Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China

Chen, Jinsong; Lin, Hui; Pei, Zhiyuan

doi:10.1109/lgrs.2007.896996

Cited by 92 publications

(40 citation statements)

References 7 publications

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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%

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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Section: Image Preprocessingmentioning

confidence: 99%

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

et al. 2017

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“…Values of DF ij of less than 0.8 are considered below average, between 0.8 and 1.5 average, and more than 2.0 close to the complete separation of class pairs. 26 In this study, six classes were selected to represent the common land use/cover on the study area from the viewpoint of L-band SAR data. These classes are water, artificial, bareland with peat, bareland without peat, vegetation with peat, and vegetation without peat.…”

Section: Image Classificationmentioning

confidence: 99%

Polarimetric synthetic aperture radar application for tropical peatlands classification: a case study in Siak River Transect, Riau Province, Indonesia

Novresiandi

Nagasawa

2017

J. Appl. Remote Sens

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Abstract. Mapping spatial distributions of tropical peatlands is important for properly estimating carbon emissions and for providing information that aids in the sustainable management of tropical peatlands, particularly in Indonesia. This study evaluated the performance of phased array type L-band synthetic aperture radar (SAR) (PALSAR) dual-polarization and fully polarimetric data for tropical peatlands classification. The study area was in Siak River Transect, Riau Province, Indonesia, a rapidly developing region, where the peatland has been intensively converted mostly into oil palm plantations over the last two decades. Thus, polarimetric features derived after polarimetric decompositions, backscatter coefficients measurements, and the radar vegetation index were evaluated to classify tropical peatlands using the decision tree classifier. Overall, polarimetric features generated by the combination of dual-polarization and fully polarimetric data yielded an overall accuracy (OA) of 69% and a kappa coefficient (K) of 0.57. The integration of an additional feature, "distance to river," to the algorithm increased the OA to 76% and K to 0.66. These results indicated that the methodology in this study might serve as an efficient tool in tropical peatlands classification, especially when involving the use of L-band SAR dual-polarization and fully polarimetric data. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…The performance of the separation between classes i and j is represented by the value DF ij . A higher DF ij means that a feature has better performance separating the associate class pairs [30]. Thus, in this study, features that yielded the highest DF value on each class pair for each polarization channel were analyzed and applied to the DT algorithm.…”

Section: Distance Factor (Df) Extractionmentioning

confidence: 99%

C-Band Dual-Polarization Synthetic Aperture Radar Application for Peat Depth Classification: A Case Study in Siak Regency, Riau Province, Indonesia

Novresiandi¹,

Nagasawa²

2017

PIER M

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Abstract-Knowledge of peat depth distribution is vitally important for accurately estimating carbon stock within tropical peatlands. These estimates aid in understanding the role of tropical peatlands in global environmental change processes. This study evaluates the potential of C-band dual-polarization synthetic aperture radar (SAR) data for peat depth classification on oil palm plantations in Siak Regency, Riau Province, Indonesia. Specifically, features derived after the ground-range radar cross section (sigma-naught or σ 0 ) and slant-range perpendicular radar cross section (gamma-naught or γ 0 ) for both polarization channels of C-band Sentinel-1 data were compared and evaluated on monthly basis, during 2015, for discriminating peat depth classes using the decision tree classifier. Overall, γ 0 features yielded a higher value of distance factors (DF) for peat depth classes, for both polarization channels, than those produced by the σ 0 , indicating a better performance in discriminating peat depth classes. Moreover, the seasonal variation of rainfall intensity was discovered to be influencing feature selection for peat depth classification. Thus, the combination of γ 0 features derived in the much rain months was selected for separating the shallow-and medium-peat classes, whereas those derived in the less rain months was selected for discriminating the deep-and very deep-peat classes. In addition, the developed methodology gave the best accuracy for the very deep-peat class, with 76% and 67.86%, producer's accuracy (PA) and user's accuracy (UA), respectively, followed by the shallow-peat class that yielded a PA of 64% and UA of 80%. Subsequently, the deep-peat class produced a PA of 58% and UA of 59.18%, whereas the medium-peat class yielded the lowest PA and UA, of 54% and 49.09%, respectively. This study showed that the C-band dual-polarization SAR data have potential for classifying peat depth classes, particularly on oil palm plantations, and might serve as an efficient tool in peat depth classification used for sustainable management of tropical peatlands.

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Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China

Cited by 92 publications

References 7 publications

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

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

Polarimetric synthetic aperture radar application for tropical peatlands classification: a case study in Siak River Transect, Riau Province, Indonesia

C-Band Dual-Polarization Synthetic Aperture Radar Application for Peat Depth Classification: A Case Study in Siak Regency, Riau Province, Indonesia

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