Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data

Phan, Hoa; Toan, Thuy Le; Bouvet, Alexandre; Nguyen, Lam Duc; Duy, Tien Pham; Zribi, Mehrez

doi:10.3390/s18010316

Cited by 60 publications

(41 citation statements)

References 37 publications

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“…Moreover, we can see the maximal values of σ • VV and σ • VH appearing at the maturity stage after the maximum of greenness index are consistent with the study of He et al [90]. Previous studies showed that σ • VH correlated with the rice development stage [5,41,45]. Thus, we use local maxima of σ • VH to consider the rice cropping intensity and cropping season.…”

Section: Data Analysis and Accuracy Assessmentsupporting

confidence: 89%

“…The σ • VV and σ • VH polarization can detect variation in scattering behaviors of targets such as volume scattering, and double-bounce scattering in the agricultural area. Previous studies noted that the σ • VV polarization is more sensitive to water bodies while the σ • VH polarization is able to delineate various rice growth stages [5,41,45]. The observed σ • VV is generally higher than σ • VH polarization because of the higher double-bounce.…”

Section: Data Analysis and Accuracy Assessmentmentioning

confidence: 85%

“…Interestingly, Arai et al [56] predicted methane emissions based on remote sensing data and found that PALSAR-2 could distinguish inundated-rice field and noninundated rice field in Vietnam. It should be pointed out, moreover, that Bouvet et al [31], Nguyen et al [41], and Phan et al [45] used SAR sensors from COSMO-SkyMed X-band, Sentinel-1A, and ENVISAT/ASAR satellite images, respectively, for monitoring rice in the VMD. However, those studies were focused on specific seasons; Nguyen et al [41] paid attention to the winter-spring season in 2017 and Phan et al [45] concentrated on summer-autumn in 2018.…”

Section: Introductionmentioning

confidence: 99%

“…It should be pointed out, moreover, that Bouvet et al [31], Nguyen et al [41], and Phan et al [45] used SAR sensors from COSMO-SkyMed X-band, Sentinel-1A, and ENVISAT/ASAR satellite images, respectively, for monitoring rice in the VMD. However, those studies were focused on specific seasons; Nguyen et al [41] paid attention to the winter-spring season in 2017 and Phan et al [45] concentrated on summer-autumn in 2018. Bouvet et al [31] also focused on the main seasons (winter-spring and autumn-winter).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Minh

Avtar

Mohan

et al. 2019

IJGI

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Cropping intensity is one of the most important decisions made independently by farmers in Vietnam. It is a crucial variable of various economic and process-based models. Rice is grown under irrigated triple- and double-rice cropping systems and a rainfed single-rice cropping system in the Vietnamese Mekong Delta (VMD). These rice cropping systems are adopted according to the geographical location and water infrastructure. However, little work has been done to map triple-cropping of rice using Sentinel-1 along with the effects of water infrastructure on the rice cropping intensity decision. This study is focused on monitoring rice cropping patterns in the An Giang province of the VMD from March 2017 to March 2018. The fieldwork was carried out on the dates close to the Sentinel-1A acquisition. The results of dual-polarized (VV and VH) Sentinel-1A data show a strong correlation with the spatial patterns of various rice growth stages and their association with the water infrastructure. The VH backscatter (σ°) is strongly correlated with the three rice growth stages, especially the reproductive stage when the backscatter is less affected by soil moisture and water in the rice fields. In all three cropping patterns, σ°VV and σ°VH show the highest value in the maturity stage, often appearing 10 to 12 days before the harvesting of the rice. A rice cropping pattern map was generated using the Support Vector Machine (SVM) classification of Sentinel-1A data. The overall accuracy of the classification was 80.7% with a 0.78 Kappa coefficient. Therefore, Sentinel-1A can be used to understand rice phenological changes as well as rice cropping systems using radar backscattering.

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Section: Data Analysis and Accuracy Assessmentsupporting

confidence: 89%

Section: Data Analysis and Accuracy Assessmentmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Minh

Avtar

Mohan

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

“…Due to a lack of cloud free Sentinel-2 images, the radar data from Sentinel-1, together with a training sample extracted from field work was not enough descriptive to obtain satisfactory classification results. Other strategy will be explored, such as splitting rice samples into different growing stages, and rice detection methods based on radar backscatter threshold (Bouvet, Le Toan, et Lam-Dao 2009;Phan et al 2018) rather than machine learning algorithms.…”

Section: Resultsmentioning

confidence: 99%

SENTINEL-1&2 FOR NEAR REAL TIME CROPPING PATTERN MONITORING IN DROUGHT PRONE AREAS. APPLICATION TO IRRIGATION WATER NEEDS IN TELANGANA, SOUTH-INDIA

Ferrant¹,

Selles²,

Page³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The inter-annual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated area extents to local water availability. We are developing and testing an automatic methodology for monitoring spatio-temporal variations of irrigated crops in near real time based on Sentinel-1 and -2 data feed over the Telangana State, South India. These freely available radar and optical data are systematically acquired worldwide, over India since 2016, on a weekly basis. Their high spatial resolution (10-20 m) are well adapted to the small size field crops that is common in India. We have focused first on drought prone areas, North of Hyderabad. Crop fraction remains low and varies widely (from 10 to 60%, ISRO-NRSC, Bhuvan). Those upstream areas, mainly irrigated with groundwater, are composed by less than 20% of irrigated areas during the dry season (Rabi, December to March) and up to 60% of the surface is used for crop production during the Kharif (June to November), which includes rainfed cotton and drip irrigated maize crops and inundated rice. A machine learning algorithm, the Random Forest (RF) method, was automatically used over 6 growing seasons ) from the Sentinel-1&2 data stacked for each season, to create crop mapping at 10m resolution over a study area located in the north of Hyderabad (210 by110km). Six seasonal land cover field surveys were used to train and validate the classifier, with a specific effort on rice and maize field sampling. The lowest irrigated area extents were found for driest conditions in Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with the increase of irrigated crops areas during Rabi 2017. For this season, 22% of rice and 9% of irrigated crops were detected after heavy rainfall events in September 2017, which have filled surface water tanks (3.4% of the surface area) and groundwater (Central Groundwater Board observations). From this abundance situation, the surface water detected for each season decreased regularly to less than 0.3% together with the rice and irrigated area extents respectively from 22 to 11% and 10 to 3%, despite a good monsoon 2017. Groundwater level show similar trends, with a drop from 20 meters depth in October 2016 and 2017 to more than 30 m in June 2018 (more recent available data). The deficit of the monsoon 2018 will certainly bring this situation to a hydrological drought at the beginning of 2019, probably similar to the Rabi 2016 situation. The estimated Irrigated Water Demand (IWD) varies from 51 to 310 mm/season, depending on water availability. This methodology shows the potential of automatically monitoring, in near real time, with standard computers, irrigated area extents presenting fast high resolution variability. As it is based on standard global satellite acquisitions, it is foreseen to be used for other regions, for any studies on farmer's adaptation to cli...

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Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0

Siddharth

Saini

Kumar

2021

Unmanned Aerial Vehicles for Internet of Things (IoT)

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Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data

Cited by 60 publications

References 37 publications

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province

SENTINEL-1&2 FOR NEAR REAL TIME CROPPING PATTERN MONITORING IN DROUGHT PRONE AREAS. APPLICATION TO IRRIGATION WATER NEEDS IN TELANGANA, SOUTH-INDIA

Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0

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