Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

Bazzi, Hassan; Baghdadi, Nicolas; Hajj, Mohammad El; Zribi, Mehrez; Minh, Dinh Ho Tong; Ndikumana, Emile; Courault, Dominique; Belhouchette, Hatem

doi:10.3390/rs11070887

Cited by 123 publications

(83 citation statements)

References 34 publications

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“…The precision, recall, and F-score were also calculated for each class (irrigated/non-irrigated). In fact, several studies report the use of these indices to assess the accuracy of the classification [20,31,37,61]. While OA can be easily interpreted as the percentage of correctly classified plots to the total number of plots, Kappa and F-score can be used to assess the statistical differences between classifications.…”

Section: Accuracy Assesmentmentioning

confidence: 99%

“…Apart from optical data, recent studies have started exploiting the potential of SAR (synthetic aperture radar) data to map different agricultural irrigated areas [20][21][22][23][24]. The assumption established for using SAR data relies on the fact that radar remote sensing is sensitive to the water content of soil due to the increase in the dielectric constant with the increase of the soil water content.…”

Section: Introductionmentioning

confidence: 99%

“…The S1 satellites (S1A and S1B) provide an exceptional combination of high spatial and high temporal resolutions for dual polarization SAR data (six days of temporal resolution and a 10 m × 10 m pixel spacing) available in free open access mode. S1 time series has been newly exploited by several studies for land cover classification [20,[31][32][33]. High quality classification mapping has been produced by applying either classical machine learning, such as random forest and the support vector machine, or complex deep learning methods on S1 time series.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

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Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

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Section: Accuracy Assesmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

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“…This first continental-scale, multi-decadal assessment of annual mangrove distribution showed that mangrove forest extent in Australia peaked in 2010 and has since declined by about 240 km 2 [139]. Several recent studies have used Sentinel-1 SAR time series data to map the distribution of rice paddies and aquaculture ponds in river deltas, taking advantage of the unique seasonal inundation dynamics and microwave backscattering characteristics of these features [171,172,[188][189][190]. Advances in SAR-based mapping of aquaculture ponds are already covered in the previous chapter, and therefore, are not repeated here.…”

Section: Earth Observation Potential For Land Cover and Land Use Infomentioning

confidence: 99%

Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions—A Review

Kuenzer

Heimhuber

Huth³

et al. 2019

Remote Sensing

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River deltas and estuaries belong to the most significant coastal landforms on our planet and are usually very densely populated. Nearly 600 million people live in river deltas, benefiting from the large variety of locational advantages and rich resources. Deltas are highly dynamic and vulnerable environments that are exposed to a wide range of natural and manmade threats. Sustainable management of river deltas therefore requires a holistic assessment of historic and recent ongoing changes and the dynamics in settlement sprawl, land cover and land use change, ecosystem development, as well as river and coastline geomorphology, all of which is difficult to achieve solely with traditional land-based surveying techniques. This review paper presents the potential of Earth Observation for analyses and quantification of land surface dynamics in the large river deltas globally, emphasizing the different geo-information products that can be derived from medium resolution, high resolution and highest resolution optical, multispectral, thermal and SAR data. Over 200 journal papers on remote sensing related studies for large river deltas and estuaries have been analyzed and categorized into thematic fields such as river course morphology, coastline changes, erosion and accretion processes, flood and inundation dynamics, regional land cover and land use dynamics, as well as the monitoring of compliance with respect to anthropogenic activity such as industry expansion-related habitat destruction. Additionally, our own exemplary analyses are interwoven into the review to visualize related delta work.

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“…In recent years, researchers have highly utilised SAR images that have a great advantage in the phenological monitoring of crops [7,8]. High resolution Sentinel-1 SAR images have been recently used in mapping and monitoring the rice fields [9,10]. However, the preprocessing of Sentinel-1 SAR (time series analysis) for monitoring crops can be a time consuming task, where a cloud platform could ease the process.…”

Section: Introductionmentioning

confidence: 99%

Rice Monitoring Using Sentinel-1 Data in the Google Earth Engine Platform

Dineshkumar

Kumar

Nitheshnirmal

2019

The 2nd International Electronic Conference on Geosciences

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Rice is the most essential and nutritional staple food crop worldwide. There is a need for accurate and timely rice mapping and monitoring, which is a pre-requisite for crop management and food security. Recent studies have utilized Sentinel-1 data for mapping and monitoring ricegrowing areas. The present study was carried out in the Google Earth Engine (GEE), where the Sentinel-1data were used for monitoring the rice-growing area over Kulithalai taluk of Karur district, located along the Cauvery delta region. Normally, the production of rice in the study area starts in the late Samba Season where the long duration variety Cr1009 (130 days) is extensively grown. The results exhibit low backscattering values during the transplanting stage of VV and VH polarization (−15.19 db and −24 db), whereas maximum backscattering is experienced at the peak vegetation stage of VV and VH polarization (−7.42 and −16.9 db) and there is a decrease in the backscattering values after attaining the maturity stage. Amongst VH and VV polarization, VH polarization provides a consistently increasing trend in backscatter coefficients from the panicle initiation phase to the early milking phase, after which the crop attains its maturity phase, whereas in VV polarization, an early peak of backscatter coefficients is seen much earlier during the flowering phase itself. Thus, in this study, VV polarization gives better interpretation than VH polarization in the selected rice crop fields. The obtained results were cross-validated by collecting the ground truth values during the satellite data acquisition time, throughout the crop growing period from the selected rice fields.

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Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

Cited by 123 publications

References 34 publications

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions—A Review

Rice Monitoring Using Sentinel-1 Data in the Google Earth Engine Platform

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