Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Clauss, Kersten; Ottinger, Marco; Kuenzer, Claudia

doi:10.1080/01431161.2017.1404162

Cited by 82 publications

(73 citation statements)

References 62 publications

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“…The Sentinel-1 A and Sentinel-1B satellites are carrying C-band SAR sensors providing imagery in single (VV or HH) and dual polarisations (VV + VH or HH + HV) with high temporal resolutions (12 days to 6 days in regions towards the poles) and with a long-term monitoring possibility (European Space Agency (ESA, 2013). To our knowledge, only a few studies have used Sentinel-1 time-series data for rice crop mapping (Chen et al, 2016;Nguyen et al, 2016;Mansaray et al, 2017;Nguyen and Wagner, 2017;Torbick et al, 2017;Clauss et al, 2018) and none of them has applied Sentinel-1 for the detection of rice crop establishment methods. Given that most rice crop mapping algorithms depend on the detection of a strong water signal at the start of the growing season, which is typical of TP rice but not of DS rice, a more comprehensive understanding of the observable differences due to TP and DS would also improve the capability of rice mapping systems.…”

Section: Introductionmentioning

confidence: 99%

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Fikriyah

Darvishzadeh

Khan

et al. 2019

International Journal of Applied Earth Observation and Geoinfor

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A B S T R A C TImproved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates with the reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate the possibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season. different management approaches to create favourable rice-growing https://doi.

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Section: Introductionmentioning

confidence: 99%

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Fikriyah

Darvishzadeh

Khan

et al. 2019

International Journal of Applied Earth Observation and Geoinfor

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“…They report in their study that rice and other crops are often misclassified when using only the spectral information of optical images. When the spectral features were only considered, rice plots were misclassified as Several studies have already used traditional machine learning algorithms for paddy rice mapping such as RF, Support Vector Machine (SVM), and decision tree [24,[30][31][32][33][34]. Recently, Zhang et al [31] used the Land Surface Temperature and phenological parameters derived from the NDVI time series of Landsat 8 images (fused with MODIS NDVI to overcome the cloud limitation) with a convolutional neural network approach to map rice areas in the Dongting Lake Area of China.…”

Section: Discussionmentioning

confidence: 99%

“…The highest overall accuracy (93.3%) was obtained when using the VV/VH ratio. Clauss et al [34] also mapped rice areas over six different study sites using S1 temporal series by applying the decision tree. They chose the VH polarization due to the high dynamic range of backscatter over rice areas.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.

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“…Most recently, due to freely available ESA Sentinel-1 C-band SAR data, many research efforts concentrate on various crop classification with advanced methods using Sentinel-1 time series [21,23,[26][27][28][29]. Likewise, we use SAR data and review several classification approaches in more detail.…”

Section: Sar Data In Crop Classificationmentioning

confidence: 99%

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

2019

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Methods based on Sentinel-1 data were developed to monitor crops and fields to facilitate the distribution of subsidies. The objectives were to (1) develop a methodology to predict individual crop species or or management regimes; (2) investigate the earliest time point in the growing season when the species predictions are satisfactory; and (3) to present a method to assess the uncertainty of the predictions at an individual field level. Seventeen Sentinel-1 synthetic aperture radar (SAR) scenes (VV and VH polarizations) acquired in interferometric wide swath mode from 14 May through to 30 August 2017 in the same geometry, and selected based on the weather conditions, were used in the study. The improved k nearest neighbour estimation, ik-NN, with a genetic algorithm feature optimization was tailored for classification with optional Sentinel-1 data sets, species groupings, and thresholds for the minimum parcel area. The number of species groups varied from 7 to as large as 41. Multinomial logistic regression was tested as an optional method. The Overall Accuracies (OA) varied depending on the number of species included in the classification, and whether all or not field parcels were included. OA with nine species groups was 72% when all parcels were included, 81% when the parcels area threshold (for incorporating parcels into classification) was 0.5 ha, and around 90% when the threshold was 4 ha. The OA gradually increased when adding extra Sentinel-1 scenes up until the early August, and the initial scenes were acquired in early June or mid-May. After that, only minor improvements in the crop recognition accuracy were noted. The ik-NN method gave greater overall accuracies than the logistic regression analysis with all data combinations tested. The width of the 95% confidence intervals with ik-NN for the estimate of the probability of the species with the largest probability on an individual parcel varied depending on the species, the area threshold of the parcel and the number of the Sentinel-1 scenes used. The results ranged between 0.06-0.08 units (6-8% points) for the most common species when the Sentinel-1 scenes were between 1 June and 12 August. The results were well-received by the authorities and encourage further research to continue the study towards an operational method in which the space-borne SAR data are a part of the information chain.

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Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Cited by 82 publications

References 62 publications

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

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

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

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