Application of multitemporal ERS-2 synthetic aperture radar in delineating rice cropping systems in the Mekong River Delta, Vietnam

Liew, Soo Chin; Kam, S. P.; Tuong, T. P.; Chen, Ping; Minh, Võ Quang; Lim, H. S.

doi:10.1109/36.718845

Cited by 76 publications

(13 citation statements)

References 3 publications

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“…HH polarisation was chosen because it responds more to the vertical structure of rice plants [20,22]. The double bounce effect between the horizontal flooded field surface and the generally vertical rice plants is more prominent in HH and gives higher backscatter than the VV polarisation [33]. A large incidence angle (45 • ) was used because it reduces wind effects on water during land preparation prior to transplanting, results in a larger dynamic range of radar backscatter and produces higher spatial resolution [34].…”

Section: Terrasar-x Scansar: Multitemporal Image Acquisition and Procmentioning

confidence: 99%

Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

et al. 2019

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The objective of this study is to provide complete information on the dynamic relationship between X-band (3.11 cm) backscattering intensity (σ • ) and rice crop's leaf area index (LAI) at all growth phases. Though the relationship between X-band σ • and LAI has been previously explored, details on the relationship at the reproductive phase remain unstudied. LAI at the reproductive phase is important particularly at the heading stage where LAI reaches its maximum as it is closely related to grain yield, and at flowering stage where the total leaf area affects the amount of photosynthates. Therefore, this study examined the relationship of increasing LAI (vegetative to reproductive phase) and decreasing LAI (ripening phase) with TerraSAR-X (TSX) ScanSAR (3.11 cm) σ • at HH polarisation and 45 • incidence angle. The results showed a statistically significant (R 2 = 0.51, p value < 0.001) non-linear relationship of LAI with σ • at the vegetative to reproductive phase while no significant linear relationship was found at the ripening phase. This study completes the response curve of X-band σ • to LAI by filling in the information on the reproductive phase which more accurately characterises the dynamic relationship between the rice crop's LAI and X-band's σ • . This contributes to improved knowledge on the use of X-band data for estimating LAI for the whole crop cycle which is essential for the modelling of crop growth and estimation of yield.

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Section: Terrasar-x Scansar: Multitemporal Image Acquisition and Procmentioning

confidence: 99%

Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

et al. 2019

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“…In mapping rice paddies, many remote sensing studies were carried out before 2000, in the 1980s and 1990s, using optical images [9][10][11], microwave images [12][13][14], and a combination of these two data [15]. An essential approach to rice plantation detection came with phenological behavior analysis, which requires a dense time series to distinguish it from other crops or between different rice-growing systems.…”

Section: Introductionmentioning

confidence: 99%

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

et al. 2020

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The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.

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“…However, the σ • coefficient has been used to understand different characteristics of the rice paddy and plant, including the contribution of leaf area index, and plant growth stage and type [21]. It has also been successfully used to understand the extent of the triple rice crop [22][23][24]. These studies illustrate the σ • coefficient's usefulness in vegetative changes, rather than the moisture index creation.…”

Section: Change Detection Approachmentioning

confidence: 99%

Identifying Alternative Wetting and Drying (AWD) Adoption in the Vietnamese Mekong River Delta: A Change Detection Approach

Lovell

2019

IJGI

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Alternative wetting and drying (AWD) is an increasingly popular water-saving practice in rice production in the Vietnamese Mekong River Delta, especially considering the impact of projected climate change and reduced water availability. Unfortunately, it is very difficult to determine adoption without deploying thousands of costly household surveys. This research used European Space Agency Sentinel-1a and 1b radar data, combined with in-situ moisture readings, to determine AWD adoption through change detection of a time series wetness index (WI). By using a beta coefficient of the radar data, the WI avoided the pitfalls of cloud cover, surface roughness, and vegetative interference that arise from the sigma coefficient data. The analysis illustrated an AWD adoption likelihood scale across the delta and it showed potential for the use of remotely sensed data to detect adoption. Trends across the Vietnamese delta showed higher adoption rates inland, with lower adoption of AWD in the coastal provinces. These results were supported by a simultaneous effort to collect household level adoption data as part of the same project. However, correlation between the WI values and in situ soil moisture meter readings were most accurate in alluvial soils, illustrating a particularly strong relationship between soil type and WI model robustness. The research suggests that future change detection efforts should focus on retrieving a multi-season dataset and employing a power density analysis on the time series data to fully understand the periodicity of dry down patterns.

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Application of multitemporal ERS-2 synthetic aperture radar in delineating rice cropping systems in the Mekong River Delta, Vietnam

Cited by 76 publications

References 3 publications

Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

Identifying Alternative Wetting and Drying (AWD) Adoption in the Vietnamese Mekong River Delta: A Change Detection Approach

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