Mapping rice-planted areas using time-series synthetic aperture radar data for the Asia-RiCE activity

Oyoshi, Kei; Tomiyama, Nobuhiro; Okumura, Toshio; Sobue, Shinichi; Sato, Jun

doi:10.1007/s10333-015-0515-x

Cited by 38 publications

(17 citation statements)

References 11 publications

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“…The minimum average of VH polarizations was −22 dB in triple-and double-rice crops, and was −18 dB for the single-rice crop. These values were consistent with previous studies [41,92]. Oyoshi et al [92] also found −20.5 dB (±3 dB) of minima VH polarizations.…”

Section: Data Analysis and Accuracy Assessmentsupporting

confidence: 93%

“…These values were consistent with previous studies [41,92]. Oyoshi et al [92] also found −20.5 dB (±3 dB) of minima VH polarizations. The mean of VH backscatter thresholds varied from −13 to −15 dB in the triple-rice crop, from −13 to −14 dB in the double-rice crop, and −15 dB for the single-rice crop.…”

Section: Data Analysis and Accuracy Assessmentsupporting

confidence: 93%

“…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. Thus, this feature of SAR data can be utilized for discriminating between rice crop and other objects.…”

Section: Discussionmentioning

confidence: 80%

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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: 93%

Section: Data Analysis and Accuracy Assessmentsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 80%

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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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“…The advantages of SAR are that it can operate during the day or night, and in rainy or cloudy conditions. However, SAR data require complex processing, by often specialised software such as the INAHOR 44 and thus were not a focus of this study. Data provided by satellites at a high resolution, such as SPOT or RapidEye, are better for small-scale estimates of crop yield because many tiles are needed to cover a large area, which also is costly.…”

Section: Model Testingmentioning

confidence: 99%

Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

Mashaba¹,

Chirima²,

Botai³

et al. 2017

S. Afr. J. Sci.

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Consumption of wheat is widespread and increasing in South Africa. However, global wheat production is projected to decline. Wheat yield forecasting is therefore crucial for ensuring food security for the country. The objective of this study was to investigate whether the anthesis wheat growth stage is suitable for forecasting dryland wheat yields in the Central Free State region using satellite imagery and linear predictive modelling. A period of 10 years of Normalized Difference Vegetation Index data smoothed with a Savitzky–Golay filter and 10 years of wheat yield data were used for model calibration. Diagnostic plots and statistical procedures were used for model validation and assessment of model adequacy. The period 30 days before harvest during the anthesis stage was established to be the best period during which to use the linear regression model. The calibrated model had a coefficient of determination of 0.73, a p-value of 0.00161 and a root mean squared error of 0.41 tons/ha. Residual plots confirmed that a linear model had a good fit for the data. The quantile-quantile plot provided evidence that the residuals were normally distributed, which means that assumptions of linear regression were fulfilled and the model can be used as a forecasting tool. Model validation showed high levels of accuracy. The evidence indicates that use of Moderate Resolution Imaging Spectroradiometer data during the anthesis growth stage is a reliable, cost-effective and potentially time-saving alternative to ground-based surveys when forecasting dryland wheat yields in the Central Free State.

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“…As such, the exploitation of Synthetic Aperture Radar (SAR) has drawn considerable attention for rice monitoring in the monsoon season, given the ability of microwaves to acquire data regardless of cloud cover and the sensitivity of SAR signal to dielectric and geometric properties of targets (Le Toan et al, 1997;Inoue et al, 2002;Chakraborty et al, 2005;Wang et al, 2009;Kuenzer and Knauer, 2013). Recognizing the role of SAR systems for operational monitoring of rice, international initiatives have been launched including the Asian Rice Crop Estimation and Monitoring (Asia-RiCE) under the Group on Earth Observations Global Agriculture Monitoring (GEOGLAM) framework (Nelson et al, 2014;Oyoshi et al, 2016). Recent studies are also turning their attention towards developing processing chains in cloud-based platforms to evaluate the potential and transferability of operational crop characterization at regional scales with the availability of operational SAR systems (Mandal et al, 2018;Singha et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data

Mandal

Kumar

Ratha

et al. 2020

Remote Sensing of Environment

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Rice growth monitoring using Synthetic Aperture Radar (SAR) is recognized as a promising approach for tracking the development of this important crop. Accurate spatio-temporal information of rice inventories is required for water resource management, production risk occurrence, and yield forecasting. This research investigates the potential of the proposed Generalized volume scattering model based Radar Vegetation Index (GRVI) for monitoring rice growth at different phenological stages. The GRVI is derived using the concept of a geodesic distance (GD) between Kennaugh matrices projected on a unit sphere. We utilized this concept of GD to quantify a similarity measure between the observed Kennaugh matrix (representation of observed Polari

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Mapping rice-planted areas using time-series synthetic aperture radar data for the Asia-RiCE activity

Cited by 38 publications

References 11 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

Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

Assessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data

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