and 1988), TM (Thematic Mapper) (2005) data, and Radar SRTM (2014) and Sentinel1 (2018 and 2019) data. The data in this study have been analyzed using the ArcMap, Envi, and SNAP software. The results from the current investigation highlight the rapid changes in the land use/land cover in the last century in which many ancient sites were lost due to agriculture and urban development. Three potential settlement areas have been identified with the Sentinel1 Radar data, and have been integrated with the early maps. These discoveries will help develop excavation strategies aimed at elucidating the ancient settlement dynamics and history of the region during the next phase of research.

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