Mapping Double and Single Crop Paddy Rice With Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam

Lasko, Kristofer; Vadrevu, Krishna Prasad; Tran, Vinh T.; Justice, Christopher O.

doi:10.1109/jstars.2017.2784784

Cited by 120 publications

(65 citation statements)

References 72 publications

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“…Regarding rice phenology, the low values of backscattering of σ • VV and σ • VH were found in the early stages such as sowing days and soil preparation, and the fallow periods in semi-dike area. it was also agreed that during sowing dates there was low backscattering [41,42,48,95]. Moreover, Oyoshi et al [92] noticed that there was low backscattering during the sowing stage and high backscattering during the maturity stage based on the unique phenological changes of rice crop using SAR data.…”

Section: Discussionmentioning

confidence: 82%

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: Discussionmentioning

confidence: 82%

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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“…Analyzing the results of the confusion matrix, the total accuracy of our classification was 84% and the Kappa coefficient was 0.77. Previous research has shown that many more classification methods could improve the accuracy of crop mapping [38,39]. However, the method we chose in this case is relatively simple and sufficiently accurate.…”

Section: Mapping the Sentinel-1-derived Winter Wheat Planting Areamentioning

confidence: 99%

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Song

Wang

2019

Remote Sensing

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Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.

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“…However, obtaining suitable optical satellite imagery is difficult because of the weather effects in cloudy areas [16]. To overcome the limitations of optical imagery, some studies used synthetic aperture radar (SAR) imagery to map the spatial distribution [17,18], crop height [19,20], and yield estimation of paddy rice [21][22][23]. Similar to the studies using optical imagery only, the integration of optical and SAR imagery for yield estimation also involved regression models [15,24] and artificial neural networks [25].…”

Section: Introductionmentioning

confidence: 99%

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Shiu

Chuang

2019

Remote Sensing

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Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models.

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Mapping Double and Single Crop Paddy Rice With Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam

Cited by 120 publications

References 72 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

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

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