Rice Growth Monitoring by Means of X-Band Co-polar SAR: Feature Clustering and BBCH Scale

Yüzügüllü, Onur; Erten, Esra; Hajnsek, Irena

doi:10.1109/lgrs.2015.2388953

Cited by 55 publications

(27 citation statements)

References 13 publications

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“…This criterion is considered in the analyses of the ground measurements and radar images. Rice phenology can be divided into three main phases: vegetative, reproductive, and maturity, according to the widely-used Biologische Bundesanstalt, Bundessortenamt und CHemische scale (a German scale used to identify the phenological development stages of cereals, BBCH) [13,21,38]. The principal phases and corresponding numerical ranges are shown in Table 1.…”

Section: Rice Phenologymentioning

confidence: 99%

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Yong

et al. 2018

Remote Sensing

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Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively.

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Section: Rice Phenologymentioning

confidence: 99%

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Yong

et al. 2018

Remote Sensing

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“…Recent studies have investigated the physical interaction between the radar signal and rice paddy setting [25,28,29,[37][38][39][40][41][42][43][44]. Before the planting of the rice seedling into fields, they are covered by water and the rice plant grows beneath a flooded surface.…”

Section: Ground Phase Estimation On Rice Paddymentioning

confidence: 99%

Vegetation Height Estimate in Rice Fields Using Single Polarization TanDEM-X Science Phase Data

2018

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This study presents the retrieval of rice paddy height using single polarization (single-pol) interferometric SAR (InSAR) data by means of model-based height inversion without an external DEM. A total of eight TanDEM-X (TDX) scenes were used and the TDX images were; acquired using a large cross-track baseline configuration during the TDX Science Phase (June–August 2015). A single-pol inversion approach for a flooded rice field is proposed and evaluated over the Buan test site in South Korea. A novel approach is adopted for the estimation of the ground (i.e., water level) interferometric phase within a flooded rice paddy from TDX data acquired during a period of early rice growth. It is consequently possible to apply the model-based inversion algorithm to the single-pol InSAR data. Rice height maps during the rice growth cycle presented and validated by field measurements. The results demonstrated the high performance of the inversion with a correlation coefficient of 0.78 and an RMSE of 0.10 m. The proposed methodology will be useful to monitor rice plants and to predict a gross rice yield, along with dual and fully polarimetric interferometric SAR data.

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“…Thus, to provide this requirement for the BBCH value assignment, the PolSAR data are clustered to obtain the smallest group with 50% of the samples using the well-known K-means algorithm in the space of statistically independent PolSAR parameters (i.e., σ o HH , σ o VV and ρ). Details about the clustering methodology are given in [40].…”

Section: Feature Clustering For Bbch Assignmentmentioning

confidence: 99%

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

et al. 2017

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Abstract:Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather conditions for detecting temporal phenological changes. This study focuses on a novel approach for growth stage determination of rice fields from SAR data using a parameter space search algorithm. The method employs an inversion scheme for a morphology-based electromagnetic backscattering model. Since such a morphology-based model is complicated and computationally expensive, a surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation. The approach is designed to provide estimates of crop morphology and corresponding growth stage from a continuous growth scale. The accuracy of the proposed method is tested with ground measurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor during a full growth cycle of rice crops. The analysis shows good agreement for both datasets. The results of the proposed method emphasize the effectiveness of X-band PolSAR data for morphology-based growth stage determination of rice crops.

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Rice Growth Monitoring by Means of X-Band Co-polar SAR: Feature Clustering and BBCH Scale

Cited by 55 publications

References 13 publications

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Vegetation Height Estimate in Rice Fields Using Single Polarization TanDEM-X Science Phase Data

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

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