Rice Crop Monitoring in South China With RADARSAT-2 Quad-Polarization SAR Data

Wu, Fan; Wang, Chao; Zhang, Hong; Zhang, Bo; Tang, Yixian

doi:10.1109/lgrs.2010.2055830

Cited by 108 publications

(51 citation statements)

References 13 publications

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“…This is not surprising as previous research on crop identification has demonstrated the value of the HV channel for discrimination, especially between grain and broad-leafed crops (Brisco et al 1992). Li et al (2011) found that HH/HV was the best dual-channel combination and Wu et al (2011) found HV to be the best polarization but that the HH/VV ratio was best for discriminating rice from bananas, forest, and river. In general, the HV channel helps discriminate forest and upland field crops from the urban and rice classes due to the greater volume scattering in these targets.…”

Section: Resultsmentioning

confidence: 99%

Compact polarimetry assessment for rice and wetland mapping

Brisco

Tedford

et al. 2012

International Journal of Remote Sensing

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Polarimetric RADARSAT-2 data of rice and wetlands are used to simulate compact polarimetry (CP) mode data from the upcoming RADARSAT Constellation Mission (RCM). The simulated CP data are then used to evaluate the information content for rice and wetland mapping using supervised classification, and the results are compared for linear and circular polarization combinations and polarimetric decompositions from the fully polarimetric data and the simulated CP data. The results are consistent for both rice and wetlands and show that the classification accuracy increases as one goes up the polarization hierarchy. The circular polarizations produced the best classification results for the polarization combinations. This result requires further research to verify. Although the CP data did not perform as well as the fully polarimetric data, the results were better than for dual polarization, and this mode may offer the best option for rice and wetland mapping applications because of swath coverage. Note that both the compact simulations and the fully polarimetric data produced operationally suitable classification accuracies. Additional research is underway to evaluate the monitoring capability of this new CP mode. This article describes the approach used for the analyses and the classification results for both rice and wetlands.

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Section: Resultsmentioning

confidence: 99%

Compact polarimetry assessment for rice and wetland mapping

Brisco

Tedford

et al. 2012

International Journal of Remote Sensing

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“…The images were first calibrated to sigma naught (σ 0 ) backscatter coefficients using Equation (1). The calibrated images were then automatically terrain corrected using range Doppler terrain correction (producing 10 m square pixel resolution images), reprojected to the Universal Transverse Mercator (UTM) zone 51 N, and speckle filtered using a 7 × 7 gamma map filter with 3 looks [44,45]. Using the 9 June image as master, all the Sentinel-1A images were co-registered on-the-fly in SNAP based on binomial interpolation, using 200 ground control points (GCPs).…”

Section: Image Preprocessingmentioning

confidence: 99%

“…This class exhibited the highest temporal backscatter values due primarily to corner reflections from buildings and other vertical objects [45]. As we have placed other low backscatter surfaces such as asphalt pavements into the class "Others" and as the temporal backscatter profile of Built is far removed from that of Water, the MNDWI image was not applied in the mapping of Built.…”

Section: Mapping Algorithmmentioning

confidence: 99%

Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

et al. 2017

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Sentinel-1A and Landsat 8 images have been combined in this study to map rice fields in urban Shanghai, southeast China, during the 2015 growing season. Rice grown in paddies in this area is characterized by wide inter-field variability in addition to being fragmented by other land-uses. Improving rice classification accuracy requires the use of multi-source and multi-temporal high resolution data for operational purposes. In this regard, we first exploited the temporal backscatter of rice fields and background land-cover types at the vertical transmitted and vertical received (VV) and vertical transmitted and horizontal received (VH) polarizations of Sentinel-1A. We observed that the temporal backscatter of rice increased sharply at the early stages of growth, as opposed to the relatively uniform temporal backscatter of the other land-cover classes. However, the increase in rice backscatter is more sustained at the VH polarization, and two-class separability measures further indicated the superiority of VH over VV in discriminating rice fields. We have therefore combined the temporal VH images of Sentinel-1A with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-free Landsat 8 image. The integration of these optical indices with temporal backscatter eliminated all commission errors in the Rice class and increased overall accuracy by 5.3%, demonstrating the complimentary role of optical indices to microwave data in mapping rice fields in subtropical and urban landscapes such as Shanghai.

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“…Studies by Chen et al [48], Bouvet et al [49] and Lam-Dao et al [17] employed Envisat ASAR data to show that the ratio between HH and VV polarization on multi-temporal datasets can be used to classify rice areas with higher accuracy and less temporal coverage compared to the first method. Polarimetric decomposition of fully polarimetric RADARSAT-2 acquisitions showed promising results regarding not only the binary rice/non-rice classification of images but also the detection of rice's growth stages [50][51][52]. Rice classification performance of TerraSAR-X (TSX) images over test sites in Spain and the theoretical models behind multi-polarization and X-band-based rice classification have been extensively described by Lopez-Sanchez et al [53][54][55] as well as for the Mekong Delta [56][57][58].…”

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confidence: 99%

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

et al. 2015

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Abstract:Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double-and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R² of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.

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Rice Crop Monitoring in South China With RADARSAT-2 Quad-Polarization SAR Data

Cited by 108 publications

References 13 publications

Compact polarimetry assessment for rice and wetland mapping

Compact polarimetry assessment for rice and wetland mapping

Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

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